from __future__ import absolute_import from __future__ import unicode_literals from warnings import warn import pathlib 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 . import attrs from . import orth from . import util from . import language_data from .lemmatizer import Lemmatizer from .train import Trainer from .attrs import TAG, DEP, ENT_IOB, ENT_TYPE, HEAD, PROB, LANG, IS_STOP 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 class BaseDefaults(object): @classmethod def create_lemmatizer(cls, nlp=None): if nlp is None or nlp.path is None: return Lemmatizer({}, {}, {}) else: return Lemmatizer.load(nlp.path, rules=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 return Vocab(lex_attr_getters=lex_attr_getters, tag_map=cls.tag_map, lemmatizer=lemmatizer) else: return Vocab.load(nlp.path, lex_attr_getters=cls.lex_attr_getters, tag_map=cls.tag_map, lemmatizer=lemmatizer) @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) 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 = {} 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 @contextmanager def train(cls, path, gold_tuples, *configs): if isinstance(path, basestring): path = pathlib.Path(path) tagger_cfg, parser_cfg, entity_cfg = configs dep_model_dir = path / 'deps' ner_model_dir = path / 'ner' pos_model_dir = path / 'pos' if dep_model_dir.exists(): shutil.rmtree(str(dep_model_dir)) if ner_model_dir.exists(): shutil.rmtree(str(ner_model_dir)) if pos_model_dir.exists(): shutil.rmtree(str(pos_model_dir)) dep_model_dir.mkdir() ner_model_dir.mkdir() pos_model_dir.mkdir() if parser_cfg['pseudoprojective']: # preprocess training data here before ArcEager.get_labels() is called gold_tuples = PseudoProjectivity.preprocess_training_data(gold_tuples) parser_cfg['actions'] = ArcEager.get_actions(gold_parses=gold_tuples) entity_cfg['actions'] = BiluoPushDown.get_actions(gold_parses=gold_tuples) with (dep_model_dir / 'config.json').open('wb') as file_: data = ujson.dumps(parser_cfg) if isinstance(data, unicode): data = data.encode('utf8') file_.write(data) with (ner_model_dir / 'config.json').open('wb') as file_: data = ujson.dumps(entity_cfg) if isinstance(data, unicode): data = data.encode('utf8') file_.write(data) with (pos_model_dir / 'config.json').open('wb') as file_: data = ujson.dumps(tagger_cfg) if isinstance(data, unicode): data = data.encode('utf8') file_.write(data) 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(path=path) 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.match_best_version(self.lang, '', util.get_data_path()) 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 end_training(self, path=None): if path is None: path = self.path elif isinstance(path, basestring): path = pathlib.Path(path) if self.tagger: self.tagger.model.end_training() self.tagger.model.dump(str(path / 'pos' / 'model')) if self.parser: self.parser.model.end_training() self.parser.model.dump(str(path / 'deps' / 'model')) if self.entity: self.entity.model.end_training() self.entity.model.dump(str(path / 'ner' / '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('wb') as file_: data = ujson.dumps([ (TAG, tagger_freqs), (DEP, dep_freqs), (ENT_IOB, entity_iob_freqs), (ENT_TYPE, entity_type_freqs), (HEAD, head_freqs) ]) if isinstance(data, unicode): data = data.encode('utf8') file_.write(data)