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87 lines
1.8 KiB
Python
87 lines
1.8 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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import pytest
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from spacy._ml import Tok2Vec
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from spacy.vocab import Vocab
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from spacy.syntax.arc_eager import ArcEager
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from spacy.syntax.nn_parser import Parser
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from spacy.tokens.doc import Doc
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from spacy.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)
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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)[0]
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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, hist_size=0)[0]
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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])[0]
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parser.model = model
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parser(doc)
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def test_update_doc(parser, model, doc, gold):
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parser.model = model
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def optimize(weights, gradient, key=None):
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weights -= 0.001 * gradient
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parser.update([doc], [gold], sgd=optimize)
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@pytest.mark.xfail
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def test_predict_doc_beam(parser, model, doc):
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parser.model = model
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parser(doc, beam_width=32, beam_density=0.001)
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@pytest.mark.xfail
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def test_update_doc_beam(parser, model, doc, gold):
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parser.model = model
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def optimize(weights, gradient, key=None):
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weights -= 0.001 * gradient
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parser.update_beam([doc], [gold], sgd=optimize)
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