spaCy/spacy/language.py

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()))]))