from __future__ import absolute_import from os import path from warnings import warn import io try: import ujson as json except ImportError: import json from .tokenizer import Tokenizer from .vocab import Vocab from .syntax.parser import Parser from .tagger import Tagger from .matcher import Matcher from .serialize.packer import Packer from . import attrs from . import orth from .syntax.ner import BiluoPushDown from .syntax.arc_eager import ArcEager from . import util from . import about from .attrs import TAG, DEP, ENT_IOB, ENT_TYPE, HEAD class Language(object): lang = None @staticmethod def lower(string): return string.lower() @staticmethod def norm(string): return string @staticmethod def prefix(string): return string[0] @staticmethod def suffix(string): return string[-3:] @staticmethod def cluster(string): return 0 @staticmethod def is_digit(string): return string.isdigit() @staticmethod def is_space(string): return string.isspace() @staticmethod def is_stop(string): return 0 @classmethod def default_lex_attrs(cls, *args, **kwargs): oov_prob = kwargs.get('oov_prob', -20) return { attrs.LOWER: cls.lower, attrs.NORM: cls.norm, attrs.SHAPE: orth.word_shape, attrs.PREFIX: cls.prefix, attrs.SUFFIX: cls.suffix, attrs.CLUSTER: cls.cluster, attrs.PROB: lambda string: oov_prob, attrs.LANG: lambda string: cls.lang, attrs.IS_ALPHA: orth.is_alpha, attrs.IS_ASCII: orth.is_ascii, attrs.IS_DIGIT: cls.is_digit, attrs.IS_LOWER: orth.is_lower, attrs.IS_PUNCT: orth.is_punct, attrs.IS_SPACE: cls.is_space, 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: cls.is_stop, attrs.IS_OOV: lambda string: True } @classmethod def default_dep_labels(cls): return {0: {'ROOT': True}} @classmethod def default_ner_labels(cls): return {0: {'PER': True, 'LOC': True, 'ORG': True, 'MISC': True}} @classmethod def default_vocab(cls, package, get_lex_attr=None, vectors_package=None): if get_lex_attr is None: if package.has_file('vocab', 'oov_prob'): with package.open(('vocab', 'oov_prob')) as file_: oov_prob = float(file_.read().strip()) get_lex_attr = cls.default_lex_attrs(oov_prob=oov_prob) else: get_lex_attr = cls.default_lex_attrs() if hasattr(package, 'dir_path'): return Vocab.from_package(package, get_lex_attr=get_lex_attr, vectors_package=vectors_package) else: return Vocab.load(package, get_lex_attr) @classmethod def default_parser(cls, package, vocab): if hasattr(package, 'dir_path'): data_dir = package.dir_path('deps') else: data_dir = package if data_dir and path.exists(data_dir): return Parser.from_dir(data_dir, vocab.strings, ArcEager) else: return None @classmethod def default_entity(cls, package, vocab): if hasattr(package, 'dir_path'): data_dir = package.dir_path('ner') else: data_dir = package if data_dir and path.exists(data_dir): return Parser.from_dir(data_dir, vocab.strings, BiluoPushDown) else: return None @classmethod def default_tokenizer(cls, package, vocab): return Tokenizer.from_package(package, vocab) def __init__(self, data_dir=None, vocab=None, tokenizer=None, tagger=None, parser=None, entity=None, matcher=None, serializer=None, load_vectors=True, package=None, vectors_package=None): """ A model can be specified: 1) by calling a Language subclass - spacy.en.English() 2) by calling a Language subclass with data_dir - spacy.en.English('my/model/root') - spacy.en.English(data_dir='my/model/root') 3) by package name - spacy.load('en_default') - spacy.load('en_default==1.0.0') 4) by package name with a relocated package base - spacy.load('en_default', via='/my/package/root') - spacy.load('en_default==1.0.0', via='/my/package/root') """ if package is None: if data_dir is None: package = util.get_package_by_name(about.__models__[self.lang]) else: package = util.get_package(data_dir) if load_vectors is not True: warn("load_vectors is deprecated", DeprecationWarning) if vocab in (None, True): vocab = self.default_vocab(package, vectors_package=vectors_package) self.vocab = vocab if tokenizer in (None, True): tokenizer = self.default_tokenizer(package, self.vocab) self.tokenizer = tokenizer if tagger in (None, True): tagger = Tagger.from_package(package, self.vocab) self.tagger = tagger if entity in (None, True): entity = self.default_entity(package, self.vocab) self.entity = entity if parser in (None, True): parser = self.default_parser(package, self.vocab) self.parser = parser if matcher in (None, True): matcher = Matcher.from_package(package, self.vocab) self.matcher = matcher def __reduce__(self): args = ( None, # data_dir self.vocab, self.tokenizer, self.tagger, self.parser, self.entity, self.matcher ) return (self.__class__, args, None, None) 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: tokens (spacy.tokens.Doc): >>> from spacy.en import English >>> nlp = English() >>> tokens = nlp('An example sentence. Another example sentence.') >>> tokens[0].orth_, tokens[0].head.tag_ ('An', 'NN') """ tokens = self.tokenizer(text) if self.tagger and tag: self.tagger(tokens) if self.matcher and entity: self.matcher(tokens) if self.parser and parse: self.parser(tokens) if self.entity and entity: # Add any of the entity labels already set, in case we don't have them. for tok in tokens: if tok.ent_type != 0: self.entity.add_label(tok.ent_type) self.entity(tokens) return tokens def pipe(self, texts, tag=True, parse=True, entity=True, n_threads=2, batch_size=1000): stream = self.tokenizer.pipe(texts, n_threads=n_threads, batch_size=batch_size) if self.tagger and tag: stream = self.tagger.pipe(stream, n_threads=n_threads, batch_size=batch_size) if self.matcher and entity: stream = self.matcher.pipe(stream, n_threads=n_threads, batch_size=batch_size) if self.parser and parse: stream = self.parser.pipe(stream, n_threads=n_threads, batch_size=batch_size) if self.entity and entity: stream = self.entity.pipe(stream, n_threads=1, batch_size=batch_size) for doc in stream: yield doc def end_training(self, data_dir=None): if data_dir is None: data_dir = self.data_dir if self.parser: self.parser.model.end_training() self.parser.model.dump(path.join(data_dir, 'deps', 'model')) if self.entity: self.entity.model.end_training() self.entity.model.dump(path.join(data_dir, 'ner', 'model')) if self.tagger: self.tagger.model.end_training() self.tagger.model.dump(path.join(data_dir, 'pos', 'model')) strings_loc = path.join(data_dir, 'vocab', 'strings.json') with io.open(strings_loc, 'w', encoding='utf8') as file_: self.vocab.strings.dump(file_) self.vocab.dump(path.join(data_dir, '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 open(path.join(data_dir, 'vocab', 'serializer.json'), 'w') as file_: file_.write( json.dumps([ (TAG, tagger_freqs), (DEP, dep_freqs), (ENT_IOB, entity_iob_freqs), (ENT_TYPE, entity_type_freqs), (HEAD, head_freqs) ]))