spaCy/spacy/language.py

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from __future__ import absolute_import
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 . 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
from .util import get_package
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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
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@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()
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@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)
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@staticmethod
def is_upper(string):
return orth.is_upper(string)
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@staticmethod
def like_url(string):
return orth.like_url(string)
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@staticmethod
def like_num(string):
return orth.like_number(string)
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@staticmethod
def like_email(string):
return orth.like_email(string)
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@staticmethod
def is_stop(string):
return 0
@classmethod
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def default_lex_attrs(cls):
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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,
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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,
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attrs.IS_UPPER: cls.is_upper,
attrs.LIKE_URL: cls.like_url,
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
}
@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}}
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@classmethod
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def default_vocab(cls, package=None, get_lex_attr=None):
if package is None:
package = get_package()
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if get_lex_attr is None:
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get_lex_attr = cls.default_lex_attrs()
return Vocab.from_package(package, get_lex_attr=get_lex_attr)
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@classmethod
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def default_parser(cls, package, vocab):
data_dir = package.dir_path('deps', require=False)
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if data_dir and path.exists(data_dir):
return Parser.from_dir(data_dir, vocab.strings, ArcEager)
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@classmethod
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def default_entity(cls, package, vocab):
data_dir = package.dir_path('ner', require=False)
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if data_dir and path.exists(data_dir):
return Parser.from_dir(data_dir, vocab.strings, BiluoPushDown)
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def __init__(self,
data_dir=None,
lang=None,
model=None,
vocab=None,
tokenizer=None,
tagger=None,
parser=None,
entity=None,
matcher=None,
serializer=None,
load_vectors=True):
"""
a model can be specified:
1) by a path to the model directory (DEPRECATED)
- Language(data_dir='path/to/data')
2) by a language identifier (and optionally a package root dir)
- Language(lang='en')
- Language(lang='en', data_dir='spacy/data')
3) by a model name/version (and optionally a package root dir)
- Language(model='en_default')
- Language(model='en_default ==1.0.0')
- Language(model='en_default <1.1.0, data_dir='spacy/data')
"""
# support non-package data dirs
if data_dir and path.exists(path.join(data_dir, 'vocab')):
class Package(object):
def __init__(self, root):
self.root = root
def has_file(self, *path_parts):
return path.exists(path.join(self.root, *path_parts))
def file_path(self, *path_parts, **kwargs):
return path.join(self.root, *path_parts)
def dir_path(self, *path_parts, **kwargs):
return path.join(self.root, *path_parts)
def load_utf8(self, func, *path_parts, **kwargs):
with io.open(self.file_path(path.join(*path_parts)),
mode='r', encoding='utf8') as f:
return func(f)
warn("using non-package data_dir", DeprecationWarning)
package = Package(data_dir)
else:
package = get_package(name=model, data_path=data_dir)
if load_vectors is not True:
warn("load_vectors is deprecated", DeprecationWarning)
if vocab in (None, True):
self.vocab = self.default_vocab(package)
if tokenizer in (None, True):
self.tokenizer = Tokenizer.from_package(package, self.vocab)
if tagger in (None, True):
self.tagger = Tagger.from_package(package, self.vocab)
if entity in (None, True):
self.entity = self.default_entity(package, self.vocab)
if parser in (None, True):
self.parser = self.default_parser(package, self.vocab)
if matcher in (None, True):
self.matcher = Matcher.from_package(package, self.vocab)
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def __reduce__(self):
args = (
None, # data_dir
self.vocab,
self.tokenizer,
self.tagger,
self.parser,
self.entity,
self.matcher,
)
return (self.__class__, args, None, None)
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,
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()
self.parser.model.dump(path.join(data_dir, 'deps', 'model'))
self.entity.model.end_training()
self.entity.model.dump(path.join(data_dir, 'ner', 'model'))
self.tagger.model.end_training()
self.tagger.model.dump(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_)
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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()))]))