mirror of
https://github.com/explosion/spaCy.git
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4299a7f654
* Add `cuda110` to setup.cfg and quickstart dropdown * Switch to `pip` for pip-only packages in conda quickstart instructions * Update zh pkuseg install message with version range and conda * Remove `zh` from `extras_require` because the default doesn't require additional packages
337 lines
12 KiB
Python
337 lines
12 KiB
Python
from typing import Optional, List, Dict, Any, Callable, Iterable
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from enum import Enum
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import tempfile
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import srsly
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import warnings
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from pathlib import Path
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from ...errors import Warnings, Errors
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from ...language import Language
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from ...scorer import Scorer
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from ...tokens import Doc
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from ...training import validate_examples, Example
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from ...util import DummyTokenizer, registry, load_config_from_str
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from .lex_attrs import LEX_ATTRS
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from .stop_words import STOP_WORDS
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from ... import util
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# fmt: off
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_PKUSEG_INSTALL_MSG = "install spacy-pkuseg with `pip install \"spacy-pkuseg>=0.0.27,<0.1.0\"` or `conda install -c conda-forge \"spacy-pkuseg>=0.0.27,<0.1.0\"`"
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# fmt: on
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DEFAULT_CONFIG = """
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[nlp]
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[nlp.tokenizer]
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@tokenizers = "spacy.zh.ChineseTokenizer"
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segmenter = "char"
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[initialize]
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[initialize.tokenizer]
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pkuseg_model = null
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pkuseg_user_dict = "default"
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"""
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class Segmenter(str, Enum):
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char = "char"
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jieba = "jieba"
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pkuseg = "pkuseg"
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@classmethod
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def values(cls):
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return list(cls.__members__.keys())
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@registry.tokenizers("spacy.zh.ChineseTokenizer")
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def create_chinese_tokenizer(segmenter: Segmenter = Segmenter.char):
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def chinese_tokenizer_factory(nlp):
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return ChineseTokenizer(nlp, segmenter=segmenter)
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return chinese_tokenizer_factory
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class ChineseTokenizer(DummyTokenizer):
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def __init__(self, nlp: Language, segmenter: Segmenter = Segmenter.char):
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self.vocab = nlp.vocab
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if isinstance(segmenter, Segmenter):
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segmenter = segmenter.value
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self.segmenter = segmenter
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self.pkuseg_seg = None
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self.jieba_seg = None
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if segmenter not in Segmenter.values():
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warn_msg = Warnings.W103.format(
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lang="Chinese",
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segmenter=segmenter,
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supported=", ".join(Segmenter.values()),
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default="'char' (character segmentation)",
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)
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warnings.warn(warn_msg)
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self.segmenter = Segmenter.char
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if segmenter == Segmenter.jieba:
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self.jieba_seg = try_jieba_import()
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def initialize(
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self,
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get_examples: Optional[Callable[[], Iterable[Example]]] = None,
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*,
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nlp: Optional[Language] = None,
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pkuseg_model: Optional[str] = None,
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pkuseg_user_dict: Optional[str] = "default",
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):
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if self.segmenter == Segmenter.pkuseg:
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if pkuseg_user_dict is None:
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pkuseg_user_dict = pkuseg_model
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self.pkuseg_seg = try_pkuseg_import(
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pkuseg_model=pkuseg_model, pkuseg_user_dict=pkuseg_user_dict
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)
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def __call__(self, text: str) -> Doc:
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if self.segmenter == Segmenter.jieba:
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words = list([x for x in self.jieba_seg.cut(text, cut_all=False) if x])
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(words, spaces) = util.get_words_and_spaces(words, text)
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return Doc(self.vocab, words=words, spaces=spaces)
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elif self.segmenter == Segmenter.pkuseg:
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if self.pkuseg_seg is None:
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raise ValueError(Errors.E1000)
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words = self.pkuseg_seg.cut(text)
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(words, spaces) = util.get_words_and_spaces(words, text)
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return Doc(self.vocab, words=words, spaces=spaces)
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# warn if segmenter setting is not the only remaining option "char"
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if self.segmenter != Segmenter.char:
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warn_msg = Warnings.W103.format(
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lang="Chinese",
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segmenter=self.segmenter,
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supported=", ".join(Segmenter.values()),
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default="'char' (character segmentation)",
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)
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warnings.warn(warn_msg)
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# split into individual characters
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words = list(text)
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(words, spaces) = util.get_words_and_spaces(words, text)
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return Doc(self.vocab, words=words, spaces=spaces)
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def pkuseg_update_user_dict(self, words: List[str], reset: bool = False):
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if self.segmenter == Segmenter.pkuseg:
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if reset:
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try:
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import spacy_pkuseg
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self.pkuseg_seg.preprocesser = spacy_pkuseg.Preprocesser(None)
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except ImportError:
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msg = (
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"spacy_pkuseg not installed: unable to reset pkuseg "
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"user dict. Please " + _PKUSEG_INSTALL_MSG
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)
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raise ImportError(msg) from None
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for word in words:
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self.pkuseg_seg.preprocesser.insert(word.strip(), "")
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else:
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warn_msg = Warnings.W104.format(target="pkuseg", current=self.segmenter)
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warnings.warn(warn_msg)
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def score(self, examples):
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validate_examples(examples, "ChineseTokenizer.score")
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return Scorer.score_tokenization(examples)
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def _get_config(self) -> Dict[str, Any]:
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return {
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"segmenter": self.segmenter,
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}
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def _set_config(self, config: Dict[str, Any] = {}) -> None:
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self.segmenter = config.get("segmenter", Segmenter.char)
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def to_bytes(self, **kwargs):
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pkuseg_features_b = b""
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pkuseg_weights_b = b""
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pkuseg_processors_data = None
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if self.pkuseg_seg:
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with tempfile.TemporaryDirectory() as tempdir:
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self.pkuseg_seg.feature_extractor.save(tempdir)
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self.pkuseg_seg.model.save(tempdir)
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tempdir = Path(tempdir)
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with open(tempdir / "features.msgpack", "rb") as fileh:
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pkuseg_features_b = fileh.read()
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with open(tempdir / "weights.npz", "rb") as fileh:
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pkuseg_weights_b = fileh.read()
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pkuseg_processors_data = (
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_get_pkuseg_trie_data(self.pkuseg_seg.preprocesser.trie),
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self.pkuseg_seg.postprocesser.do_process,
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sorted(list(self.pkuseg_seg.postprocesser.common_words)),
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sorted(list(self.pkuseg_seg.postprocesser.other_words)),
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)
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serializers = {
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"cfg": lambda: srsly.json_dumps(self._get_config()),
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"pkuseg_features": lambda: pkuseg_features_b,
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"pkuseg_weights": lambda: pkuseg_weights_b,
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"pkuseg_processors": lambda: srsly.msgpack_dumps(pkuseg_processors_data),
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}
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return util.to_bytes(serializers, [])
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def from_bytes(self, data, **kwargs):
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pkuseg_data = {"features_b": b"", "weights_b": b"", "processors_data": None}
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def deserialize_pkuseg_features(b):
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pkuseg_data["features_b"] = b
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def deserialize_pkuseg_weights(b):
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pkuseg_data["weights_b"] = b
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def deserialize_pkuseg_processors(b):
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pkuseg_data["processors_data"] = srsly.msgpack_loads(b)
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deserializers = {
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"cfg": lambda b: self._set_config(srsly.json_loads(b)),
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"pkuseg_features": deserialize_pkuseg_features,
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"pkuseg_weights": deserialize_pkuseg_weights,
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"pkuseg_processors": deserialize_pkuseg_processors,
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}
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util.from_bytes(data, deserializers, [])
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if pkuseg_data["features_b"] and pkuseg_data["weights_b"]:
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with tempfile.TemporaryDirectory() as tempdir:
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tempdir = Path(tempdir)
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with open(tempdir / "features.msgpack", "wb") as fileh:
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fileh.write(pkuseg_data["features_b"])
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with open(tempdir / "weights.npz", "wb") as fileh:
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fileh.write(pkuseg_data["weights_b"])
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try:
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import spacy_pkuseg
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except ImportError:
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raise ImportError(
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"spacy-pkuseg not installed. To use this model, "
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+ _PKUSEG_INSTALL_MSG
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) from None
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self.pkuseg_seg = spacy_pkuseg.pkuseg(str(tempdir))
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if pkuseg_data["processors_data"]:
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processors_data = pkuseg_data["processors_data"]
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(user_dict, do_process, common_words, other_words) = processors_data
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self.pkuseg_seg.preprocesser = spacy_pkuseg.Preprocesser(user_dict)
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self.pkuseg_seg.postprocesser.do_process = do_process
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self.pkuseg_seg.postprocesser.common_words = set(common_words)
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self.pkuseg_seg.postprocesser.other_words = set(other_words)
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return self
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def to_disk(self, path, **kwargs):
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path = util.ensure_path(path)
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def save_pkuseg_model(path):
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if self.pkuseg_seg:
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if not path.exists():
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path.mkdir(parents=True)
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self.pkuseg_seg.model.save(path)
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self.pkuseg_seg.feature_extractor.save(path)
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def save_pkuseg_processors(path):
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if self.pkuseg_seg:
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data = (
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_get_pkuseg_trie_data(self.pkuseg_seg.preprocesser.trie),
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self.pkuseg_seg.postprocesser.do_process,
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sorted(list(self.pkuseg_seg.postprocesser.common_words)),
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sorted(list(self.pkuseg_seg.postprocesser.other_words)),
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)
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srsly.write_msgpack(path, data)
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serializers = {
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"cfg": lambda p: srsly.write_json(p, self._get_config()),
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"pkuseg_model": lambda p: save_pkuseg_model(p),
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"pkuseg_processors": lambda p: save_pkuseg_processors(p),
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}
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return util.to_disk(path, serializers, [])
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def from_disk(self, path, **kwargs):
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path = util.ensure_path(path)
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def load_pkuseg_model(path):
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try:
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import spacy_pkuseg
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except ImportError:
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if self.segmenter == Segmenter.pkuseg:
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raise ImportError(
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"spacy-pkuseg not installed. To use this model, "
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+ _PKUSEG_INSTALL_MSG
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) from None
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if path.exists():
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self.pkuseg_seg = spacy_pkuseg.pkuseg(path)
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def load_pkuseg_processors(path):
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try:
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import spacy_pkuseg
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except ImportError:
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if self.segmenter == Segmenter.pkuseg:
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raise ImportError(self._pkuseg_install_msg) from None
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if self.segmenter == Segmenter.pkuseg:
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data = srsly.read_msgpack(path)
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(user_dict, do_process, common_words, other_words) = data
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self.pkuseg_seg.preprocesser = spacy_pkuseg.Preprocesser(user_dict)
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self.pkuseg_seg.postprocesser.do_process = do_process
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self.pkuseg_seg.postprocesser.common_words = set(common_words)
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self.pkuseg_seg.postprocesser.other_words = set(other_words)
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serializers = {
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"cfg": lambda p: self._set_config(srsly.read_json(p)),
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"pkuseg_model": lambda p: load_pkuseg_model(p),
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"pkuseg_processors": lambda p: load_pkuseg_processors(p),
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}
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util.from_disk(path, serializers, [])
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class ChineseDefaults(Language.Defaults):
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config = load_config_from_str(DEFAULT_CONFIG)
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lex_attr_getters = LEX_ATTRS
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stop_words = STOP_WORDS
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writing_system = {"direction": "ltr", "has_case": False, "has_letters": False}
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class Chinese(Language):
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lang = "zh"
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Defaults = ChineseDefaults
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def try_jieba_import() -> None:
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try:
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import jieba
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# segment a short text to have jieba initialize its cache in advance
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list(jieba.cut("作为", cut_all=False))
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return jieba
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except ImportError:
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msg = (
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"Jieba not installed. To use jieba, install it with `pip "
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" install jieba` or from https://github.com/fxsjy/jieba"
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)
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raise ImportError(msg) from None
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def try_pkuseg_import(pkuseg_model: str, pkuseg_user_dict: str) -> None:
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try:
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import spacy_pkuseg
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except ImportError:
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msg = "spacy-pkuseg not installed. To use pkuseg, " + _PKUSEG_INSTALL_MSG
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raise ImportError(msg) from None
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try:
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return spacy_pkuseg.pkuseg(pkuseg_model, pkuseg_user_dict)
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except FileNotFoundError:
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msg = "Unable to load pkuseg model from: " + pkuseg_model
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raise FileNotFoundError(msg) from None
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def _get_pkuseg_trie_data(node, path=""):
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data = []
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for c, child_node in sorted(node.children.items()):
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data.extend(_get_pkuseg_trie_data(child_node, path + c))
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if node.isword:
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data.append((path, node.usertag))
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return data
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__all__ = ["Chinese"]
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