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69 lines
2.0 KiB
Python
69 lines
2.0 KiB
Python
'''Test the ability to add a label to a (potentially trained) parsing model.'''
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from __future__ import unicode_literals
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import pytest
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import numpy.random
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from thinc.neural.optimizers import Adam
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from thinc.neural.ops import NumpyOps
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from ...attrs import NORM
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from ...gold import GoldParse
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from ...vocab import Vocab
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from ...tokens import Doc
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from ...pipeline import NeuralDependencyParser
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numpy.random.seed(0)
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@pytest.fixture
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def vocab():
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return Vocab(lex_attr_getters={NORM: lambda s: s})
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@pytest.fixture
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def parser(vocab):
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parser = NeuralDependencyParser(vocab)
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parser.cfg['token_vector_width'] = 4
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parser.cfg['hidden_width'] = 6
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parser.cfg['hist_size'] = 0
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parser.add_label('left')
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parser.begin_training([], **parser.cfg)
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sgd = Adam(NumpyOps(), 0.001)
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for i in range(30):
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losses = {}
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doc = Doc(vocab, words=['a', 'b', 'c', 'd'])
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gold = GoldParse(doc, heads=[1, 1, 3, 3],
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deps=['left', 'ROOT', 'left', 'ROOT'])
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parser.update([doc], [gold], sgd=sgd, losses=losses)
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return parser
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def test_add_label(parser):
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doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
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doc = parser(doc)
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assert doc[0].head.i == 1
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assert doc[0].dep_ == 'left'
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assert doc[1].head.i == 1
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assert doc[2].head.i == 3
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assert doc[2].head.i == 3
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parser.add_label('right')
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doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
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doc = parser(doc)
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assert doc[0].head.i == 1
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assert doc[0].dep_ == 'left'
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assert doc[1].head.i == 1
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assert doc[2].head.i == 3
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assert doc[2].head.i == 3
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sgd = Adam(NumpyOps(), 0.001)
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for i in range(10):
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losses = {}
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doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
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gold = GoldParse(doc, heads=[1, 1, 3, 3],
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deps=['right', 'ROOT', 'left', 'ROOT'])
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parser.update([doc], [gold], sgd=sgd, losses=losses)
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doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
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doc = parser(doc)
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assert doc[0].dep_ == 'right'
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assert doc[2].dep_ == 'left'
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