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72 lines
1.7 KiB
Python
72 lines
1.7 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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from thinc.neural import Model
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from mock import Mock
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import pytest
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import numpy
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from ..._ml import chain, Tok2Vec, doc2feats
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from ...vocab import Vocab
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from ...pipeline import TokenVectorEncoder
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from ...syntax.arc_eager import ArcEager
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from ...syntax.nn_parser import Parser
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from ...tokens.doc import Doc
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from ...gold import GoldParse
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@pytest.fixture
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def vocab():
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return Vocab()
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@pytest.fixture
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def arc_eager(vocab):
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actions = ArcEager.get_actions(left_labels=['L'], right_labels=['R'])
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return ArcEager(vocab.strings, actions)
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@pytest.fixture
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def tok2vec():
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return Tok2Vec(8, 100, preprocess=doc2feats())
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@pytest.fixture
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def parser(vocab, arc_eager):
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return Parser(vocab, moves=arc_eager, model=None)
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@pytest.fixture
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def model(arc_eager, tok2vec):
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return Parser.Model(arc_eager.n_moves, token_vector_width=tok2vec.nO)
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@pytest.fixture
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def doc(vocab):
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return Doc(vocab, words=['a', 'b', 'c'])
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@pytest.fixture
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def gold(doc):
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return GoldParse(doc, heads=[1, 1, 1], deps=['L', 'ROOT', 'R'])
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def test_can_init_nn_parser(parser):
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assert parser.model is None
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def test_build_model(parser):
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parser.model = Parser.Model(parser.moves.n_moves)
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assert parser.model is not None
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def test_predict_doc(parser, tok2vec, model, doc):
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doc.tensor = tok2vec([doc])
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parser.model = model
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parser(doc)
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def test_update_doc(parser, tok2vec, model, doc, gold):
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parser.model = model
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tokvecs, bp_tokvecs = tok2vec.begin_update([doc])
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d_tokvecs = parser.update((doc, tokvecs), gold)
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assert d_tokvecs.shape == tokvecs.shape
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def optimize(weights, gradient, key=None):
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weights -= 0.001 * gradient
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bp_tokvecs(d_tokvecs, sgd=optimize)
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assert d_tokvecs.sum() == 0.
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