import pytest from spacy import registry from spacy.gold import Example 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 thinc.api import Model from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL from spacy.pipeline.dep_parser import DEFAULT_PARSER_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(): cfg = {"model": DEFAULT_TOK2VEC_MODEL} tok2vec = registry.make_from_config(cfg, validate=True)["model"] tok2vec.initialize() return tok2vec @pytest.fixture def parser(vocab, arc_eager): config = { "learn_tokens": False, "min_action_freq": 30, "update_with_oracle_cut_size": 100, } cfg = {"model": DEFAULT_PARSER_MODEL} model = registry.make_from_config(cfg, validate=True)["model"] return Parser(vocab, model, moves=arc_eager, **config) @pytest.fixture def model(arc_eager, tok2vec, vocab): cfg = {"model": DEFAULT_PARSER_MODEL} model = registry.make_from_config(cfg, validate=True)["model"] 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 {"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): config = { "learn_tokens": False, "min_action_freq": 0, "update_with_oracle_cut_size": 100, } cfg = {"model": DEFAULT_PARSER_MODEL} model = registry.make_from_config(cfg, validate=True)["model"] parser.model = Parser(vocab, model=model, moves=parser.moves, **config).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 example = Example.from_dict(doc, gold) parser.update([example], sgd=optimize) @pytest.mark.skip(reason="No longer supported") def test_predict_doc_beam(parser, model, doc): parser.model = model parser(doc, beam_width=32, beam_density=0.001) @pytest.mark.skip(reason="No longer supported") 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)