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 .attrs import TAG, DEP, ENT_IOB, ENT_TYPE, HEAD from .util import get_package class Language(object): lang = None @staticmethod def lower(string): return string.lower() @staticmethod def norm(string): return string @staticmethod def shape(string): return orth.word_shape(string) @staticmethod def prefix(string): return string[0] @staticmethod def suffix(string): return string[-3:] @staticmethod def prob(string): return -30 @staticmethod def cluster(string): return 0 @staticmethod def is_alpha(string): return orth.is_alpha(string) @staticmethod def is_ascii(string): return orth.is_ascii(string) @staticmethod def is_digit(string): return string.isdigit() @staticmethod def is_lower(string): return orth.is_lower(string) @staticmethod def is_punct(string): return orth.is_punct(string) @staticmethod def is_space(string): return string.isspace() @staticmethod def is_title(string): return orth.is_title(string) @staticmethod def is_upper(string): return orth.is_upper(string) @staticmethod def like_url(string): return orth.like_url(string) @staticmethod def like_num(string): return orth.like_number(string) @staticmethod def like_email(string): return orth.like_email(string) @staticmethod def is_stop(string): return 0 @classmethod def default_lex_attrs(cls): return { attrs.LOWER: cls.lower, attrs.NORM: cls.norm, attrs.SHAPE: cls.shape, attrs.PREFIX: cls.prefix, attrs.SUFFIX: cls.suffix, attrs.CLUSTER: cls.cluster, attrs.PROB: lambda string: -10.0, attrs.IS_ALPHA: cls.is_alpha, attrs.IS_ASCII: cls.is_ascii, attrs.IS_DIGIT: cls.is_digit, attrs.IS_LOWER: cls.is_lower, attrs.IS_PUNCT: cls.is_punct, attrs.IS_SPACE: cls.is_space, attrs.IS_TITLE: cls.is_title, attrs.IS_UPPER: cls.is_upper, attrs.LIKE_URL: cls.like_url, attrs.LIKE_NUM: cls.like_num, attrs.LIKE_EMAIL: cls.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=None, get_lex_attr=None): if package is None: package = get_package() if get_lex_attr is None: get_lex_attr = cls.default_lex_attrs() return Vocab.from_package(package, get_lex_attr=get_lex_attr) @classmethod def default_parser(cls, package, vocab): data_dir = package.dir_path('deps', require=False) if data_dir and path.exists(data_dir): return Parser.from_dir(data_dir, vocab.strings, ArcEager) @classmethod def default_entity(cls, package, vocab): data_dir = package.dir_path('ner', require=False) if data_dir and path.exists(data_dir): return Parser.from_dir(data_dir, vocab.strings, BiluoPushDown) def __init__(self, data_dir=None, model=None, vocab=None, tokenizer=None, tagger=None, parser=None, entity=None, matcher=None, serializer=None, load_vectors=True): """ a model can be specified: 1) by a path to the model directory (DEPRECATED) - Language(data_dir='path/to/data') 2) by a language identifier (and optionally a package root dir) - Language(lang='en') - Language(lang='en', data_dir='spacy/data') 3) by a model name/version (and optionally a package root dir) - Language(model='en_default') - Language(model='en_default ==1.0.0') - Language(model='en_default <1.1.0, data_dir='spacy/data') """ # support non-package data dirs if data_dir and path.exists(path.join(data_dir, 'vocab')): class Package(object): def __init__(self, root): self.root = root def has_file(self, *path_parts): return path.exists(path.join(self.root, *path_parts)) def file_path(self, *path_parts, **kwargs): return path.join(self.root, *path_parts) def dir_path(self, *path_parts, **kwargs): return path.join(self.root, *path_parts) def load_utf8(self, func, *path_parts, **kwargs): with io.open(self.file_path(path.join(*path_parts)), mode='r', encoding='utf8') as f: return func(f) warn("using non-package data_dir", DeprecationWarning) package = Package(data_dir) else: package = get_package(name=model, data_path=data_dir) if load_vectors is not True: warn("load_vectors is deprecated", DeprecationWarning) if vocab in (None, True): vocab = self.default_vocab(package) self.vocab = vocab if tokenizer in (None, True): tokenizer = Tokenizer.from_package(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 None, # model 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: self.entity(tokens) return tokens def end_training(self, data_dir=None): if data_dir is None: data_dir = self.data_dir self.parser.model.end_training() self.parser.model.dump(path.join(data_dir, 'deps', 'model')) self.entity.model.end_training() self.entity.model.dump(path.join(data_dir, 'ner', 'model')) 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_) with open(path.join(data_dir, 'vocab', 'serializer.json'), 'w') as file_: file_.write( json.dumps([ (TAG, list(self.tagger.freqs[TAG].items())), (DEP, list(self.parser.moves.freqs[DEP].items())), (ENT_IOB, list(self.entity.moves.freqs[ENT_IOB].items())), (ENT_TYPE, list(self.entity.moves.freqs[ENT_TYPE].items())), (HEAD, list(self.parser.moves.freqs[HEAD].items()))]))