mirror of
https://github.com/explosion/spaCy.git
synced 2025-01-27 09:44:36 +03:00
e48a09df4e
* OrigAnnot class instead of gold.orig_annot list of zipped tuples * from_orig to replace from_annot_tuples * rename to RawAnnot * some unit tests for GoldParse creation and internal format * removing orig_annot and switching to lists instead of tuple * rewriting tuples to use RawAnnot (+ debug statements, WIP) * fix pop() changing the data * small fixes * pop-append fixes * return RawAnnot for existing GoldParse to have uniform interface * clean up imports * fix merge_sents * add unit test for 4402 with new structure (not working yet) * introduce DocAnnot * typo fixes * add unit test for merge_sents * rename from_orig to from_raw * fixing unit tests * fix nn parser * read_annots to produce text, doc_annot pairs * _make_golds fix * rename golds_to_gold_annots * small fixes * fix encoding * have golds_to_gold_annots use DocAnnot * missed a spot * merge_sents as function in DocAnnot * allow specifying only part of the token-level annotations * refactor with Example class + underlying dicts * pipeline components to work with Example objects (wip) * input checking * fix yielding * fix calls to update * small fixes * fix scorer unit test with new format * fix kwargs order * fixes for ud and conllu scripts * fix reading data for conllu script * add in proper errors (not fixed numbering yet to avoid merge conflicts) * fixing few more small bugs * fix EL script
88 lines
2.5 KiB
Python
88 lines
2.5 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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import pytest
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from thinc.neural.optimizers import Adam
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from thinc.neural.ops import NumpyOps
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from spacy.attrs import NORM
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from spacy.gold import GoldParse
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from spacy.vocab import Vocab
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from spacy.tokens import Doc
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from spacy.pipeline import DependencyParser, EntityRecognizer
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from spacy.util import fix_random_seed
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@pytest.fixture
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def vocab():
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return Vocab(lex_attr_getters={NORM: lambda s: s})
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@pytest.fixture
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def parser(vocab):
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parser = DependencyParser(vocab)
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return parser
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def test_init_parser(parser):
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pass
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def _train_parser(parser):
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fix_random_seed(1)
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parser.add_label("left")
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parser.begin_training([], **parser.cfg)
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sgd = Adam(NumpyOps(), 0.001)
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for i in range(5):
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losses = {}
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doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
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gold = GoldParse(doc, heads=[1, 1, 3, 3], deps=["left", "ROOT", "left", "ROOT"])
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parser.update((doc, gold), sgd=sgd, losses=losses)
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return parser
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def test_add_label(parser):
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parser = _train_parser(parser)
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parser.add_label("right")
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sgd = Adam(NumpyOps(), 0.001)
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for i in range(10):
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losses = {}
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doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
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gold = GoldParse(
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doc, heads=[1, 1, 3, 3], deps=["right", "ROOT", "left", "ROOT"]
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)
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parser.update((doc, gold), sgd=sgd, losses=losses)
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doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
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doc = parser(doc)
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assert doc[0].dep_ == "right"
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assert doc[2].dep_ == "left"
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def test_add_label_deserializes_correctly():
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ner1 = EntityRecognizer(Vocab())
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ner1.add_label("C")
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ner1.add_label("B")
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ner1.add_label("A")
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ner1.begin_training([])
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ner2 = EntityRecognizer(Vocab()).from_bytes(ner1.to_bytes())
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assert ner1.moves.n_moves == ner2.moves.n_moves
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for i in range(ner1.moves.n_moves):
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assert ner1.moves.get_class_name(i) == ner2.moves.get_class_name(i)
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@pytest.mark.parametrize(
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"pipe_cls,n_moves", [(DependencyParser, 5), (EntityRecognizer, 4)]
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)
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def test_add_label_get_label(pipe_cls, n_moves):
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"""Test that added labels are returned correctly. This test was added to
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test for a bug in DependencyParser.labels that'd cause it to fail when
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splitting the move names.
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"""
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labels = ["A", "B", "C"]
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pipe = pipe_cls(Vocab())
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for label in labels:
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pipe.add_label(label)
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assert len(pipe.move_names) == len(labels) * n_moves
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pipe_labels = sorted(list(pipe.labels))
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assert pipe_labels == labels
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