mirror of https://github.com/explosion/spaCy.git
334 lines
9.9 KiB
Python
334 lines
9.9 KiB
Python
from __future__ import absolute_import
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from os import path
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from warnings import warn
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import io
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try:
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import ujson as json
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except ImportError:
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import json
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from .tokenizer import Tokenizer
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from .vocab import Vocab
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from .syntax.parser import Parser
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from .tagger import Tagger
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from .matcher import Matcher
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from .serialize.packer import Packer
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from . import attrs
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from . import orth
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from .syntax.ner import BiluoPushDown
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from .syntax.arc_eager import ArcEager
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from . import util
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from .attrs import TAG, DEP, ENT_IOB, ENT_TYPE, HEAD
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class Language(object):
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lang = None
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@staticmethod
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def lower(string):
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return string.lower()
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@staticmethod
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def norm(string):
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return string
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@staticmethod
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def shape(string):
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return orth.word_shape(string)
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@staticmethod
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def prefix(string):
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return string[0]
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@staticmethod
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def suffix(string):
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return string[-3:]
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@staticmethod
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def prob(string):
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return -30
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@staticmethod
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def cluster(string):
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return 0
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@staticmethod
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def is_alpha(string):
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return orth.is_alpha(string)
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@staticmethod
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def is_ascii(string):
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return orth.is_ascii(string)
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@staticmethod
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def is_digit(string):
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return string.isdigit()
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@staticmethod
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def is_lower(string):
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return orth.is_lower(string)
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@staticmethod
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def is_punct(string):
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return orth.is_punct(string)
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@staticmethod
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def is_space(string):
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return string.isspace()
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@staticmethod
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def is_title(string):
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return orth.is_title(string)
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@staticmethod
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def is_upper(string):
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return orth.is_upper(string)
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@staticmethod
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def like_url(string):
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return orth.like_url(string)
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@staticmethod
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def like_num(string):
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return orth.like_number(string)
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@staticmethod
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def like_email(string):
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return orth.like_email(string)
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@staticmethod
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def is_stop(string):
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return 0
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@classmethod
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def default_lex_attrs(cls):
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return {
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attrs.LOWER: cls.lower,
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attrs.NORM: cls.norm,
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attrs.SHAPE: cls.shape,
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attrs.PREFIX: cls.prefix,
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attrs.SUFFIX: cls.suffix,
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attrs.CLUSTER: cls.cluster,
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attrs.PROB: lambda string: -10.0,
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attrs.IS_ALPHA: cls.is_alpha,
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attrs.IS_ASCII: cls.is_ascii,
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attrs.IS_DIGIT: cls.is_digit,
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attrs.IS_LOWER: cls.is_lower,
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attrs.IS_PUNCT: cls.is_punct,
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attrs.IS_SPACE: cls.is_space,
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attrs.IS_TITLE: cls.is_title,
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attrs.IS_UPPER: cls.is_upper,
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attrs.LIKE_URL: cls.like_url,
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attrs.LIKE_NUM: cls.like_num,
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attrs.LIKE_EMAIL: cls.like_email,
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attrs.IS_STOP: cls.is_stop,
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attrs.IS_OOV: lambda string: True
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}
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@classmethod
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def default_dep_labels(cls):
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return {0: {'ROOT': True}}
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@classmethod
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def default_ner_labels(cls):
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return {0: {'PER': True, 'LOC': True, 'ORG': True, 'MISC': True}}
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@classmethod
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def default_vocab(cls, package, get_lex_attr=None):
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if get_lex_attr is None:
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get_lex_attr = cls.default_lex_attrs()
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if hasattr(package, 'dir_path'):
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return Vocab.from_package(package, get_lex_attr=get_lex_attr)
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else:
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return Vocab.load(package, get_lex_attr)
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@classmethod
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def default_parser(cls, package, vocab):
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if hasattr(package, 'dir_path'):
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data_dir = package.dir_path('deps')
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else:
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data_dir = package
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if data_dir and path.exists(data_dir):
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return Parser.from_dir(data_dir, vocab.strings, ArcEager)
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else:
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return None
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@classmethod
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def default_entity(cls, package, vocab):
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if hasattr(package, 'dir_path'):
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data_dir = package.dir_path('ner')
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else:
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data_dir = package
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if data_dir and path.exists(data_dir):
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return Parser.from_dir(data_dir, vocab.strings, BiluoPushDown)
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else:
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return None
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def __init__(self,
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data_dir=None,
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vocab=None,
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tokenizer=None,
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tagger=None,
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parser=None,
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entity=None,
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matcher=None,
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serializer=None,
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load_vectors=True,
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package=None):
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"""
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a model can be specified:
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1) by calling a Language subclass
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- spacy.en.English()
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2) by calling a Language subclass with data_dir
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- spacy.en.English('my/model/root')
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- spacy.en.English(data_dir='my/model/root')
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3) by package name
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- spacy.load('en_default')
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- spacy.load('en_default==1.0.0')
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4) by package name with a relocated package base
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- spacy.load('en_default', via='/my/package/root')
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- spacy.load('en_default==1.0.0', via='/my/package/root')
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"""
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if package is None:
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if data_dir is None:
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package = util.get_package_by_name()
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else:
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package = util.get_package(data_dir)
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if load_vectors is not True:
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warn("load_vectors is deprecated", DeprecationWarning)
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if vocab in (None, True):
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vocab = self.default_vocab(package)
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self.vocab = vocab
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if tokenizer in (None, True):
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tokenizer = Tokenizer.from_package(package, self.vocab)
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self.tokenizer = tokenizer
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if tagger in (None, True):
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tagger = Tagger.from_package(package, self.vocab)
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self.tagger = tagger
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if entity in (None, True):
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entity = self.default_entity(package, self.vocab)
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self.entity = entity
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if parser in (None, True):
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parser = self.default_parser(package, self.vocab)
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self.parser = parser
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if matcher in (None, True):
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matcher = Matcher.from_package(package, self.vocab)
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self.matcher = matcher
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def __reduce__(self):
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args = (
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None, # data_dir
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self.vocab,
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self.tokenizer,
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self.tagger,
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self.parser,
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self.entity,
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self.matcher
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)
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return (self.__class__, args, None, None)
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def __call__(self, text, tag=True, parse=True, entity=True):
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"""Apply the pipeline to some text. The text can span multiple sentences,
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and can contain arbtrary whitespace. Alignment into the original string
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is preserved.
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Args:
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text (unicode): The text to be processed.
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Returns:
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tokens (spacy.tokens.Doc):
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>>> from spacy.en import English
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>>> nlp = English()
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>>> tokens = nlp('An example sentence. Another example sentence.')
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>>> tokens[0].orth_, tokens[0].head.tag_
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('An', 'NN')
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"""
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tokens = self.tokenizer(text)
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if self.tagger and tag:
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self.tagger(tokens)
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if self.matcher and entity:
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self.matcher(tokens)
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if self.parser and parse:
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self.parser(tokens)
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if self.entity and entity:
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# Add any of the entity labels already set, in case we don't have them.
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for tok in tokens:
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if tok.ent_type != 0:
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self.entity.add_label(tok.ent_type)
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self.entity(tokens)
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return tokens
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def pipe(self, texts, tag=True, parse=True, entity=True, n_threads=2,
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batch_size=1000):
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stream = self.tokenizer.pipe(texts,
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n_threads=n_threads, batch_size=batch_size)
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if self.tagger and tag:
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stream = self.tagger.pipe(stream,
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n_threads=n_threads, batch_size=batch_size)
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if self.matcher and entity:
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stream = self.matcher.pipe(stream,
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n_threads=n_threads, batch_size=batch_size)
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if self.parser and parse:
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stream = self.parser.pipe(stream,
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n_threads=n_threads, batch_size=batch_size)
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if self.entity and entity:
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stream = self.entity.pipe(stream,
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n_threads=n_threads, batch_size=batch_size)
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for doc in stream:
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yield doc
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def end_training(self, data_dir=None):
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if data_dir is None:
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data_dir = self.data_dir
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if self.parser:
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self.parser.model.end_training()
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self.parser.model.dump(path.join(data_dir, 'deps', 'model'))
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if self.entity:
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self.entity.model.end_training()
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self.entity.model.dump(path.join(data_dir, 'ner', 'model'))
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if self.tagger:
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self.tagger.model.end_training()
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self.tagger.model.dump(path.join(data_dir, 'pos', 'model'))
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strings_loc = path.join(data_dir, 'vocab', 'strings.json')
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with io.open(strings_loc, 'w', encoding='utf8') as file_:
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self.vocab.strings.dump(file_)
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self.vocab.dump(path.join(data_dir, 'vocab', 'lexemes.bin'))
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if self.tagger:
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tagger_freqs = list(self.tagger.freqs[TAG].items())
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else:
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tagger_freqs = []
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if self.parser:
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dep_freqs = list(self.parser.moves.freqs[DEP].items())
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head_freqs = list(self.parser.moves.freqs[HEAD].items())
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else:
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dep_freqs = []
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head_freqs = []
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if self.entity:
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entity_iob_freqs = list(self.entity.moves.freqs[ENT_IOB].items())
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entity_type_freqs = list(self.entity.moves.freqs[ENT_TYPE].items())
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else:
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entity_iob_freqs = []
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entity_type_freqs = []
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with open(path.join(data_dir, 'vocab', 'serializer.json'), 'w') as file_:
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file_.write(
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json.dumps([
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(TAG, tagger_freqs),
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(DEP, dep_freqs),
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(ENT_IOB, entity_iob_freqs),
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(ENT_TYPE, entity_type_freqs),
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(HEAD, head_freqs)
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]))
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