import pytest from spacy.pipeline.defaults import default_parser, default_tok2vec from spacy.vocab import Vocab from spacy.syntax.arc_eager import ArcEager from spacy.syntax.nn_parser import Parser from spacy.tokens.doc import Doc from spacy.gold import GoldParse from thinc.api import Model @pytest.fixture def vocab(): return Vocab() @pytest.fixture def arc_eager(vocab): actions = ArcEager.get_actions(left_labels=["L"], right_labels=["R"]) return ArcEager(vocab.strings, actions) @pytest.fixture def tok2vec(): tok2vec = default_tok2vec() tok2vec.initialize() return tok2vec @pytest.fixture def parser(vocab, arc_eager): return Parser(vocab, model=default_parser(), moves=arc_eager) @pytest.fixture def model(arc_eager, tok2vec, vocab): model = default_parser() model.attrs["resize_output"](model, arc_eager.n_moves) model.initialize() return model @pytest.fixture def doc(vocab): return Doc(vocab, words=["a", "b", "c"]) @pytest.fixture def gold(doc): return GoldParse(doc, heads=[1, 1, 1], deps=["L", "ROOT", "R"]) def test_can_init_nn_parser(parser): assert isinstance(parser.model, Model) def test_build_model(parser, vocab): parser.model = Parser(vocab, model=default_parser(), moves=parser.moves).model assert parser.model is not None def test_predict_doc(parser, tok2vec, model, doc): doc.tensor = tok2vec.predict([doc])[0] parser.model = model parser(doc) def test_update_doc(parser, model, doc, gold): parser.model = model def optimize(key, weights, gradient): weights -= 0.001 * gradient return weights, gradient parser.update((doc, gold), sgd=optimize) @pytest.mark.xfail def test_predict_doc_beam(parser, model, doc): parser.model = model parser(doc, beam_width=32, beam_density=0.001) @pytest.mark.xfail def test_update_doc_beam(parser, model, doc, gold): parser.model = model def optimize(weights, gradient, key=None): weights -= 0.001 * gradient parser.update_beam((doc, gold), sgd=optimize)