mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
406 lines
14 KiB
Python
406 lines
14 KiB
Python
from __future__ import absolute_import
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from __future__ import unicode_literals
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from warnings import warn
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import pathlib
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from contextlib import contextmanager
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import shutil
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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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try:
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basestring
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except NameError:
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basestring = str
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from .tokenizer import Tokenizer
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from .vocab import Vocab
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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 . import util
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from .lemmatizer import Lemmatizer
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from .train import Trainer
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from .attrs import TAG, DEP, ENT_IOB, ENT_TYPE, HEAD, PROB, LANG, IS_STOP
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from .syntax.parser import get_templates
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from .syntax.nonproj import PseudoProjectivity
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from .pipeline import DependencyParser, EntityRecognizer
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class BaseDefaults(object):
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def __init__(self, lang, path):
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self.path = path
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self.lang = lang
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self.lex_attr_getters = dict(self.__class__.lex_attr_getters)
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if self.path and (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: lang
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self.lex_attr_getters[IS_STOP] = lambda string: string in self.stop_words
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def Lemmatizer(self):
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return Lemmatizer.load(self.path) if self.path else Lemmatizer({}, {}, {})
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def Vectors(self):
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return True
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def Vocab(self, lex_attr_getters=True, tag_map=True,
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lemmatizer=True, serializer_freqs=True, vectors=True):
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if lex_attr_getters is True:
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lex_attr_getters = self.lex_attr_getters
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if tag_map is True:
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tag_map = self.tag_map
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if lemmatizer is True:
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lemmatizer = self.Lemmatizer()
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if vectors is True:
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vectors = self.Vectors()
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if self.path:
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return Vocab.load(self.path, lex_attr_getters=lex_attr_getters,
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tag_map=tag_map, lemmatizer=lemmatizer,
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serializer_freqs=serializer_freqs)
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else:
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return Vocab(lex_attr_getters=lex_attr_getters, tag_map=tag_map,
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lemmatizer=lemmatizer, serializer_freqs=serializer_freqs)
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def Tokenizer(self, vocab, rules=None, prefix_search=None, suffix_search=None,
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infix_finditer=None):
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if rules is None:
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rules = self.tokenizer_exceptions
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if prefix_search is None:
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prefix_search = util.compile_prefix_regex(self.prefixes).search
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if suffix_search is None:
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suffix_search = util.compile_suffix_regex(self.suffixes).search
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if infix_finditer is None:
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infix_finditer = util.compile_infix_regex(self.infixes).finditer
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if self.path:
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return Tokenizer.load(self.path, vocab, rules=rules,
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prefix_search=prefix_search,
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suffix_search=suffix_search,
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infix_finditer=infix_finditer)
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else:
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tokenizer = Tokenizer(vocab, rules=rules,
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prefix_search=prefix_search, suffix_search=suffix_search,
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infix_finditer=infix_finditer)
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return tokenizer
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def Tagger(self, vocab, **cfg):
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if self.path:
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return Tagger.load(self.path / 'pos', vocab)
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else:
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if 'features' not in cfg:
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cfg['features'] = self.parser_features
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return Tagger(vocab, **cfg)
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def Parser(self, vocab, **cfg):
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if self.path and (self.path / 'deps').exists():
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return DependencyParser.load(self.path / 'deps', vocab)
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else:
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if 'features' not in cfg:
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cfg['features'] = self.parser_features
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return DependencyParser(vocab, **cfg)
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def Entity(self, vocab, **cfg):
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if self.path and (self.path / 'ner').exists():
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return EntityRecognizer.load(self.path / 'ner', vocab)
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else:
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if 'features' not in cfg:
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cfg['features'] = self.entity_features
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return EntityRecognizer(vocab, **cfg)
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def Matcher(self, vocab, **cfg):
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if self.path:
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return Matcher.load(self.path, vocab)
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else:
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return Matcher(vocab)
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def MakeDoc(self, nlp, **cfg):
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return lambda text: nlp.tokenizer(text)
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def Pipeline(self, nlp, **cfg):
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pipeline = []
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if nlp.tagger:
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pipeline.append(nlp.tagger)
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if nlp.parser:
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pipeline.append(nlp.parser)
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if nlp.entity:
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pipeline.append(nlp.entity)
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return pipeline
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prefixes = tuple()
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suffixes = tuple()
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infixes = tuple()
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tag_map = {}
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tokenizer_exceptions = {}
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parser_features = get_templates('parser')
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entity_features = get_templates('ner')
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stop_words = set()
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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 = BaseDefaults
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lang = None
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@classmethod
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@contextmanager
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def train(cls, path, gold_tuples, *configs):
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if isinstance(path, basestring):
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path = pathlib.Path(path)
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tagger_cfg, parser_cfg, entity_cfg = configs
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dep_model_dir = path / 'deps'
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ner_model_dir = path / 'ner'
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pos_model_dir = path / 'pos'
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if dep_model_dir.exists():
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shutil.rmtree(str(dep_model_dir))
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if ner_model_dir.exists():
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shutil.rmtree(str(ner_model_dir))
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if pos_model_dir.exists():
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shutil.rmtree(str(pos_model_dir))
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dep_model_dir.mkdir()
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ner_model_dir.mkdir()
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pos_model_dir.mkdir()
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if parser_cfg['pseudoprojective']:
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# preprocess training data here before ArcEager.get_labels() is called
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gold_tuples = PseudoProjectivity.preprocess_training_data(gold_tuples)
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parser_cfg['labels'] = ArcEager.get_labels(gold_tuples)
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entity_cfg['labels'] = BiluoPushDown.get_labels(gold_tuples)
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with (dep_model_dir / 'config.json').open('wb') as file_:
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json.dump(parser_cfg, file_)
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with (ner_model_dir / 'config.json').open('wb') as file_:
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json.dump(entity_cfg, file_)
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with (pos_model_dir / 'config.json').open('wb') as file_:
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json.dump(tagger_cfg, file_)
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self = cls(
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path=path,
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vocab=False,
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tokenizer=False,
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tagger=False,
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parser=False,
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entity=False,
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matcher=False,
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serializer=False,
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vectors=False,
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pipeline=False)
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self.defaults.parser_labels = parser_cfg['labels']
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self.defaults.entity_labels = entity_cfg['labels']
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self.vocab = self.defaults.Vocab()
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self.tokenizer = self.defaults.Tokenizer(self.vocab)
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self.tagger = self.defaults.Tagger(self.vocab, **tagger_cfg)
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self.parser = self.defaults.Parser(self.vocab, **parser_cfg)
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self.entity = self.defaults.Entity(self.vocab, **entity_cfg)
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self.pipeline = self.defaults.Pipeline(self)
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yield Trainer(self, gold_tuples)
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self.end_training()
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def __init__(self,
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path=True,
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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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make_doc=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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if path is True:
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path = util.match_best_version(self.lang, '', util.get_data_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.defaults = defaults
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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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if make_doc in (None, True, False):
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self.make_doc = defaults.MakeDoc(self)
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else:
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self.make_doc = make_doc
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if pipeline in (None, False):
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self.pipeline = []
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elif pipeline is True:
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self.pipeline = defaults.Pipeline(self)
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else:
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self.pipeline = 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.make_doc(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:
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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, 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.make_doc(text) for text in texts)
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for proc in self.pipeline:
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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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elif isinstance(path, basestring):
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path = pathlib.Path(path)
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if self.tagger:
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self.tagger.model.end_training()
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self.tagger.model.dump(str(path / 'pos' / 'model'))
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if self.parser:
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self.parser.model.end_training()
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self.parser.model.dump(str(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(str(path / 'ner' / '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('wb') 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 self.Defaults(self.lang, path)
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