mirror of https://github.com/explosion/spaCy.git
514 lines
20 KiB
Python
514 lines
20 KiB
Python
# coding: utf8
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from __future__ import absolute_import, unicode_literals
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from contextlib import contextmanager
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import dill
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import numpy
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from thinc.neural import Model
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from thinc.neural.ops import NumpyOps, CupyOps
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from thinc.neural.optimizers import Adam, SGD
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import random
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import ujson
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from collections import OrderedDict
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from .tokenizer import Tokenizer
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from .vocab import Vocab
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from .tagger import Tagger
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from .lemmatizer import Lemmatizer
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from .syntax.parser import get_templates
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from .syntax import nonproj
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from .pipeline import NeuralDependencyParser, EntityRecognizer
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from .pipeline import TokenVectorEncoder, NeuralTagger, NeuralEntityRecognizer
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from .pipeline import NeuralLabeller
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from .compat import json_dumps
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from .attrs import IS_STOP
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from .lang.punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES, TOKENIZER_INFIXES
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from .lang.tokenizer_exceptions import TOKEN_MATCH
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from .lang.tag_map import TAG_MAP
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from .lang.lex_attrs import LEX_ATTRS
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from . import util
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from .scorer import Scorer
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class BaseDefaults(object):
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@classmethod
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def create_lemmatizer(cls, nlp=None):
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return Lemmatizer(cls.lemma_index, cls.lemma_exc, cls.lemma_rules)
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@classmethod
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def create_vocab(cls, nlp=None):
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lemmatizer = cls.create_lemmatizer(nlp)
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lex_attr_getters = dict(cls.lex_attr_getters)
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# This is messy, but it's the minimal working fix to Issue #639.
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lex_attr_getters[IS_STOP] = lambda string: string.lower() in cls.stop_words
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vocab = Vocab(lex_attr_getters=lex_attr_getters, tag_map=cls.tag_map,
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lemmatizer=lemmatizer)
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for tag_str, exc in cls.morph_rules.items():
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for orth_str, attrs in exc.items():
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vocab.morphology.add_special_case(tag_str, orth_str, attrs)
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return vocab
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@classmethod
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def create_tokenizer(cls, nlp=None):
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rules = cls.tokenizer_exceptions
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token_match = cls.token_match
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prefix_search = util.compile_prefix_regex(cls.prefixes).search \
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if cls.prefixes else None
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suffix_search = util.compile_suffix_regex(cls.suffixes).search \
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if cls.suffixes else None
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infix_finditer = util.compile_infix_regex(cls.infixes).finditer \
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if cls.infixes else None
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vocab = nlp.vocab if nlp is not None else cls.create_vocab(nlp)
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return Tokenizer(vocab, rules=rules,
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prefix_search=prefix_search, suffix_search=suffix_search,
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infix_finditer=infix_finditer, token_match=token_match)
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@classmethod
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def create_tagger(cls, nlp=None, **cfg):
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if nlp is None:
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return NeuralTagger(cls.create_vocab(nlp), **cfg)
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else:
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return NeuralTagger(nlp.vocab, **cfg)
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@classmethod
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def create_parser(cls, nlp=None, **cfg):
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if nlp is None:
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return NeuralDependencyParser(cls.create_vocab(nlp), **cfg)
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else:
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return NeuralDependencyParser(nlp.vocab, **cfg)
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@classmethod
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def create_entity(cls, nlp=None, **cfg):
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if nlp is None:
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return NeuralEntityRecognizer(cls.create_vocab(nlp), **cfg)
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else:
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return NeuralEntityRecognizer(nlp.vocab, **cfg)
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@classmethod
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def create_pipeline(cls, nlp=None):
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meta = nlp.meta if nlp is not None else {}
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# Resolve strings, like "cnn", "lstm", etc
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pipeline = []
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for entry in cls.pipeline:
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factory = cls.Defaults.factories[entry]
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pipeline.append(factory(nlp, **meta.get(entry, {})))
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return pipeline
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factories = {
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'make_doc': create_tokenizer,
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'tensorizer': lambda nlp, **cfg: [TokenVectorEncoder(nlp.vocab, **cfg)],
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'tagger': lambda nlp, **cfg: [NeuralTagger(nlp.vocab, **cfg)],
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'parser': lambda nlp, **cfg: [
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NeuralDependencyParser(nlp.vocab, **cfg),
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nonproj.deprojectivize],
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'ner': lambda nlp, **cfg: [NeuralEntityRecognizer(nlp.vocab, **cfg)],
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# Temporary compatibility -- delete after pivot
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'token_vectors': lambda nlp, **cfg: [TokenVectorEncoder(nlp.vocab, **cfg)],
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'tags': lambda nlp, **cfg: [NeuralTagger(nlp.vocab, **cfg)],
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'dependencies': lambda nlp, **cfg: [
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NeuralDependencyParser(nlp.vocab, **cfg),
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nonproj.deprojectivize,
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],
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'entities': lambda nlp, **cfg: [NeuralEntityRecognizer(nlp.vocab, **cfg)],
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}
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token_match = TOKEN_MATCH
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prefixes = tuple(TOKENIZER_PREFIXES)
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suffixes = tuple(TOKENIZER_SUFFIXES)
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infixes = tuple(TOKENIZER_INFIXES)
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tag_map = dict(TAG_MAP)
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tokenizer_exceptions = {}
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parser_features = get_templates('parser')
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entity_features = get_templates('ner')
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tagger_features = Tagger.feature_templates # TODO -- fix this
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stop_words = set()
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lemma_rules = {}
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lemma_exc = {}
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lemma_index = {}
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morph_rules = {}
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lex_attr_getters = LEX_ATTRS
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syntax_iterators = {}
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class Language(object):
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"""A text-processing pipeline. Usually you'll load this once per process,
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and pass the instance around your application.
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Defaults (class): Settings, data and factory methods for creating the `nlp`
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object and processing pipeline.
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lang (unicode): Two-letter language ID, i.e. ISO code.
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"""
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Defaults = BaseDefaults
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lang = None
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def __init__(self, vocab=True, make_doc=True, pipeline=None, meta={}, **kwargs):
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"""Initialise a Language object.
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vocab (Vocab): A `Vocab` object. If `True`, a vocab is created via
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`Language.Defaults.create_vocab`.
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make_doc (callable): A function that takes text and returns a `Doc`
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object. Usually a `Tokenizer`.
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pipeline (list): A list of annotation processes or IDs of annotation,
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processes, e.g. a `Tagger` object, or `'tagger'`. IDs are looked
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up in `Language.Defaults.factories`.
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meta (dict): Custom meta data for the Language class. Is written to by
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models to add model meta data.
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RETURNS (Language): The newly constructed object.
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"""
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self.meta = dict(meta)
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if vocab is True:
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factory = self.Defaults.create_vocab
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vocab = factory(self, **meta.get('vocab', {}))
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self.vocab = vocab
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if make_doc is True:
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factory = self.Defaults.create_tokenizer
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make_doc = factory(self, **meta.get('tokenizer', {}))
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self.tokenizer = make_doc
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if pipeline is True:
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self.pipeline = self.Defaults.create_pipeline(self)
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elif pipeline:
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self.pipeline = list(pipeline)
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# Resolve strings, like "cnn", "lstm", etc
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for i, entry in enumerate(self.pipeline):
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if entry in self.Defaults.factories:
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factory = self.Defaults.factories[entry]
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self.pipeline[i] = factory(self, **meta.get(entry, {}))
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else:
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self.pipeline = []
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flat_list = []
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for pipe in self.pipeline:
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if isinstance(pipe, list):
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flat_list.extend(pipe)
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else:
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flat_list.append(pipe)
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self.pipeline = flat_list
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# Conveniences to access pipeline components
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@property
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def tensorizer(self):
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return self.get_component('tensorizer')
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@property
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def tagger(self):
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return self.get_component('tagger')
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@property
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def parser(self):
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return self.get_component('parser')
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@property
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def entity(self):
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return self.get_component('ner')
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@property
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def matcher(self):
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return self.get_component('matcher')
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def get_component(self, name):
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if self.pipeline in (True, None):
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return None
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for proc in self.pipeline:
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if hasattr(proc, 'name') and proc.name.endswith(name):
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return proc
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return None
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def __call__(self, text, disable=[]):
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"""'Apply the pipeline to some text. The text can span multiple sentences,
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and can contain arbtrary whitespace. Alignment into the original string
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is preserved.
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text (unicode): The text to be processed.
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disable (list): Names of the pipeline components to disable.
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RETURNS (Doc): A container for accessing the annotations.
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EXAMPLE:
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>>> tokens = nlp('An example sentence. Another example sentence.')
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>>> tokens[0].text, tokens[0].head.tag_
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('An', 'NN')
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"""
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doc = self.make_doc(text)
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for proc in self.pipeline:
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name = getattr(proc, 'name', None)
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if name in disable:
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continue
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doc = proc(doc)
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return doc
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def make_doc(self, text):
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return self.tokenizer(text)
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def update(self, docs, golds, drop=0., sgd=None, losses=None):
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"""Update the models in the pipeline.
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docs (iterable): A batch of `Doc` objects.
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golds (iterable): A batch of `GoldParse` objects.
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drop (float): The droput rate.
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sgd (callable): An optimizer.
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RETURNS (dict): Results from the update.
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EXAMPLE:
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>>> with nlp.begin_training(gold, use_gpu=True) as (trainer, optimizer):
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>>> for epoch in trainer.epochs(gold):
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>>> for docs, golds in epoch:
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>>> state = nlp.update(docs, golds, sgd=optimizer)
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"""
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tok2vec = self.pipeline[0]
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feats = tok2vec.doc2feats(docs)
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grads = {}
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def get_grads(W, dW, key=None):
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grads[key] = (W, dW)
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pipes = list(self.pipeline[1:])
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random.shuffle(pipes)
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for proc in pipes:
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if not hasattr(proc, 'update'):
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continue
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tokvecses, bp_tokvecses = tok2vec.model.begin_update(feats, drop=drop)
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d_tokvecses = proc.update((docs, tokvecses), golds,
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drop=drop, sgd=get_grads, losses=losses)
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if d_tokvecses is not None:
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bp_tokvecses(d_tokvecses, sgd=sgd)
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for key, (W, dW) in grads.items():
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sgd(W, dW, key=key)
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# Clear the tensor variable, to free GPU memory.
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# If we don't do this, the memory leak gets pretty
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# bad, because we may be holding part of a batch.
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for doc in docs:
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doc.tensor = None
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def preprocess_gold(self, docs_golds):
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"""Can be called before training to pre-process gold data. By default,
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it handles nonprojectivity and adds missing tags to the tag map.
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docs_golds (iterable): Tuples of `Doc` and `GoldParse` objects.
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YIELDS (tuple): Tuples of preprocessed `Doc` and `GoldParse` objects.
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"""
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for proc in self.pipeline:
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if hasattr(proc, 'preprocess_gold'):
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docs_golds = proc.preprocess_gold(docs_golds)
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for doc, gold in docs_golds:
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yield doc, gold
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def begin_training(self, get_gold_tuples, **cfg):
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"""Allocate models, pre-process training data and acquire a trainer and
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optimizer. Used as a contextmanager.
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gold_tuples (iterable): Gold-standard training data.
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**cfg: Config parameters.
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YIELDS (tuple): A trainer and an optimizer.
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EXAMPLE:
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>>> with nlp.begin_training(gold, use_gpu=True) as (trainer, optimizer):
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>>> for epoch in trainer.epochs(gold):
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>>> for docs, golds in epoch:
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>>> state = nlp.update(docs, golds, sgd=optimizer)
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"""
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if self.parser:
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self.pipeline.append(NeuralLabeller(self.vocab))
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# Populate vocab
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for _, annots_brackets in get_gold_tuples():
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for annots, _ in annots_brackets:
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for word in annots[1]:
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_ = self.vocab[word]
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contexts = []
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if cfg.get('device', -1) >= 0:
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import cupy.cuda.device
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device = cupy.cuda.device.Device(cfg['device'])
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device.use()
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Model.ops = CupyOps()
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Model.Ops = CupyOps
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else:
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device = None
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for proc in self.pipeline:
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if hasattr(proc, 'begin_training'):
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context = proc.begin_training(get_gold_tuples(),
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pipeline=self.pipeline)
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contexts.append(context)
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learn_rate = util.env_opt('learn_rate', 0.001)
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beta1 = util.env_opt('optimizer_B1', 0.9)
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beta2 = util.env_opt('optimizer_B2', 0.999)
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eps = util.env_opt('optimizer_eps', 1e-08)
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L2 = util.env_opt('L2_penalty', 1e-6)
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max_grad_norm = util.env_opt('grad_norm_clip', 1.)
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optimizer = Adam(Model.ops, learn_rate, L2=L2, beta1=beta1,
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beta2=beta2, eps=eps)
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optimizer.max_grad_norm = max_grad_norm
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optimizer.device = device
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return optimizer
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def evaluate(self, docs_golds):
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docs, golds = zip(*docs_golds)
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scorer = Scorer()
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for doc, gold in zip(self.pipe(docs, batch_size=32), golds):
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scorer.score(doc, gold)
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doc.tensor = None
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return scorer
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@contextmanager
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def use_params(self, params, **cfg):
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"""Replace weights of models in the pipeline with those provided in the
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params dictionary. Can be used as a contextmanager, in which case,
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models go back to their original weights after the block.
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params (dict): A dictionary of parameters keyed by model ID.
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**cfg: Config parameters.
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EXAMPLE:
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>>> with nlp.use_params(optimizer.averages):
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>>> nlp.to_disk('/tmp/checkpoint')
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"""
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contexts = [pipe.use_params(params) for pipe
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in self.pipeline if hasattr(pipe, 'use_params')]
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# TODO: Having trouble with contextlib
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# Workaround: these aren't actually context managers atm.
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for context in contexts:
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try:
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next(context)
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except StopIteration:
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pass
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yield
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for context in contexts:
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try:
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next(context)
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except StopIteration:
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pass
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def pipe(self, texts, n_threads=2, batch_size=1000, disable=[]):
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"""Process texts as a stream, and yield `Doc` objects in order. Supports
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GIL-free multi-threading.
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texts (iterator): A sequence of texts to process.
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n_threads (int): The number of worker threads to use. If -1, OpenMP will
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decide how many to use at run time. Default is 2.
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batch_size (int): The number of texts to buffer.
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disable (list): Names of the pipeline components to disable.
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YIELDS (Doc): Documents in the order of the original text.
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EXAMPLE:
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>>> texts = [u'One document.', u'...', u'Lots of documents']
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>>> for doc in nlp.pipe(texts, batch_size=50, n_threads=4):
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>>> assert doc.is_parsed
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"""
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docs = (self.make_doc(text) for text in texts)
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docs = texts
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for proc in self.pipeline:
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name = getattr(proc, 'name', None)
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if name in disable:
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continue
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if hasattr(proc, 'pipe'):
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docs = proc.pipe(docs, n_threads=n_threads, batch_size=batch_size)
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else:
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# Apply the function, but yield the doc
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docs = _pipe(proc, docs)
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for doc in docs:
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yield doc
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def to_disk(self, path, disable=tuple()):
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"""Save the current state to a directory. If a model is loaded, this
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will include the model.
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path (unicode or Path): A path to a directory, which will be created if
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it doesn't exist. Paths may be either strings or `Path`-like objects.
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disable (list): Names of pipeline components to disable and prevent
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from being saved.
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EXAMPLE:
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>>> nlp.to_disk('/path/to/models')
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"""
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path = util.ensure_path(path)
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serializers = OrderedDict((
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('vocab', lambda p: self.vocab.to_disk(p)),
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('tokenizer', lambda p: self.tokenizer.to_disk(p, vocab=False)),
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('meta.json', lambda p: p.open('w').write(json_dumps(self.meta)))
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))
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for proc in self.pipeline:
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if not hasattr(proc, 'name'):
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continue
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if proc.name in disable:
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continue
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if not hasattr(proc, 'to_disk'):
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continue
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serializers[proc.name] = lambda p, proc=proc: proc.to_disk(p, vocab=False)
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util.to_disk(path, serializers, {p: False for p in disable})
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def from_disk(self, path, disable=tuple()):
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"""Loads state from a directory. Modifies the object in place and
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returns it. If the saved `Language` object contains a model, the
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model will be loaded.
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path (unicode or Path): A path to a directory. Paths may be either
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strings or `Path`-like objects.
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disable (list): Names of the pipeline components to disable.
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RETURNS (Language): The modified `Language` object.
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EXAMPLE:
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>>> from spacy.language import Language
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>>> nlp = Language().from_disk('/path/to/models')
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"""
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path = util.ensure_path(path)
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deserializers = OrderedDict((
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('vocab', lambda p: self.vocab.from_disk(p)),
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('tokenizer', lambda p: self.tokenizer.from_disk(p, vocab=False)),
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('meta.json', lambda p: p.open('w').write(json_dumps(self.meta)))
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))
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for proc in self.pipeline:
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if not hasattr(proc, 'name'):
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continue
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if proc.name in disable:
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continue
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if not hasattr(proc, 'to_disk'):
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continue
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deserializers[proc.name] = lambda p, proc=proc: proc.from_disk(p, vocab=False)
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exclude = {p: False for p in disable}
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if not (path / 'vocab').exists():
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exclude['vocab'] = True
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util.from_disk(path, deserializers, exclude)
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return self
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def to_bytes(self, disable=[]):
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"""Serialize the current state to a binary string.
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disable (list): Nameds of pipeline components to disable and prevent
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from being serialized.
|
|
RETURNS (bytes): The serialized form of the `Language` object.
|
|
"""
|
|
serializers = OrderedDict((
|
|
('vocab', lambda: self.vocab.to_bytes()),
|
|
('tokenizer', lambda: self.tokenizer.to_bytes(vocab=False)),
|
|
('meta', lambda: ujson.dumps(self.meta))
|
|
))
|
|
for i, proc in enumerate(self.pipeline):
|
|
if getattr(proc, 'name', None) in disable:
|
|
continue
|
|
if not hasattr(proc, 'to_bytes'):
|
|
continue
|
|
serializers[i] = lambda proc=proc: proc.to_bytes(vocab=False)
|
|
return util.to_bytes(serializers, {})
|
|
|
|
def from_bytes(self, bytes_data, disable=[]):
|
|
"""Load state from a binary string.
|
|
|
|
bytes_data (bytes): The data to load from.
|
|
disable (list): Names of the pipeline components to disable.
|
|
RETURNS (Language): The `Language` object.
|
|
"""
|
|
deserializers = OrderedDict((
|
|
('vocab', lambda b: self.vocab.from_bytes(b)),
|
|
('tokenizer', lambda b: self.tokenizer.from_bytes(b, vocab=False)),
|
|
('meta', lambda b: self.meta.update(ujson.loads(b)))
|
|
))
|
|
for i, proc in enumerate(self.pipeline):
|
|
if getattr(proc, 'name', None) in disable:
|
|
continue
|
|
if not hasattr(proc, 'from_bytes'):
|
|
continue
|
|
deserializers[i] = lambda b, proc=proc: proc.from_bytes(b, vocab=False)
|
|
msg = util.from_bytes(bytes_data, deserializers, {})
|
|
return self
|
|
|
|
|
|
def _pipe(func, docs):
|
|
for doc in docs:
|
|
func(doc)
|
|
yield doc
|