spaCy/spacy/language.py

# 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)