mirror of https://github.com/explosion/spaCy.git
258 lines
7.6 KiB
Python
258 lines
7.6 KiB
Python
from os import path
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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 .morphology import Morphology
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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 ._ml import Model
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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 Language(object):
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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_number(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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@classmethod
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def default_lex_attrs(cls, data_dir=None):
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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_number,
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attrs.LIKE_EMAIL: cls.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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@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_data_dir(cls):
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return path.join(path.dirname(__file__), 'data')
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@classmethod
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def default_morphology(cls, data_dir):
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return Morphology.from_dir(data_dir)
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@classmethod
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def default_vectors(cls, data_dir):
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return None
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@classmethod
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def default_vocab(cls, data_dir=None, get_lex_attr=None, vectors=None, morphology=None):
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if data_dir is None:
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data_dir = cls.default_data_dir()
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if vectors is None:
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vectors = cls.default_vectors(data_dir)
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if get_lex_attr is None:
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get_lex_attr = cls.default_lex_attrs(data_dir)
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return Vocab.from_dir(
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path.join(data_dir, 'vocab'),
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get_lex_attr=get_lex_attr,
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vectors=vectors)
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@classmethod
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def default_tokenizer(cls, vocab, data_dir):
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if path.exists(data_dir):
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return Tokenizer.from_dir(vocab, data_dir)
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else:
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return Tokenizer(vocab, {}, None, None, None)
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@classmethod
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def default_tagger(cls, vocab, data_dir):
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if path.exists(data_dir):
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return Tagger.from_dir(data_dir, vocab)
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else:
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return None
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@classmethod
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def default_parser(cls, vocab, data_dir):
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if 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, vocab, data_dir):
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if 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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@classmethod
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def default_matcher(cls, vocab, data_dir):
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if path.exists(data_dir):
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return Matcher.from_dir(data_dir, vocab)
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else:
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return None
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def __init__(self, data_dir=None, vocab=None, tokenizer=None, tagger=None,
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parser=None, entity=None, matcher=None, serializer=None):
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if data_dir is None:
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data_dir = self.default_data_dir()
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if vocab is None:
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vocab = self.default_vocab(data_dir)
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if tokenizer is None:
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tokenizer = self.default_tokenizer(vocab, data_dir=path.join(data_dir, 'tokenizer'))
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if tagger is None:
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tagger = self.default_tagger(vocab, data_dir=path.join(data_dir, 'pos'))
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if entity is None:
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entity = self.default_entity(vocab, data_dir=path.join(data_dir, 'ner'))
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if parser is None:
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parser = self.default_parser(vocab, data_dir=path.join(data_dir, 'deps'))
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if matcher is None:
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matcher = self.default_matcher(vocab, data_dir=data_dir)
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self.vocab = vocab
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self.tokenizer = tokenizer
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self.tagger = tagger
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self.parser = parser
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self.entity = entity
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self.matcher = matcher
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def __call__(self, text, tag=True, parse=True, entity=True, merge_mwes=False):
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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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self.entity(tokens)
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return tokens
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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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self.parser.model.end_training(path.join(data_dir, 'deps', 'model'))
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self.entity.model.end_training(path.join(data_dir, 'ner', 'model'))
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self.tagger.model.end_training(path.join(data_dir, 'pos', 'model'))
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self.vocab.strings.dump(path.join(data_dir, 'vocab', 'strings.txt'))
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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, list(self.tagger.freqs[TAG].items())),
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(DEP, list(self.parser.moves.freqs[DEP].items())),
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(ENT_IOB, list(self.entity.moves.freqs[ENT_IOB].items())),
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(ENT_TYPE, list(self.entity.moves.freqs[ENT_TYPE].items())),
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(HEAD, list(self.parser.moves.freqs[HEAD].items()))]))
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