# coding: utf8 from __future__ import absolute_import, unicode_literals from contextlib import contextmanager import shutil import ujson try: basestring except NameError: basestring = str try: unicode except NameError: unicode = str from .tokenizer import Tokenizer from .vocab import Vocab from .tagger import Tagger from .matcher import Matcher from .lemmatizer import Lemmatizer from .train import Trainer from .syntax.parser import get_templates from .syntax.nonproj import PseudoProjectivity from .pipeline import DependencyParser, EntityRecognizer from .syntax.arc_eager import ArcEager from .syntax.ner import BiluoPushDown from .attrs import IS_STOP from . import attrs from . import orth from . import util from . import language_data class BaseDefaults(object): @classmethod def create_lemmatizer(cls, nlp=None): return Lemmatizer(cls.lemma_index, cls.lemma_exc, cls.lemma_rules) @classmethod def create_vocab(cls, nlp=None): lemmatizer = cls.create_lemmatizer(nlp) if nlp is None or nlp.path is None: lex_attr_getters = dict(cls.lex_attr_getters) # This is very messy, but it's the minimal working fix to Issue #639. # This defaults stuff needs to be refactored (again) lex_attr_getters[IS_STOP] = lambda string: string.lower() in cls.stop_words vocab = Vocab(lex_attr_getters=lex_attr_getters, tag_map=cls.tag_map, lemmatizer=lemmatizer) else: vocab = Vocab.load(nlp.path, lex_attr_getters=cls.lex_attr_getters, tag_map=cls.tag_map, lemmatizer=lemmatizer) for tag_str, exc in cls.morph_rules.items(): for orth_str, attrs in exc.items(): vocab.morphology.add_special_case(tag_str, orth_str, attrs) return vocab @classmethod def add_vectors(cls, nlp=None): if nlp is None or nlp.path is None: return False else: vec_path = nlp.path / 'vocab' / 'vec.bin' if vec_path.exists(): return lambda vocab: vocab.load_vectors_from_bin_loc(vec_path) @classmethod def create_tokenizer(cls, nlp=None): rules = cls.tokenizer_exceptions if cls.token_match: token_match = cls.token_match if cls.prefixes: prefix_search = util.compile_prefix_regex(cls.prefixes).search else: prefix_search = None if cls.suffixes: suffix_search = util.compile_suffix_regex(cls.suffixes).search else: suffix_search = None if cls.infixes: infix_finditer = util.compile_infix_regex(cls.infixes).finditer else: infix_finditer = None vocab = nlp.vocab if nlp is not None else cls.create_vocab(nlp) return Tokenizer(vocab, rules=rules, prefix_search=prefix_search, suffix_search=suffix_search, infix_finditer=infix_finditer, token_match=token_match) @classmethod def create_tagger(cls, nlp=None): if nlp is None: return Tagger(cls.create_vocab(), features=cls.tagger_features) elif nlp.path is False: return Tagger(nlp.vocab, features=cls.tagger_features) elif nlp.path is None or not (nlp.path / 'pos').exists(): return None else: return Tagger.load(nlp.path / 'pos', nlp.vocab) @classmethod def create_parser(cls, nlp=None, **cfg): if nlp is None: return DependencyParser(cls.create_vocab(), features=cls.parser_features, **cfg) elif nlp.path is False: return DependencyParser(nlp.vocab, features=cls.parser_features, **cfg) elif nlp.path is None or not (nlp.path / 'deps').exists(): return None else: return DependencyParser.load(nlp.path / 'deps', nlp.vocab, **cfg) @classmethod def create_entity(cls, nlp=None, **cfg): if nlp is None: return EntityRecognizer(cls.create_vocab(), features=cls.entity_features, **cfg) elif nlp.path is False: return EntityRecognizer(nlp.vocab, features=cls.entity_features, **cfg) elif nlp.path is None or not (nlp.path / 'ner').exists(): return None else: return EntityRecognizer.load(nlp.path / 'ner', nlp.vocab, **cfg) @classmethod def create_matcher(cls, nlp=None): if nlp is None: return Matcher(cls.create_vocab()) elif nlp.path is False: return Matcher(nlp.vocab) elif nlp.path is None or not (nlp.path / 'vocab').exists(): return None else: return Matcher.load(nlp.path / 'vocab', nlp.vocab) @classmethod def create_pipeline(self, nlp=None): pipeline = [] if nlp is None: return [] if nlp.tagger: pipeline.append(nlp.tagger) if nlp.parser: pipeline.append(nlp.parser) pipeline.append(PseudoProjectivity.deprojectivize) if nlp.entity: pipeline.append(nlp.entity) return pipeline token_match = language_data.TOKEN_MATCH prefixes = tuple(language_data.TOKENIZER_PREFIXES) suffixes = tuple(language_data.TOKENIZER_SUFFIXES) infixes = tuple(language_data.TOKENIZER_INFIXES) tag_map = dict(language_data.TAG_MAP) tokenizer_exceptions = {} parser_features = get_templates('parser') entity_features = get_templates('ner') tagger_features = Tagger.feature_templates # TODO -- fix this stop_words = set() lemma_rules = {} lemma_exc = {} lemma_index = {} morph_rules = {} 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 @classmethod def setup_directory(cls, path, **configs): for name, config in configs.items(): directory = path / name if directory.exists(): shutil.rmtree(str(directory)) directory.mkdir() with (directory / 'config.json').open('wb') as file_: data = ujson.dumps(config, indent=2) if isinstance(data, unicode): data = data.encode('utf8') file_.write(data) if not (path / 'vocab').exists(): (path / 'vocab').mkdir() @classmethod @contextmanager def train(cls, path, gold_tuples, **configs): if parser_cfg['pseudoprojective']: # preprocess training data here before ArcEager.get_labels() is called gold_tuples = PseudoProjectivity.preprocess_training_data(gold_tuples) for subdir in ('deps', 'ner', 'pos'): if subdir not in configs: configs[subdir] = {} configs['deps']['actions'] = ArcEager.get_actions(gold_parses=gold_tuples) configs['ner']['actions'] = BiluoPushDown.get_actions(gold_parses=gold_tuples) cls.setup_directory(path, **configs) self = cls( path=path, vocab=False, tokenizer=False, tagger=False, parser=False, entity=False, matcher=False, serializer=False, vectors=False, pipeline=False) self.vocab = self.Defaults.create_vocab(self) self.tokenizer = self.Defaults.create_tokenizer(self) self.tagger = self.Defaults.create_tagger(self) self.parser = self.Defaults.create_parser(self) self.entity = self.Defaults.create_entity(self) self.pipeline = self.Defaults.create_pipeline(self) yield Trainer(self, gold_tuples) self.end_training() self.save_to_directory(path, deps=self.parser.cfg, ner=self.entity.cfg, pos=self.tagger.cfg) def __init__(self, **overrides): if 'data_dir' in overrides and 'path' not in overrides: raise ValueError("The argument 'data_dir' has been renamed to 'path'") path = overrides.get('path', True) if isinstance(path, basestring): path = pathlib.Path(path) if path is True: path = util.get_data_path() / self.lang if not path.exists() and 'path' not in overrides: path = None self.meta = overrides.get('meta', {}) self.path = path self.vocab = self.Defaults.create_vocab(self) \ if 'vocab' not in overrides \ else overrides['vocab'] add_vectors = self.Defaults.add_vectors(self) \ if 'add_vectors' not in overrides \ else overrides['add_vectors'] if self.vocab and add_vectors: add_vectors(self.vocab) self.tokenizer = self.Defaults.create_tokenizer(self) \ if 'tokenizer' not in overrides \ else overrides['tokenizer'] self.tagger = self.Defaults.create_tagger(self) \ if 'tagger' not in overrides \ else overrides['tagger'] self.parser = self.Defaults.create_parser(self) \ if 'parser' not in overrides \ else overrides['parser'] self.entity = self.Defaults.create_entity(self) \ if 'entity' not in overrides \ else overrides['entity'] self.matcher = self.Defaults.create_matcher(self) \ if 'matcher' not in overrides \ else overrides['matcher'] if 'make_doc' in overrides: self.make_doc = overrides['make_doc'] elif 'create_make_doc' in overrides: self.make_doc = overrides['create_make_doc'](self) elif not hasattr(self, 'make_doc'): self.make_doc = lambda text: self.tokenizer(text) if 'pipeline' in overrides: self.pipeline = overrides['pipeline'] elif 'create_pipeline' in overrides: self.pipeline = overrides['create_pipeline'](self) else: self.pipeline = [self.tagger, self.parser, self.matcher, self.entity] 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: doc (Doc): A container for accessing the annotations. Example: >>> 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.make_doc(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: 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): """ Process texts as a stream, and yield Doc objects in order. Supports GIL-free multi-threading. Arguments: texts (iterator) tag (bool) parse (bool) entity (bool) """ skip = {self.tagger: not tag, self.parser: not parse, self.entity: not entity} stream = (self.make_doc(text) for text in texts) for proc in self.pipeline: 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 save_to_directory(self, path): configs = { 'pos': self.tagger.cfg if self.tagger else {}, 'deps': self.parser.cfg if self.parser else {}, 'ner': self.entity.cfg if self.entity else {}, } self.setup_directory(path, **configs) 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') # TODO: Word vectors? if self.tagger: self.tagger.model.dump(str(path / 'pos' / 'model')) if self.parser: self.parser.model.dump(str(path / 'deps' / 'model')) if self.entity: self.entity.model.dump(str(path / 'ner' / 'model')) def end_training(self, path=None): if self.tagger: self.tagger.model.end_training() if self.parser: self.parser.model.end_training() if self.entity: self.entity.model.end_training() # NB: This is slightly different from before --- we no longer default # to taking nlp.path if path is not None: self.save_to_directory(path)