spaCy/spacy/language.py

588 lines
23 KiB
Python

# coding: utf8
from __future__ import absolute_import, unicode_literals
from contextlib import contextmanager
import dill
import numpy
from thinc.neural import Model
from thinc.neural.ops import NumpyOps, CupyOps
from thinc.neural.optimizers import Adam, SGD
import random
import ujson
from collections import OrderedDict
import itertools
from .tokenizer import Tokenizer
from .vocab import Vocab
from .tagger import Tagger
from .lemmatizer import Lemmatizer
from .syntax.parser import get_templates
from .syntax import nonproj
from .pipeline import NeuralDependencyParser, EntityRecognizer
from .pipeline import TokenVectorEncoder, NeuralTagger, NeuralEntityRecognizer
from .pipeline import NeuralLabeller
from .pipeline import SimilarityHook
from .pipeline import TextCategorizer
from . import about
from .compat import json_dumps, izip
from .attrs import IS_STOP
from .lang.punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES, TOKENIZER_INFIXES
from .lang.tokenizer_exceptions import TOKEN_MATCH
from .lang.tag_map import TAG_MAP
from .lang.lex_attrs import LEX_ATTRS
from . import util
from .scorer import Scorer
from ._ml import link_vectors_to_models
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)
lex_attr_getters = dict(cls.lex_attr_getters)
# This is messy, but it's the minimal working fix to Issue #639.
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)
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 create_tokenizer(cls, nlp=None):
rules = cls.tokenizer_exceptions
token_match = cls.token_match
prefix_search = util.compile_prefix_regex(cls.prefixes).search \
if cls.prefixes else None
suffix_search = util.compile_suffix_regex(cls.suffixes).search \
if cls.suffixes else None
infix_finditer = util.compile_infix_regex(cls.infixes).finditer \
if cls.infixes else 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, **cfg):
if nlp is None:
return NeuralTagger(cls.create_vocab(nlp), **cfg)
else:
return NeuralTagger(nlp.vocab, **cfg)
@classmethod
def create_parser(cls, nlp=None, **cfg):
if nlp is None:
return NeuralDependencyParser(cls.create_vocab(nlp), **cfg)
else:
return NeuralDependencyParser(nlp.vocab, **cfg)
@classmethod
def create_entity(cls, nlp=None, **cfg):
if nlp is None:
return NeuralEntityRecognizer(cls.create_vocab(nlp), **cfg)
else:
return NeuralEntityRecognizer(nlp.vocab, **cfg)
@classmethod
def create_pipeline(cls, nlp=None, disable=tuple()):
meta = nlp.meta if nlp is not None else {}
# Resolve strings, like "cnn", "lstm", etc
pipeline = []
for entry in meta.get('pipeline', []):
if entry in disable or getattr(entry, 'name', entry) in disable:
continue
factory = cls.Defaults.factories[entry]
pipeline.append(factory(nlp, **meta.get(entry, {})))
return pipeline
factories = {
'make_doc': create_tokenizer,
'tensorizer': lambda nlp, **cfg: [TokenVectorEncoder(nlp.vocab, **cfg)],
'tagger': lambda nlp, **cfg: [NeuralTagger(nlp.vocab, **cfg)],
'parser': lambda nlp, **cfg: [
NeuralDependencyParser(nlp.vocab, **cfg),
nonproj.deprojectivize],
'ner': lambda nlp, **cfg: [NeuralEntityRecognizer(nlp.vocab, **cfg)],
'similarity': lambda nlp, **cfg: [SimilarityHook(nlp.vocab, **cfg)],
'textcat': lambda nlp, **cfg: [TextCategorizer(nlp.vocab, **cfg)],
# Temporary compatibility -- delete after pivot
'token_vectors': lambda nlp, **cfg: [TokenVectorEncoder(nlp.vocab, **cfg)],
'tags': lambda nlp, **cfg: [NeuralTagger(nlp.vocab, **cfg)],
'dependencies': lambda nlp, **cfg: [
NeuralDependencyParser(nlp.vocab, **cfg),
nonproj.deprojectivize,
],
'entities': lambda nlp, **cfg: [NeuralEntityRecognizer(nlp.vocab, **cfg)],
}
token_match = TOKEN_MATCH
prefixes = tuple(TOKENIZER_PREFIXES)
suffixes = tuple(TOKENIZER_SUFFIXES)
infixes = tuple(TOKENIZER_INFIXES)
tag_map = dict(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 = LEX_ATTRS
syntax_iterators = {}
class Language(object):
"""A text-processing pipeline. Usually you'll load this once per process,
and pass the instance around your application.
Defaults (class): Settings, data and factory methods for creating the `nlp`
object and processing pipeline.
lang (unicode): Two-letter language ID, i.e. ISO code.
"""
Defaults = BaseDefaults
lang = None
def __init__(self, vocab=True, make_doc=True, pipeline=None,
meta={}, disable=tuple(), **kwargs):
"""Initialise a Language object.
vocab (Vocab): A `Vocab` object. If `True`, a vocab is created via
`Language.Defaults.create_vocab`.
make_doc (callable): A function that takes text and returns a `Doc`
object. Usually a `Tokenizer`.
pipeline (list): A list of annotation processes or IDs of annotation,
processes, e.g. a `Tagger` object, or `'tagger'`. IDs are looked
up in `Language.Defaults.factories`.
disable (list): A list of component names to exclude from the pipeline.
The disable list has priority over the pipeline list -- if the same
string occurs in both, the component is not loaded.
meta (dict): Custom meta data for the Language class. Is written to by
models to add model meta data.
RETURNS (Language): The newly constructed object.
"""
self._meta = dict(meta)
if vocab is True:
factory = self.Defaults.create_vocab
vocab = factory(self, **meta.get('vocab', {}))
self.vocab = vocab
if make_doc is True:
factory = self.Defaults.create_tokenizer
make_doc = factory(self, **meta.get('tokenizer', {}))
self.tokenizer = make_doc
if pipeline is True:
self.pipeline = self.Defaults.create_pipeline(self, disable)
elif pipeline:
# Careful not to do getattr(p, 'name', None) here
# If we had disable=[None], we'd disable everything!
self.pipeline = [p for p in pipeline
if p not in disable
and getattr(p, 'name', p) not in disable]
# Resolve strings, like "cnn", "lstm", etc
for i, entry in enumerate(self.pipeline):
if entry in self.Defaults.factories:
factory = self.Defaults.factories[entry]
self.pipeline[i] = factory(self, **meta.get(entry, {}))
else:
self.pipeline = []
flat_list = []
for pipe in self.pipeline:
if isinstance(pipe, list):
flat_list.extend(pipe)
else:
flat_list.append(pipe)
self.pipeline = flat_list
self._optimizer = None
@property
def meta(self):
self._meta.setdefault('lang', self.vocab.lang)
self._meta.setdefault('name', '')
self._meta.setdefault('version', '0.0.0')
self._meta.setdefault('spacy_version', about.__version__)
self._meta.setdefault('description', '')
self._meta.setdefault('author', '')
self._meta.setdefault('email', '')
self._meta.setdefault('url', '')
self._meta.setdefault('license', '')
pipeline = []
for component in self.pipeline:
if hasattr(component, 'name'):
pipeline.append(component.name)
self._meta['pipeline'] = pipeline
return self._meta
@meta.setter
def meta(self, value):
self._meta = value
# Conveniences to access pipeline components
@property
def tensorizer(self):
return self.get_component('tensorizer')
@property
def tagger(self):
return self.get_component('tagger')
@property
def parser(self):
return self.get_component('parser')
@property
def entity(self):
return self.get_component('ner')
@property
def matcher(self):
return self.get_component('matcher')
def get_component(self, name):
if self.pipeline in (True, None):
return None
for proc in self.pipeline:
if hasattr(proc, 'name') and proc.name.endswith(name):
return proc
return None
def __call__(self, text, disable=[]):
"""'Apply the pipeline to some text. The text can span multiple sentences,
and can contain arbtrary whitespace. Alignment into the original string
is preserved.
text (unicode): The text to be processed.
disable (list): Names of the pipeline components to disable.
RETURNS (Doc): A container for accessing the annotations.
EXAMPLE:
>>> tokens = nlp('An example sentence. Another example sentence.')
>>> tokens[0].text, tokens[0].head.tag_
('An', 'NN')
"""
doc = self.make_doc(text)
for proc in self.pipeline:
name = getattr(proc, 'name', None)
if name in disable:
continue
doc = proc(doc)
return doc
def make_doc(self, text):
return self.tokenizer(text)
def update(self, docs, golds, drop=0., sgd=None, losses=None,
update_shared=False):
"""Update the models in the pipeline.
docs (iterable): A batch of `Doc` objects.
golds (iterable): A batch of `GoldParse` objects.
drop (float): The droput rate.
sgd (callable): An optimizer.
RETURNS (dict): Results from the update.
EXAMPLE:
>>> with nlp.begin_training(gold, use_gpu=True) as (trainer, optimizer):
>>> for epoch in trainer.epochs(gold):
>>> for docs, golds in epoch:
>>> state = nlp.update(docs, golds, sgd=optimizer)
"""
if len(docs) != len(golds):
raise IndexError("Update expects same number of docs and golds "
"Got: %d, %d" % (len(docs), len(golds)))
if len(docs) == 0:
return
if sgd is None:
if self._optimizer is None:
self._optimizer = Adam(Model.ops, 0.001)
sgd = self._optimizer
grads = {}
def get_grads(W, dW, key=None):
grads[key] = (W, dW)
pipes = list(self.pipeline)
random.shuffle(pipes)
for proc in pipes:
if not hasattr(proc, 'update'):
continue
proc.update(docs, golds, drop=drop, sgd=get_grads, losses=losses)
for key, (W, dW) in grads.items():
sgd(W, dW, key=key)
def preprocess_gold(self, docs_golds):
"""Can be called before training to pre-process gold data. By default,
it handles nonprojectivity and adds missing tags to the tag map.
docs_golds (iterable): Tuples of `Doc` and `GoldParse` objects.
YIELDS (tuple): Tuples of preprocessed `Doc` and `GoldParse` objects.
"""
for proc in self.pipeline:
if hasattr(proc, 'preprocess_gold'):
docs_golds = proc.preprocess_gold(docs_golds)
for doc, gold in docs_golds:
yield doc, gold
def resume_training(self, **cfg):
if cfg.get('device', -1) >= 0:
device = util.use_gpu(cfg['device'])
if self.vocab.vectors.data.shape[1] >= 1:
self.vocab.vectors.data = Model.ops.asarray(
self.vocab.vectors.data)
else:
device = None
learn_rate = util.env_opt('learn_rate', 0.001)
beta1 = util.env_opt('optimizer_B1', 0.9)
beta2 = util.env_opt('optimizer_B2', 0.999)
eps = util.env_opt('optimizer_eps', 1e-08)
L2 = util.env_opt('L2_penalty', 1e-6)
max_grad_norm = util.env_opt('grad_norm_clip', 1.)
self._optimizer = Adam(Model.ops, learn_rate, L2=L2, beta1=beta1,
beta2=beta2, eps=eps)
self._optimizer.max_grad_norm = max_grad_norm
self._optimizer.device = device
return self._optimizer
def begin_training(self, get_gold_tuples=None, **cfg):
"""Allocate models, pre-process training data and acquire a trainer and
optimizer. Used as a contextmanager.
get_gold_tuples (function): Function returning gold data
**cfg: Config parameters.
returns: An optimizer
"""
# Populate vocab
if get_gold_tuples is not None:
for _, annots_brackets in get_gold_tuples():
for annots, _ in annots_brackets:
for word in annots[1]:
_ = self.vocab[word]
contexts = []
if cfg.get('device', -1) >= 0:
device = util.use_gpu(cfg['device'])
if self.vocab.vectors.data.shape[1] >= 1:
self.vocab.vectors.data = Model.ops.asarray(
self.vocab.vectors.data)
else:
device = None
link_vectors_to_models(self.vocab)
for proc in self.pipeline:
if hasattr(proc, 'begin_training'):
context = proc.begin_training(get_gold_tuples(),
pipeline=self.pipeline)
contexts.append(context)
learn_rate = util.env_opt('learn_rate', 0.001)
beta1 = util.env_opt('optimizer_B1', 0.9)
beta2 = util.env_opt('optimizer_B2', 0.999)
eps = util.env_opt('optimizer_eps', 1e-08)
L2 = util.env_opt('L2_penalty', 1e-6)
max_grad_norm = util.env_opt('grad_norm_clip', 1.)
self._optimizer = Adam(Model.ops, learn_rate, L2=L2, beta1=beta1,
beta2=beta2, eps=eps)
self._optimizer.max_grad_norm = max_grad_norm
self._optimizer.device = device
return self._optimizer
def evaluate(self, docs_golds):
scorer = Scorer()
docs, golds = zip(*docs_golds)
docs = list(docs)
golds = list(golds)
for pipe in self.pipeline:
if not hasattr(pipe, 'pipe'):
for doc in docs:
pipe(doc)
else:
docs = list(pipe.pipe(docs))
assert len(docs) == len(golds)
for doc, gold in zip(docs, golds):
scorer.score(doc, gold)
return scorer
@contextmanager
def use_params(self, params, **cfg):
"""Replace weights of models in the pipeline with those provided in the
params dictionary. Can be used as a contextmanager, in which case,
models go back to their original weights after the block.
params (dict): A dictionary of parameters keyed by model ID.
**cfg: Config parameters.
EXAMPLE:
>>> with nlp.use_params(optimizer.averages):
>>> nlp.to_disk('/tmp/checkpoint')
"""
contexts = [pipe.use_params(params) for pipe
in self.pipeline if hasattr(pipe, 'use_params')]
# TODO: Having trouble with contextlib
# Workaround: these aren't actually context managers atm.
for context in contexts:
try:
next(context)
except StopIteration:
pass
yield
for context in contexts:
try:
next(context)
except StopIteration:
pass
def pipe(self, texts, as_tuples=False, n_threads=2, batch_size=1000,
disable=[]):
"""Process texts as a stream, and yield `Doc` objects in order. Supports
GIL-free multi-threading.
texts (iterator): A sequence of texts to process.
as_tuples (bool):
If set to True, inputs should be a sequence of
(text, context) tuples. Output will then be a sequence of
(doc, context) tuples. Defaults to False.
n_threads (int): The number of worker threads to use. If -1, OpenMP will
decide how many to use at run time. Default is 2.
batch_size (int): The number of texts to buffer.
disable (list): Names of the pipeline components to disable.
YIELDS (Doc): Documents in the order of the original text.
EXAMPLE:
>>> texts = [u'One document.', u'...', u'Lots of documents']
>>> for doc in nlp.pipe(texts, batch_size=50, n_threads=4):
>>> assert doc.is_parsed
"""
if as_tuples:
text_context1, text_context2 = itertools.tee(texts)
texts = (tc[0] for tc in text_context1)
contexts = (tc[1] for tc in text_context2)
docs = self.pipe(texts, n_threads=n_threads, batch_size=batch_size,
disable=disable)
for doc, context in izip(docs, contexts):
yield (doc, context)
return
docs = (self.make_doc(text) for text in texts)
for proc in self.pipeline:
name = getattr(proc, 'name', None)
if name in disable:
continue
if hasattr(proc, 'pipe'):
docs = proc.pipe(docs, n_threads=n_threads, batch_size=batch_size)
else:
# Apply the function, but yield the doc
docs = _pipe(proc, docs)
for doc in docs:
yield doc
def to_disk(self, path, disable=tuple()):
"""Save the current state to a directory. If a model is loaded, this
will include the model.
path (unicode or Path): A path to a directory, which will be created if
it doesn't exist. Paths may be either strings or `Path`-like objects.
disable (list): Names of pipeline components to disable and prevent
from being saved.
EXAMPLE:
>>> nlp.to_disk('/path/to/models')
"""
path = util.ensure_path(path)
serializers = OrderedDict((
('vocab', lambda p: self.vocab.to_disk(p)),
('tokenizer', lambda p: self.tokenizer.to_disk(p, vocab=False)),
('meta.json', lambda p: p.open('w').write(json_dumps(self.meta)))
))
for proc in self.pipeline:
if not hasattr(proc, 'name'):
continue
if proc.name in disable:
continue
if not hasattr(proc, 'to_disk'):
continue
serializers[proc.name] = lambda p, proc=proc: proc.to_disk(p, vocab=False)
util.to_disk(path, serializers, {p: False for p in disable})
def from_disk(self, path, disable=tuple()):
"""Loads state from a directory. Modifies the object in place and
returns it. If the saved `Language` object contains a model, the
model will be loaded.
path (unicode or Path): A path to a directory. Paths may be either
strings or `Path`-like objects.
disable (list): Names of the pipeline components to disable.
RETURNS (Language): The modified `Language` object.
EXAMPLE:
>>> from spacy.language import Language
>>> nlp = Language().from_disk('/path/to/models')
"""
path = util.ensure_path(path)
deserializers = OrderedDict((
('vocab', lambda p: self.vocab.from_disk(p)),
('tokenizer', lambda p: self.tokenizer.from_disk(p, vocab=False)),
('meta.json', lambda p: p.open('w').write(json_dumps(self.meta)))
))
for proc in self.pipeline:
if not hasattr(proc, 'name'):
continue
if proc.name in disable:
continue
if not hasattr(proc, 'to_disk'):
continue
deserializers[proc.name] = lambda p, proc=proc: proc.from_disk(p, vocab=False)
exclude = {p: False for p in disable}
if not (path / 'vocab').exists():
exclude['vocab'] = True
util.from_disk(path, deserializers, exclude)
return self
def to_bytes(self, disable=[]):
"""Serialize the current state to a binary string.
disable (list): Nameds of pipeline components to disable and prevent
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