spaCy/spacy/language.py
2017-03-09 18:42:40 -06:00

418 lines
15 KiB
Python

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)