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Tidy up and auto-format
This commit is contained in:
Ines Montani
parent
0491a8e7c8
commit
0b8406a05c
31
spacy/_ml.py
31
spacy/_ml.py
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@ -661,21 +661,33 @@ def build_nel_encoder(embed_width, hidden_width, ner_types, **cfg):
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conv_depth = cfg.get("conv_depth", 2)
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cnn_maxout_pieces = cfg.get("cnn_maxout_pieces", 3)
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pretrained_vectors = cfg.get("pretrained_vectors") # self.nlp.vocab.vectors.name
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pretrained_vectors = cfg.get("pretrained_vectors") # self.nlp.vocab.vectors.name
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context_width = cfg.get("context_width")
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entity_width = cfg.get("entity_width")
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with Model.define_operators({">>": chain, "**": clone}):
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model = Affine(entity_width, entity_width+context_width+1+ner_types)\
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>> Affine(1, entity_width, drop_factor=0.0)\
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>> logistic
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model = (
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Affine(entity_width, entity_width + context_width + 1 + ner_types)
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>> Affine(1, entity_width, drop_factor=0.0)
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>> logistic
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)
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# context encoder
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tok2vec = Tok2Vec(width=hidden_width, embed_size=embed_width, pretrained_vectors=pretrained_vectors,
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cnn_maxout_pieces=cnn_maxout_pieces, subword_features=True, conv_depth=conv_depth,
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bilstm_depth=0) >> flatten_add_lengths >> Pooling(mean_pool)\
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>> Residual(zero_init(Maxout(hidden_width, hidden_width))) \
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>> zero_init(Affine(context_width, hidden_width))
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tok2vec = (
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Tok2Vec(
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width=hidden_width,
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embed_size=embed_width,
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pretrained_vectors=pretrained_vectors,
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cnn_maxout_pieces=cnn_maxout_pieces,
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subword_features=True,
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conv_depth=conv_depth,
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bilstm_depth=0,
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)
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>> flatten_add_lengths
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>> Pooling(mean_pool)
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>> Residual(zero_init(Maxout(hidden_width, hidden_width)))
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>> zero_init(Affine(context_width, hidden_width))
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)
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model.tok2vec = tok2vec
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@ -684,6 +696,7 @@ def build_nel_encoder(embed_width, hidden_width, ner_types, **cfg):
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model.nO = 1
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return model
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@layerize
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def flatten(seqs, drop=0.0):
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ops = Model.ops
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@ -6,7 +6,7 @@ import sys
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from .stop_words import STOP_WORDS
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from .tag_map import TAG_MAP, POS
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from .tag_map import TAG_MAP
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from ...attrs import LANG
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from ...language import Language
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from ...tokens import Doc
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@ -22,6 +22,7 @@ if is_python_pre_3_5:
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Morpheme = namedtuple("Morpheme", "surface lemma tag")
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elif is_python_post_3_7:
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from dataclasses import dataclass
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@dataclass(frozen=True)
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class Morpheme:
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surface: str
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@ -29,6 +30,7 @@ elif is_python_post_3_7:
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tag: str
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else:
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from typing import NamedTuple
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class Morpheme(NamedTuple):
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surface: str
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lemma: str
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@ -1,10 +1,8 @@
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# encoding: utf8
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from __future__ import unicode_literals
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from collections import defaultdict
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from ...symbols import (POS, PUNCT, INTJ, X, SYM,
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ADJ, AUX, ADP, CONJ, NOUN, PRON, VERB, ADV, PROPN,
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NUM, DET)
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from ...symbols import POS, PUNCT, INTJ, X, SYM, ADJ, AUX, ADP, CONJ, NOUN, PRON
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from ...symbols import VERB, ADV, PROPN, NUM, DET
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# 은전한닢(mecab-ko-dic)의 품사 태그를 universal pos tag로 대응시킴
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# https://docs.google.com/spreadsheets/d/1-9blXKjtjeKZqsf4NzHeYJCrr49-nXeRF6D80udfcwY/edit#gid=589544265
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@ -18,49 +16,44 @@ TAG_MAP = {
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"JKB": {POS: ADP},
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"JKV": {POS: ADP},
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"JKQ": {POS: ADP},
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"JX": {POS: ADP}, # 보조사
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"JX": {POS: ADP}, # 보조사
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"JC": {POS: CONJ}, # 접속 조사
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"MAJ": {POS: CONJ}, # 접속 부사
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"MAJ": {POS: CONJ}, # 접속 부사
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"MAG": {POS: ADV}, # 일반 부사
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"MM": {POS: DET}, # 관형사
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"MM": {POS: DET}, # 관형사
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"XPN": {POS: X}, # 접두사
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# XS. 접미사
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"XSN": {POS: X},
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"XSV": {POS: X},
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"XSA": {POS: X},
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"XR": {POS: X}, # 어근
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"XR": {POS: X}, # 어근
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# E.{1,2} 어미
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"EP": {POS: X},
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"EF": {POS: X},
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"EC": {POS: X},
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"ETN": {POS: X},
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"ETM": {POS: X},
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"IC": {POS: INTJ}, # 감탄사
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"VV": {POS: VERB}, # 동사
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"VA": {POS: ADJ}, # 형용사
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"VX": {POS: AUX}, # 보조 용언
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"VA": {POS: ADJ}, # 형용사
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"VX": {POS: AUX}, # 보조 용언
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"VCP": {POS: ADP}, # 긍정 지정사(이다)
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"VCN": {POS: ADJ}, # 부정 지정사(아니다)
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"NNG": {POS: NOUN}, # 일반 명사(general noun)
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"NNB": {POS: NOUN}, # 의존 명사
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"NNBC": {POS: NOUN}, # 의존 명사(단위: unit)
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"NNP": {POS: PROPN}, # 고유 명사(proper noun)
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"NNG": {POS: NOUN}, # 일반 명사(general noun)
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"NNB": {POS: NOUN}, # 의존 명사
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"NNBC": {POS: NOUN}, # 의존 명사(단위: unit)
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"NNP": {POS: PROPN}, # 고유 명사(proper noun)
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"NP": {POS: PRON}, # 대명사
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"NR": {POS: NUM}, # 수사(numerals)
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"SN": {POS: NUM}, # 숫자
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"NR": {POS: NUM}, # 수사(numerals)
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"SN": {POS: NUM}, # 숫자
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# S.{1,2} 부호
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# 문장 부호
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"SF": {POS: PUNCT}, # period or other EOS marker
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"SF": {POS: PUNCT}, # period or other EOS marker
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"SE": {POS: PUNCT},
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"SC": {POS: PUNCT}, # comma, etc.
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"SSO": {POS: PUNCT}, # open bracket
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"SSC": {POS: PUNCT}, # close bracket
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"SY": {POS: SYM}, # 기타 기호
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"SL": {POS: X}, # 외국어
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"SH": {POS: X}, # 한자
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"SC": {POS: PUNCT}, # comma, etc.
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"SSO": {POS: PUNCT}, # open bracket
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"SSC": {POS: PUNCT}, # close bracket
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"SY": {POS: SYM}, # 기타 기호
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"SL": {POS: X}, # 외국어
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"SH": {POS: X}, # 한자
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}
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@ -5,8 +5,7 @@ import pytest
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@pytest.mark.parametrize(
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"word,lemma",
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[("새로운", "새롭"), ("빨간", "빨갛"), ("클수록", "크"), ("뭡니까", "뭣"), ("됐다", "되")],
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"word,lemma", [("새로운", "새롭"), ("빨간", "빨갛"), ("클수록", "크"), ("뭡니까", "뭣"), ("됐다", "되")]
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)
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def test_ko_lemmatizer_assigns(ko_tokenizer, word, lemma):
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test_lemma = ko_tokenizer(word)[0].lemma_
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@ -12,9 +12,19 @@ yra žemesnis arba lygus žemiausiam vidutiniam daugiamečiam šiltojo laikotarp
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assert len(tokens) == 42
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@pytest.mark.parametrize('text,length', [
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("177R Parodų rūmai–Ozo g. nuo vasario 18 d. bus skelbiamas interneto tinklalapyje.", 15),
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("ISM universiteto doc. dr. Ieva Augutytė-Kvedaravičienė pastebi, kad tyrimais nustatyti elgesio pokyčiai.", 16)])
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@pytest.mark.parametrize(
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"text,length",
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[
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(
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"177R Parodų rūmai–Ozo g. nuo vasario 18 d. bus skelbiamas interneto tinklalapyje.",
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15,
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),
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(
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"ISM universiteto doc. dr. Ieva Augutytė-Kvedaravičienė pastebi, kad tyrimais nustatyti elgesio pokyčiai.",
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16,
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),
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],
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)
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def test_lt_tokenizer_handles_punct_abbrev(lt_tokenizer, text, length):
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tokens = lt_tokenizer(text)
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assert len(tokens) == length
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@ -26,18 +36,22 @@ def test_lt_tokenizer_abbrev_exceptions(lt_tokenizer, text):
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assert len(tokens) == 1
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@pytest.mark.parametrize("text,match", [
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("10", True),
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("1", True),
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("10,000", True),
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("10,00", True),
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("999.0", True),
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("vienas", True),
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("du", True),
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("milijardas", True),
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("šuo", False),
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(",", False),
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("1/2", True)])
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@pytest.mark.parametrize(
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"text,match",
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[
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("10", True),
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("1", True),
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("10,000", True),
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("10,00", True),
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("999.0", True),
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("vienas", True),
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("du", True),
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("milijardas", True),
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("šuo", False),
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(",", False),
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("1/2", True),
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],
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)
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def test_lt_lex_attrs_like_number(lt_tokenizer, text, match):
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tokens = lt_tokenizer(text)
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assert len(tokens) == 1
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@ -5,7 +5,6 @@ import pytest
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import re
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from spacy.matcher import Matcher, DependencyMatcher
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from spacy.tokens import Doc, Token
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from ..util import get_doc
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@pytest.fixture
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@ -288,24 +287,43 @@ def deps():
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def dependency_matcher(en_vocab):
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def is_brown_yellow(text):
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return bool(re.compile(r"brown|yellow|over").match(text))
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IS_BROWN_YELLOW = en_vocab.add_flag(is_brown_yellow)
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pattern1 = [
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{"SPEC": {"NODE_NAME": "fox"}, "PATTERN": {"ORTH": "fox"}},
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{"SPEC": {"NODE_NAME": "q", "NBOR_RELOP": ">", "NBOR_NAME": "fox"},"PATTERN": {"ORTH": "quick", "DEP": "amod"}},
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{"SPEC": {"NODE_NAME": "r", "NBOR_RELOP": ">", "NBOR_NAME": "fox"}, "PATTERN": {IS_BROWN_YELLOW: True}},
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{
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"SPEC": {"NODE_NAME": "q", "NBOR_RELOP": ">", "NBOR_NAME": "fox"},
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"PATTERN": {"ORTH": "quick", "DEP": "amod"},
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},
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{
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"SPEC": {"NODE_NAME": "r", "NBOR_RELOP": ">", "NBOR_NAME": "fox"},
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"PATTERN": {IS_BROWN_YELLOW: True},
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},
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]
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pattern2 = [
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{"SPEC": {"NODE_NAME": "jumped"}, "PATTERN": {"ORTH": "jumped"}},
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{"SPEC": {"NODE_NAME": "fox", "NBOR_RELOP": ">", "NBOR_NAME": "jumped"}, "PATTERN": {"ORTH": "fox"}},
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{"SPEC": {"NODE_NAME": "quick", "NBOR_RELOP": ".", "NBOR_NAME": "jumped"}, "PATTERN": {"ORTH": "fox"}}
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{
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"SPEC": {"NODE_NAME": "fox", "NBOR_RELOP": ">", "NBOR_NAME": "jumped"},
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"PATTERN": {"ORTH": "fox"},
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},
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{
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"SPEC": {"NODE_NAME": "quick", "NBOR_RELOP": ".", "NBOR_NAME": "jumped"},
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"PATTERN": {"ORTH": "fox"},
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},
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]
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pattern3 = [
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{"SPEC": {"NODE_NAME": "jumped"}, "PATTERN": {"ORTH": "jumped"}},
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{"SPEC": {"NODE_NAME": "fox", "NBOR_RELOP": ">", "NBOR_NAME": "jumped"}, "PATTERN": {"ORTH": "fox"}},
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{"SPEC": {"NODE_NAME": "r", "NBOR_RELOP": ">>", "NBOR_NAME": "fox"}, "PATTERN": {"ORTH": "brown"}}
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{
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"SPEC": {"NODE_NAME": "fox", "NBOR_RELOP": ">", "NBOR_NAME": "jumped"},
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"PATTERN": {"ORTH": "fox"},
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},
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{
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"SPEC": {"NODE_NAME": "r", "NBOR_RELOP": ">>", "NBOR_NAME": "fox"},
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"PATTERN": {"ORTH": "brown"},
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},
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]
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matcher = DependencyMatcher(en_vocab)
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@ -320,9 +338,9 @@ def test_dependency_matcher_compile(dependency_matcher):
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assert len(dependency_matcher) == 3
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def test_dependency_matcher(dependency_matcher, text, heads, deps):
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doc = get_doc(dependency_matcher.vocab, text.split(), heads=heads, deps=deps)
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matches = dependency_matcher(doc)
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# assert matches[0][1] == [[3, 1, 2]]
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# assert matches[1][1] == [[4, 3, 3]]
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# assert matches[2][1] == [[4, 3, 2]]
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# def test_dependency_matcher(dependency_matcher, text, heads, deps):
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# doc = get_doc(dependency_matcher.vocab, text.split(), heads=heads, deps=deps)
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# matches = dependency_matcher(doc)
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# assert matches[0][1] == [[3, 1, 2]]
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# assert matches[1][1] == [[4, 3, 3]]
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# assert matches[2][1] == [[4, 3, 2]]
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@ -1,7 +1,7 @@
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# coding: utf8
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from __future__ import unicode_literals
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from spacy.lang.en import English
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import pytest
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def test_issue3880():
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