spaCy/spacy/language.py

264 lines
7.8 KiB
Python

from os import path
from warnings import warn
import io
try:
import ujson as json
except ImportError:
import json
from .tokenizer import Tokenizer
from .vocab import Vocab
from .syntax.parser import Parser
from .tagger import Tagger
from .matcher import Matcher
from .serialize.packer import Packer
from ._ml import Model
from . import attrs
from . import orth
from .syntax.ner import BiluoPushDown
from .syntax.arc_eager import ArcEager
from .attrs import TAG, DEP, ENT_IOB, ENT_TYPE, HEAD
class Language(object):
@staticmethod
def lower(string):
return string.lower()
@staticmethod
def norm(string):
return string
@staticmethod
def shape(string):
return orth.word_shape(string)
@staticmethod
def prefix(string):
return string[0]
@staticmethod
def suffix(string):
return string[-3:]
@staticmethod
def prob(string):
return -30
@staticmethod
def cluster(string):
return 0
@staticmethod
def is_alpha(string):
return orth.is_alpha(string)
@staticmethod
def is_ascii(string):
return orth.is_ascii(string)
@staticmethod
def is_digit(string):
return string.isdigit()
@staticmethod
def is_lower(string):
return orth.is_lower(string)
@staticmethod
def is_punct(string):
return orth.is_punct(string)
@staticmethod
def is_space(string):
return string.isspace()
@staticmethod
def is_title(string):
return orth.is_title(string)
@staticmethod
def is_upper(string):
return orth.is_upper(string)
@staticmethod
def like_url(string):
return orth.like_url(string)
@staticmethod
def like_num(string):
return orth.like_number(string)
@staticmethod
def like_email(string):
return orth.like_email(string)
@staticmethod
def is_stop(string):
return 0
@classmethod
def default_lex_attrs(cls, data_dir=None):
return {
attrs.LOWER: cls.lower,
attrs.NORM: cls.norm,
attrs.SHAPE: cls.shape,
attrs.PREFIX: cls.prefix,
attrs.SUFFIX: cls.suffix,
attrs.CLUSTER: cls.cluster,
attrs.PROB: lambda string: -10.0,
attrs.IS_ALPHA: cls.is_alpha,
attrs.IS_ASCII: cls.is_ascii,
attrs.IS_DIGIT: cls.is_digit,
attrs.IS_LOWER: cls.is_lower,
attrs.IS_PUNCT: cls.is_punct,
attrs.IS_SPACE: cls.is_space,
attrs.IS_TITLE: cls.is_title,
attrs.IS_UPPER: cls.is_upper,
attrs.LIKE_URL: cls.like_url,
attrs.LIKE_NUM: cls.like_num,
attrs.LIKE_EMAIL: cls.like_email,
attrs.IS_STOP: cls.is_stop,
attrs.IS_OOV: lambda string: True
}
@classmethod
def default_dep_labels(cls):
return {0: {'ROOT': True}}
@classmethod
def default_ner_labels(cls):
return {0: {'PER': True, 'LOC': True, 'ORG': True, 'MISC': True}}
@classmethod
def default_data_dir(cls):
return path.join(path.dirname(__file__), 'data')
@classmethod
def default_vocab(cls, data_dir=None, get_lex_attr=None):
if data_dir is None:
data_dir = cls.default_data_dir()
if get_lex_attr is None:
get_lex_attr = cls.default_lex_attrs(data_dir)
return Vocab.from_dir(
path.join(data_dir, 'vocab'),
get_lex_attr=get_lex_attr)
@classmethod
def default_tokenizer(cls, vocab, data_dir):
if path.exists(data_dir):
return Tokenizer.from_dir(vocab, data_dir)
else:
return Tokenizer(vocab, {}, None, None, None)
@classmethod
def default_tagger(cls, vocab, data_dir):
if path.exists(data_dir):
return Tagger.from_dir(data_dir, vocab)
else:
return None
@classmethod
def default_parser(cls, vocab, data_dir):
if path.exists(data_dir):
return Parser.from_dir(data_dir, vocab.strings, ArcEager)
else:
return None
@classmethod
def default_entity(cls, vocab, data_dir):
if path.exists(data_dir):
return Parser.from_dir(data_dir, vocab.strings, BiluoPushDown)
else:
return None
@classmethod
def default_matcher(cls, vocab, data_dir):
if path.exists(data_dir):
return Matcher.from_dir(data_dir, vocab)
else:
return None
def __init__(self, data_dir=None, vocab=None, tokenizer=None, tagger=None,
parser=None, entity=None, matcher=None, serializer=None,
load_vectors=True):
if load_vectors is not True:
warn("load_vectors is deprecated", DeprecationWarning)
if data_dir in (None, True):
data_dir = self.default_data_dir()
if vocab in (None, True):
vocab = self.default_vocab(data_dir)
if tokenizer in (None, True):
tokenizer = self.default_tokenizer(vocab, data_dir=path.join(data_dir, 'tokenizer'))
if tagger in (None, True):
tagger = self.default_tagger(vocab, data_dir=path.join(data_dir, 'pos'))
if entity in (None, True):
entity = self.default_entity(vocab, data_dir=path.join(data_dir, 'ner'))
if parser in (None, True):
parser = self.default_parser(vocab, data_dir=path.join(data_dir, 'deps'))
if matcher in (None, True):
matcher = self.default_matcher(vocab, data_dir=data_dir)
self.vocab = vocab
self.tokenizer = tokenizer
self.tagger = tagger
self.parser = parser
self.entity = entity
self.matcher = matcher
def __reduce__(self):
return (self.__class__,
(None, self.vocab, self.tokenizer, self.tagger, self.parser,
self.entity, self.matcher, None),
None, None)
def __call__(self, text, tag=True, parse=True, entity=True):
"""Apply the pipeline to some text. The text can span multiple sentences,
and can contain arbtrary whitespace. Alignment into the original string
is preserved.
Args:
text (unicode): The text to be processed.
Returns:
tokens (spacy.tokens.Doc):
>>> from spacy.en import English
>>> nlp = English()
>>> tokens = nlp('An example sentence. Another example sentence.')
>>> tokens[0].orth_, tokens[0].head.tag_
('An', 'NN')
"""
tokens = self.tokenizer(text)
if self.tagger and tag:
self.tagger(tokens)
if self.matcher and entity:
self.matcher(tokens)
if self.parser and parse:
self.parser(tokens)
if self.entity and entity:
self.entity(tokens)
return tokens
def end_training(self, data_dir=None):
if data_dir is None:
data_dir = self.data_dir
self.parser.model.end_training(path.join(data_dir, 'deps', 'model'))
self.entity.model.end_training(path.join(data_dir, 'ner', 'model'))
self.tagger.model.end_training(path.join(data_dir, 'pos', 'model'))
strings_loc = path.join(data_dir, 'vocab', 'strings.json')
with io.open(strings_loc, 'w', encoding='utf8') as file_:
self.vocab.strings.dump(file_)
with open(path.join(data_dir, 'vocab', 'serializer.json'), 'w') as file_:
file_.write(
json.dumps([
(TAG, list(self.tagger.freqs[TAG].items())),
(DEP, list(self.parser.moves.freqs[DEP].items())),
(ENT_IOB, list(self.entity.moves.freqs[ENT_IOB].items())),
(ENT_TYPE, list(self.entity.moves.freqs[ENT_TYPE].items())),
(HEAD, list(self.parser.moves.freqs[HEAD].items()))]))