mirror of https://github.com/explosion/spaCy.git
260 lines
8.9 KiB
Python
260 lines
8.9 KiB
Python
from __future__ import absolute_import
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from warnings import warn
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import pathlib
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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 . 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 .attrs import TAG, DEP, ENT_IOB, ENT_TYPE, HEAD
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class Defaults(object):
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def __init__(self, lang, path):
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self.lang = lang
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self.path = path
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self.lex_attr_getters = dict(self.__class__.lex_attr_getters)
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if (self.path / 'vocab' / 'oov_prob').exists():
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with (self.path / 'vocab' / 'oov_prob').open() as file_:
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oov_prob = file_.read().strip()
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self.lex_attr_getters['PROB'] = lambda string: oov_prob
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self.lex_attr_getters['LANG'] = lambda string: self.lang,
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def Vectors(self):
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pass
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def Vocab(self, vectors=None, lex_attr_getters=None):
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if lex_attr_getters is None:
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lex_attr_getters = dict(self.lex_attr_getters)
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if vectors is None:
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vectors = self.Vectors()
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return Vocab.load(self.path, get_lex_attr=get_lex_attr, vectors=vectors)
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def Tokenizer(self, vocab):
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return Tokenizer.load(self.path, vocab)
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def Tagger(self, vocab):
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return Tagger.load(self.path, self.vocab)
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def Parser(self, vocab):
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if (self.path / 'deps').exists():
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return Parser.load(self.path / 'deps', vocab, ArcEager)
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else:
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return None
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def Entity(self, vocab):
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if (self.path / 'ner').exists():
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return Parser.load(self.path / 'ner', vocab, BiluoPushDown)
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else:
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return None
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def Matcher(self, vocab):
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return Matcher.load(self.path, vocab)
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def Pipeline(self, nlp):
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return [
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nlp.tokenizer,
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nlp.tagger,
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nlp.parser,
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nlp.entity]
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dep_labels = {0: {'ROOT': True}}
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ner_labels = {0: {'PER': True, 'LOC': True, 'ORG': True, 'MISC': True}}
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lex_attr_getters = {
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attrs.LOWER: lambda string: string.lower(),
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attrs.NORM: lambda string: string,
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attrs.SHAPE: orth.word_shape,
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attrs.PREFIX: lambda string: string[0],
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attrs.SUFFIX: lambda string: string[-3:],
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attrs.CLUSTER: lambda string: 0,
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attrs.IS_ALPHA: orth.is_alpha,
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attrs.IS_ASCII: orth.is_ascii,
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attrs.IS_DIGIT: lambda string: string.isdigit(),
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attrs.IS_LOWER: orth.is_lower,
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attrs.IS_PUNCT: orth.is_punct,
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attrs.IS_SPACE: lambda string: string.isspace(),
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attrs.IS_TITLE: orth.is_title,
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attrs.IS_UPPER: orth.is_upper,
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attrs.IS_BRACKET: orth.is_bracket,
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attrs.IS_QUOTE: orth.is_quote,
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attrs.IS_LEFT_PUNCT: orth.is_left_punct,
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attrs.IS_RIGHT_PUNCT: orth.is_right_punct,
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attrs.LIKE_URL: orth.like_url,
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attrs.LIKE_NUM: orth.like_number,
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attrs.LIKE_EMAIL: orth.like_email,
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attrs.IS_STOP: lambda string: False,
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attrs.IS_OOV: lambda string: True
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}
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class Language(object):
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'''A text-processing pipeline. Usually you'll load this once per process, and
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pass the instance around your program.
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'''
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Defaults = Defaults
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lang = None
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def __init__(self,
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path=None,
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vocab=True,
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tokenizer=True,
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tagger=True,
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parser=True,
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entity=True,
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matcher=True,
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serializer=True,
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vectors=True,
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pipeline=True,
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defaults=True,
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data_dir=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 data_dir is not None and path is None:
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warn("'data_dir' argument now named 'path'. Doing what you mean.")
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path = data_dir
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if isinstance(path, basestring):
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path = pathlib.Path(path)
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self.path = path
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defaults = defaults if defaults is not True else self.get_defaults(self.path)
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self.vocab = vocab if vocab is not True else defaults.Vocab(vectors=vectors)
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self.tokenizer = tokenizer if tokenizer is not True else defaults.Tokenizer(self.vocab)
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self.tagger = tagger if tagger is not True else defaults.Tagger(self.vocab)
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self.entity = entity if entity is not True else defaults.Entity(self.vocab)
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self.parser = parser if parser is not True else defaults.Parser(self.vocab)
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self.matcher = matcher if matcher is not True else defaults.Matcher(self.vocab)
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self.pipeline = self.pipeline if pipeline is not True else defaults.Pipeline(self)
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def __reduce__(self):
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args = (
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self.path,
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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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doc = self.pipeline[0](text)
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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 token in doc:
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if token.ent_type != 0:
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self.entity.add_label(token.ent_type)
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skip = {self.tagger: not tag, self.parser: not parse, self.entity: not entity}
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for proc in self.pipeline[1:]:
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if proc and not skip.get(proc):
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proc(doc)
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return doc
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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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skip = {self.tagger: not tag, self.parser: not parse, self.entity: not entity}
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stream = self.pipeline[0].pipe(texts,
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n_threads=n_threads, batch_size=batch_size)
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for proc in self.pipeline[1:]:
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if proc and not skip.get(proc):
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if hasattr(proc, 'pipe'):
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stream = proc.pipe(stream, n_threads=n_threads, batch_size=batch_size)
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else:
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stream = (proc(item) for item in stream)
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for doc in stream:
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yield doc
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def end_training(self, path=None):
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if path is None:
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path = self.path
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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 / '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 / '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 / 'pos' / 'model')
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strings_loc = path / 'vocab' / 'strings.json'
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with strings_loc.open('w', encoding='utf8') as file_:
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self.vocab.strings.dump(file_)
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self.vocab.dump(path / '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 (path / 'vocab' / 'serializer.json').open('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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def get_defaults(self, path):
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return Defaults(self.lang, path)
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