mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-26 18:06:29 +03:00
269 lines
9.2 KiB
Python
269 lines
9.2 KiB
Python
from __future__ import absolute_import
|
|
from __future__ import unicode_literals
|
|
from warnings import warn
|
|
import pathlib
|
|
|
|
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 . import attrs
|
|
from . import orth
|
|
from .syntax.ner import BiluoPushDown
|
|
from .syntax.arc_eager import ArcEager
|
|
from . import util
|
|
|
|
from .attrs import TAG, DEP, ENT_IOB, ENT_TYPE, HEAD, PROB, LANG, IS_STOP
|
|
|
|
|
|
class BaseDefaults(object):
|
|
def __init__(self, lang, path):
|
|
self.path = path
|
|
self.lang = lang
|
|
self.lex_attr_getters = dict(self.__class__.lex_attr_getters)
|
|
if (self.path / 'vocab' / 'oov_prob').exists():
|
|
with (self.path / 'vocab' / 'oov_prob').open() as file_:
|
|
oov_prob = file_.read().strip()
|
|
self.lex_attr_getters[PROB] = lambda string: oov_prob
|
|
self.lex_attr_getters[LANG] = lambda string: lang
|
|
self.lex_attr_getters[IS_STOP] = lambda string: string in self.stop_words
|
|
|
|
def Vectors(self):
|
|
return True
|
|
|
|
def Vocab(self, vectors=None, lex_attr_getters=None):
|
|
if lex_attr_getters is None:
|
|
lex_attr_getters = dict(self.lex_attr_getters)
|
|
if vectors is None:
|
|
vectors = self.Vectors()
|
|
return Vocab.load(self.path, get_lex_attr=self.lex_attr_getters, vectors=vectors)
|
|
|
|
def Tokenizer(self, vocab):
|
|
return Tokenizer.load(self.path, vocab)
|
|
|
|
def Tagger(self, vocab):
|
|
return Tagger.load(self.path / 'pos', vocab)
|
|
|
|
def Parser(self, vocab):
|
|
if (self.path / 'deps').exists():
|
|
return Parser.load(self.path / 'deps', vocab, ArcEager)
|
|
else:
|
|
return None
|
|
|
|
def Entity(self, vocab):
|
|
if (self.path / 'ner').exists():
|
|
return Parser.load(self.path / 'ner', vocab, BiluoPushDown)
|
|
else:
|
|
return None
|
|
|
|
def Matcher(self, vocab):
|
|
return Matcher.load(self.path, vocab)
|
|
|
|
def Pipeline(self, nlp):
|
|
return [
|
|
nlp.tokenizer,
|
|
nlp.tagger,
|
|
nlp.parser,
|
|
nlp.entity]
|
|
|
|
dep_labels = {0: {'ROOT': True}}
|
|
|
|
ner_labels = {0: {'PER': True, 'LOC': True, 'ORG': True, 'MISC': True}}
|
|
|
|
|
|
stop_words = set()
|
|
|
|
lex_attr_getters = {
|
|
attrs.LOWER: lambda string: string.lower(),
|
|
attrs.NORM: lambda string: string,
|
|
attrs.SHAPE: orth.word_shape,
|
|
attrs.PREFIX: lambda string: string[0],
|
|
attrs.SUFFIX: lambda string: string[-3:],
|
|
attrs.CLUSTER: lambda string: 0,
|
|
attrs.IS_ALPHA: orth.is_alpha,
|
|
attrs.IS_ASCII: orth.is_ascii,
|
|
attrs.IS_DIGIT: lambda string: string.isdigit(),
|
|
attrs.IS_LOWER: orth.is_lower,
|
|
attrs.IS_PUNCT: orth.is_punct,
|
|
attrs.IS_SPACE: lambda string: string.isspace(),
|
|
attrs.IS_TITLE: orth.is_title,
|
|
attrs.IS_UPPER: orth.is_upper,
|
|
attrs.IS_BRACKET: orth.is_bracket,
|
|
attrs.IS_QUOTE: orth.is_quote,
|
|
attrs.IS_LEFT_PUNCT: orth.is_left_punct,
|
|
attrs.IS_RIGHT_PUNCT: orth.is_right_punct,
|
|
attrs.LIKE_URL: orth.like_url,
|
|
attrs.LIKE_NUM: orth.like_number,
|
|
attrs.LIKE_EMAIL: orth.like_email,
|
|
attrs.IS_STOP: lambda string: False,
|
|
attrs.IS_OOV: lambda string: True
|
|
}
|
|
|
|
|
|
|
|
class Language(object):
|
|
'''A text-processing pipeline. Usually you'll load this once per process, and
|
|
pass the instance around your program.
|
|
'''
|
|
Defaults = BaseDefaults
|
|
lang = None
|
|
|
|
def __init__(self,
|
|
path=None,
|
|
vocab=True,
|
|
tokenizer=True,
|
|
tagger=True,
|
|
parser=True,
|
|
entity=True,
|
|
matcher=True,
|
|
serializer=True,
|
|
vectors=True,
|
|
pipeline=True,
|
|
defaults=True,
|
|
data_dir=None):
|
|
"""
|
|
A model can be specified:
|
|
|
|
1) by calling a Language subclass
|
|
- spacy.en.English()
|
|
|
|
2) by calling a Language subclass with data_dir
|
|
- spacy.en.English('my/model/root')
|
|
- spacy.en.English(data_dir='my/model/root')
|
|
|
|
3) by package name
|
|
- spacy.load('en_default')
|
|
- spacy.load('en_default==1.0.0')
|
|
|
|
4) by package name with a relocated package base
|
|
- spacy.load('en_default', via='/my/package/root')
|
|
- spacy.load('en_default==1.0.0', via='/my/package/root')
|
|
"""
|
|
if data_dir is not None and path is None:
|
|
warn("'data_dir' argument now named 'path'. Doing what you mean.")
|
|
path = data_dir
|
|
if isinstance(path, basestring):
|
|
path = pathlib.Path(path)
|
|
if path is None:
|
|
path = util.match_best_version(self.lang, '', util.get_data_path())
|
|
self.path = path
|
|
defaults = defaults if defaults is not True else self.get_defaults(self.path)
|
|
|
|
self.vocab = vocab if vocab is not True else defaults.Vocab(vectors=vectors)
|
|
self.tokenizer = tokenizer if tokenizer is not True else defaults.Tokenizer(self.vocab)
|
|
self.tagger = tagger if tagger is not True else defaults.Tagger(self.vocab)
|
|
self.entity = entity if entity is not True else defaults.Entity(self.vocab)
|
|
self.parser = parser if parser is not True else defaults.Parser(self.vocab)
|
|
self.matcher = matcher if matcher is not True else defaults.Matcher(self.vocab)
|
|
self.pipeline = self.pipeline if pipeline is not True else defaults.Pipeline(self)
|
|
|
|
def __reduce__(self):
|
|
args = (
|
|
self.path,
|
|
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):
|
|
"""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')
|
|
"""
|
|
doc = self.pipeline[0](text)
|
|
if self.entity and entity:
|
|
# Add any of the entity labels already set, in case we don't have them.
|
|
for token in doc:
|
|
if token.ent_type != 0:
|
|
self.entity.add_label(token.ent_type)
|
|
skip = {self.tagger: not tag, self.parser: not parse, self.entity: not entity}
|
|
for proc in self.pipeline[1:]:
|
|
if proc and not skip.get(proc):
|
|
proc(doc)
|
|
return doc
|
|
|
|
def pipe(self, texts, tag=True, parse=True, entity=True, n_threads=2,
|
|
batch_size=1000):
|
|
skip = {self.tagger: not tag, self.parser: not parse, self.entity: not entity}
|
|
stream = self.pipeline[0].pipe(texts,
|
|
n_threads=n_threads, batch_size=batch_size)
|
|
for proc in self.pipeline[1:]:
|
|
if proc and not skip.get(proc):
|
|
if hasattr(proc, 'pipe'):
|
|
stream = proc.pipe(stream, n_threads=n_threads, batch_size=batch_size)
|
|
else:
|
|
stream = (proc(item) for item in stream)
|
|
for doc in stream:
|
|
yield doc
|
|
|
|
def end_training(self, path=None):
|
|
if path is None:
|
|
path = self.path
|
|
if self.parser:
|
|
self.parser.model.end_training()
|
|
self.parser.model.dump(path / 'deps' / 'model')
|
|
if self.entity:
|
|
self.entity.model.end_training()
|
|
self.entity.model.dump(path / 'ner' / 'model')
|
|
if self.tagger:
|
|
self.tagger.model.end_training()
|
|
self.tagger.model.dump(path / 'pos' / 'model')
|
|
|
|
strings_loc = path / 'vocab' / 'strings.json'
|
|
with strings_loc.open('w', encoding='utf8') as file_:
|
|
self.vocab.strings.dump(file_)
|
|
self.vocab.dump(path / 'vocab' / 'lexemes.bin')
|
|
|
|
if self.tagger:
|
|
tagger_freqs = list(self.tagger.freqs[TAG].items())
|
|
else:
|
|
tagger_freqs = []
|
|
if self.parser:
|
|
dep_freqs = list(self.parser.moves.freqs[DEP].items())
|
|
head_freqs = list(self.parser.moves.freqs[HEAD].items())
|
|
else:
|
|
dep_freqs = []
|
|
head_freqs = []
|
|
if self.entity:
|
|
entity_iob_freqs = list(self.entity.moves.freqs[ENT_IOB].items())
|
|
entity_type_freqs = list(self.entity.moves.freqs[ENT_TYPE].items())
|
|
else:
|
|
entity_iob_freqs = []
|
|
entity_type_freqs = []
|
|
with (path / 'vocab' / 'serializer.json').open('w') as file_:
|
|
file_.write(
|
|
json.dumps([
|
|
(TAG, tagger_freqs),
|
|
(DEP, dep_freqs),
|
|
(ENT_IOB, entity_iob_freqs),
|
|
(ENT_TYPE, entity_type_freqs),
|
|
(HEAD, head_freqs)
|
|
]))
|
|
|
|
|
|
def get_defaults(self, path):
|
|
return self.Defaults(self.lang, path)
|