mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-14 05:37:03 +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
76 lines
2.0 KiB
Python
76 lines
2.0 KiB
Python
# coding: utf8
|
|
from __future__ import unicode_literals
|
|
|
|
import pytest
|
|
from thinc.neural.optimizers import Adam
|
|
from thinc.neural.ops import NumpyOps
|
|
from spacy.attrs import NORM
|
|
from spacy.gold import GoldParse
|
|
from spacy.vocab import Vocab
|
|
from spacy.tokens import Doc
|
|
from spacy.pipeline import DependencyParser
|
|
|
|
|
|
@pytest.fixture
|
|
def vocab():
|
|
return Vocab(lex_attr_getters={NORM: lambda s: s})
|
|
|
|
|
|
@pytest.fixture
|
|
def parser(vocab):
|
|
parser = DependencyParser(vocab)
|
|
parser.cfg["token_vector_width"] = 4
|
|
parser.cfg["hidden_width"] = 32
|
|
# parser.add_label('right')
|
|
parser.add_label("left")
|
|
parser.begin_training([], **parser.cfg)
|
|
sgd = Adam(NumpyOps(), 0.001)
|
|
|
|
for i in range(10):
|
|
losses = {}
|
|
doc = Doc(vocab, words=["a", "b", "c", "d"])
|
|
gold = GoldParse(doc, heads=[1, 1, 3, 3], deps=["left", "ROOT", "left", "ROOT"])
|
|
parser.update((doc, gold), sgd=sgd, losses=losses)
|
|
return parser
|
|
|
|
|
|
def test_no_sentences(parser):
|
|
doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
|
|
doc = parser(doc)
|
|
assert len(list(doc.sents)) >= 1
|
|
|
|
|
|
def test_sents_1(parser):
|
|
doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
|
|
doc[2].sent_start = True
|
|
doc = parser(doc)
|
|
assert len(list(doc.sents)) >= 2
|
|
doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
|
|
doc[1].sent_start = False
|
|
doc[2].sent_start = True
|
|
doc[3].sent_start = False
|
|
doc = parser(doc)
|
|
assert len(list(doc.sents)) == 2
|
|
|
|
|
|
def test_sents_1_2(parser):
|
|
doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
|
|
doc[1].sent_start = True
|
|
doc[2].sent_start = True
|
|
doc = parser(doc)
|
|
assert len(list(doc.sents)) >= 3
|
|
|
|
|
|
def test_sents_1_3(parser):
|
|
doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
|
|
doc[1].sent_start = True
|
|
doc[3].sent_start = True
|
|
doc = parser(doc)
|
|
assert len(list(doc.sents)) >= 3
|
|
doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
|
|
doc[1].sent_start = True
|
|
doc[2].sent_start = False
|
|
doc[3].sent_start = True
|
|
doc = parser(doc)
|
|
assert len(list(doc.sents)) == 3
|