111 lines
2.8 KiB
Python
111 lines
2.8 KiB
Python
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import pytest
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from thinc.api import Model
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from spacy import registry
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from spacy.pipeline._parser_internals.arc_eager import ArcEager
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from spacy.pipeline.dep_parser import DEFAULT_PARSER_MODEL
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from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
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from spacy.pipeline.transition_parser import Parser
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from spacy.tokens.doc import Doc
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from spacy.training import Example
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from spacy.vocab import Vocab
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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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cfg = {"model": DEFAULT_TOK2VEC_MODEL}
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tok2vec = registry.resolve(cfg, validate=True)["model"]
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tok2vec.initialize()
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return tok2vec
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@pytest.fixture
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def parser(vocab, arc_eager):
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config = {
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"learn_tokens": False,
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"min_action_freq": 30,
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"update_with_oracle_cut_size": 100,
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}
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cfg = {"model": DEFAULT_PARSER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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return Parser(vocab, model, moves=arc_eager, **config)
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@pytest.fixture
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def model(arc_eager, tok2vec, vocab):
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cfg = {"model": DEFAULT_PARSER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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model.attrs["resize_output"](model, arc_eager.n_moves)
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model.initialize()
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return model
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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 {"heads": [1, 1, 1], "deps": ["L", "ROOT", "R"]}
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def test_can_init_nn_parser(parser):
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assert isinstance(parser.model, Model)
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def test_build_model(parser, vocab):
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config = {
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"learn_tokens": False,
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"min_action_freq": 0,
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"update_with_oracle_cut_size": 100,
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}
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cfg = {"model": DEFAULT_PARSER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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parser.model = Parser(vocab, model=model, moves=parser.moves, **config).model
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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.predict([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(key, weights, gradient):
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weights -= 0.001 * gradient
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return weights, gradient
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example = Example.from_dict(doc, gold)
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parser.update([example], sgd=optimize)
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@pytest.mark.skip(reason="No longer supported")
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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.skip(reason="No longer supported")
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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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