spaCy/spacy/language.py

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from __future__ import absolute_import
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from __future__ import unicode_literals
from warnings import warn
import pathlib
from contextlib import contextmanager
import shutil
try:
import ujson as json
except ImportError:
import json
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try:
basestring
except NameError:
basestring = str
from .tokenizer import Tokenizer
from .vocab import Vocab
from .syntax.parser import Parser
from .tagger import Tagger
from .matcher import Matcher
from . import attrs
from . import orth
from .syntax.ner import BiluoPushDown
from .syntax.arc_eager import ArcEager
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from . import util
from .lemmatizer import Lemmatizer
from .train import Trainer
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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
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class BaseDefaults(object):
def __init__(self, lang, path):
self.path = path
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self.lang = lang
self.lex_attr_getters = dict(self.__class__.lex_attr_getters)
if self.path and (self.path / 'vocab' / 'oov_prob').exists():
with (self.path / 'vocab' / 'oov_prob').open() as file_:
oov_prob = file_.read().strip()
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self.lex_attr_getters[PROB] = lambda string: oov_prob
self.lex_attr_getters[LANG] = lambda string: lang
self.lex_attr_getters[IS_STOP] = lambda string: string in self.stop_words
def Lemmatizer(self):
return Lemmatizer.load(self.path) if self.path else Lemmatizer({}, {}, {})
def Vectors(self):
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return True
def Vocab(self, lex_attr_getters=True, tag_map=True,
lemmatizer=True, serializer_freqs=True, vectors=True):
if lex_attr_getters is True:
lex_attr_getters = self.lex_attr_getters
if tag_map is True:
tag_map = self.tag_map
if lemmatizer is True:
lemmatizer = self.Lemmatizer()
if vectors is True:
vectors = self.Vectors()
if self.path:
return Vocab.load(self.path, lex_attr_getters=lex_attr_getters,
tag_map=tag_map, lemmatizer=lemmatizer,
serializer_freqs=serializer_freqs)
else:
return Vocab(lex_attr_getters=lex_attr_getters, tag_map=tag_map,
lemmatizer=lemmatizer, serializer_freqs=serializer_freqs)
def Tokenizer(self, vocab, rules=None, prefix_search=None, suffix_search=None,
infix_finditer=None):
if rules is None:
rules = self.tokenizer_exceptions
if prefix_search is None:
prefix_search = util.compile_prefix_regex(self.prefixes).search
if suffix_search is None:
suffix_search = util.compile_suffix_regex(self.suffixes).search
if infix_finditer is None:
infix_finditer = util.compile_infix_regex(self.infixes).finditer
if self.path:
return Tokenizer.load(self.path, vocab, rules=rules,
prefix_search=prefix_search,
suffix_search=suffix_search,
infix_finditer=infix_finditer)
else:
tokenizer = Tokenizer(vocab, rules=rules,
prefix_search=prefix_search, suffix_search=suffix_search,
infix_finditer=infix_finditer)
return tokenizer
def Tagger(self, vocab, **cfg):
if self.path:
return Tagger.load(self.path / 'pos', vocab)
else:
return Tagger.blank(vocab, Tagger.default_templates())
def Parser(self, vocab, **cfg):
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if self.path and (self.path / 'deps').exists():
return Parser.load(self.path / 'deps', vocab, ArcEager)
else:
if 'features' not in cfg:
cfg['features'] = self.parser_features
if 'labels' not in cfg:
cfg['labels'] = self.parser_labels
return Parser.blank(vocab, ArcEager, **cfg)
def Entity(self, vocab, **cfg):
if self.path and (self.path / 'ner').exists():
return Parser.load(self.path / 'ner', vocab, BiluoPushDown)
else:
if 'features' not in cfg:
cfg['features'] = self.entity_features
if 'labels' not in cfg:
cfg['labels'] = self.entity_labels
return Parser.blank(vocab, BiluoPushDown, **cfg)
def Matcher(self, vocab, **cfg):
if self.path:
return Matcher.load(self.path, vocab)
else:
return Matcher(vocab)
def Pipeline(self, nlp, **cfg):
pipeline = [nlp.tokenizer]
if nlp.tagger:
pipeline.append(nlp.tagger)
if nlp.parser:
pipeline.append(nlp.parser)
if nlp.entity:
pipeline.append(nlp.entity)
return pipeline
prefixes = tuple()
suffixes = tuple()
infixes = tuple()
tag_map = {}
tokenizer_exceptions = {}
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parser_labels = {0: {'': True}, 1: {'': True}, 2: {'ROOT': True, 'nmod': True},
3: {'ROOT': True, 'nmod': True}, 4: {'ROOT': True}}
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entity_labels = {
0: {'': True},
1: {'PER': True, 'LOC': True, 'ORG': True, 'MISC': True},
2: {'PER': True, 'LOC': True, 'ORG': True, 'MISC': True},
3: {'PER': True, 'LOC': True, 'ORG': True, 'MISC': True},
4: {'PER': True, 'LOC': True, 'ORG': True, 'MISC': True},
5: {'': True}
}
parser_features = get_templates('parser')
entity_features = get_templates('ner')
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stop_words = set()
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
}
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class Language(object):
'''A text-processing pipeline. Usually you'll load this once per process, and
pass the instance around your program.
'''
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Defaults = BaseDefaults
lang = None
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@classmethod
@contextmanager
def train(cls, path, gold_tuples, *configs):
if isinstance(path, basestring):
path = pathlib.Path(path)
tagger_cfg, parser_cfg, entity_cfg = configs
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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['labels'] = ArcEager.get_labels(gold_tuples)
entity_cfg['labels'] = BiluoPushDown.get_labels(gold_tuples)
with (dep_model_dir / 'config.json').open('wb') as file_:
json.dump(parser_cfg, file_)
with (ner_model_dir / 'config.json').open('wb') as file_:
json.dump(entity_cfg, file_)
with (pos_model_dir / 'config.json').open('wb') as file_:
json.dump(tagger_cfg, file_)
self = cls(
path=path,
vocab=False,
tokenizer=False,
tagger=False,
parser=False,
entity=False,
matcher=False,
serializer=False,
vectors=False,
pipeline=False)
self.defaults.parser_labels = parser_cfg['labels']
self.defaults.entity_labels = entity_cfg['labels']
self.vocab = self.defaults.Vocab()
self.tokenizer = self.defaults.Tokenizer(self.vocab)
self.tagger = self.defaults.Tagger(self.vocab, **tagger_cfg)
self.parser = self.defaults.Parser(self.vocab, **parser_cfg)
self.entity = self.defaults.Entity(self.vocab, **entity_cfg)
self.pipeline = self.defaults.Pipeline(self)
yield Trainer(self, gold_tuples)
self.end_training()
def __init__(self,
path=None,
vocab=True,
tokenizer=True,
tagger=True,
parser=True,
entity=True,
matcher=True,
serializer=True,
vectors=True,
pipeline=True,
defaults=True,
data_dir=None):
"""
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A model can be specified:
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1) by calling a Language subclass
- spacy.en.English()
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2) by calling a Language subclass with data_dir
- spacy.en.English('my/model/root')
- spacy.en.English(data_dir='my/model/root')
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3) by package name
- spacy.load('en_default')
- spacy.load('en_default==1.0.0')
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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 data_dir is not None and path is None:
warn("'data_dir' argument now named 'path'. Doing what you mean.")
path = data_dir
if isinstance(path, basestring):
path = pathlib.Path(path)
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if path is None:
path = util.match_best_version(self.lang, '', util.get_data_path())
self.path = path
defaults = defaults if defaults is not True else self.get_defaults(self.path)
self.defaults = defaults
self.vocab = vocab if vocab is not True else defaults.Vocab(vectors=vectors)
self.tokenizer = tokenizer if tokenizer is not True else defaults.Tokenizer(self.vocab)
self.tagger = tagger if tagger is not True else defaults.Tagger(self.vocab)
self.entity = entity if entity is not True else defaults.Entity(self.vocab)
self.parser = parser if parser is not True else defaults.Parser(self.vocab)
self.matcher = matcher if matcher is not True else defaults.Matcher(self.vocab)
if pipeline in (None, False):
self.pipeline = []
elif pipeline is True:
self.pipeline = defaults.Pipeline(self)
else:
self.pipeline = pipeline(self)
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def __reduce__(self):
args = (
self.path,
self.vocab,
self.tokenizer,
self.tagger,
self.parser,
self.entity,
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self.matcher
)
return (self.__class__, args, None, None)
def __call__(self, text, tag=True, parse=True, entity=True):
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"""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')
"""
doc = self.pipeline[0](text)
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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[1:]:
if proc and not skip.get(proc):
proc(doc)
return doc
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def pipe(self, texts, tag=True, parse=True, entity=True, n_threads=2,
batch_size=1000):
skip = {self.tagger: not tag, self.parser: not parse, self.entity: not entity}
stream = self.pipeline[0].pipe(texts,
n_threads=n_threads, batch_size=batch_size)
for proc in self.pipeline[1:]:
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()
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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')
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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_:
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file_.write(
json.dumps([
(TAG, tagger_freqs),
(DEP, dep_freqs),
(ENT_IOB, entity_iob_freqs),
(ENT_TYPE, entity_type_freqs),
(HEAD, head_freqs)
]))
def get_defaults(self, path):
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return self.Defaults(self.lang, path)