mirror of https://github.com/explosion/spaCy.git
976 lines
36 KiB
Cython
976 lines
36 KiB
Cython
# cython: infer_types=True
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# cython: profile=True
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# coding: utf8
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from __future__ import unicode_literals
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import numpy
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cimport numpy as np
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import cytoolz
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from collections import OrderedDict
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import ujson
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import msgpack
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from thinc.api import chain
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from thinc.v2v import Affine, SELU, Softmax
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from thinc.t2v import Pooling, max_pool, mean_pool
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from thinc.neural.util import to_categorical, copy_array
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from thinc.neural._classes.difference import Siamese, CauchySimilarity
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from .tokens.doc cimport Doc
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from .syntax.nn_parser cimport Parser
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from .syntax import nonproj
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from .syntax.ner cimport BiluoPushDown
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from .syntax.arc_eager cimport ArcEager
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from .morphology cimport Morphology
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from .vocab cimport Vocab
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from .syntax import nonproj
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from .compat import json_dumps
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from .attrs import POS
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from .parts_of_speech import X
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from ._ml import Tok2Vec, build_text_classifier, build_tagger_model
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from ._ml import link_vectors_to_models, zero_init, flatten
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from ._ml import create_default_optimizer
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from . import util
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class SentenceSegmenter(object):
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"""A simple spaCy hook, to allow custom sentence boundary detection logic
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(that doesn't require the dependency parse). To change the sentence
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boundary detection strategy, pass a generator function `strategy` on
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initialization, or assign a new strategy to the .strategy attribute.
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Sentence detection strategies should be generators that take `Doc` objects
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and yield `Span` objects for each sentence.
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"""
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name = 'sbd'
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def __init__(self, vocab, strategy=None):
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self.vocab = vocab
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if strategy is None or strategy == 'on_punct':
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strategy = self.split_on_punct
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self.strategy = strategy
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def __call__(self, doc):
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doc.user_hooks['sents'] = self.strategy
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return doc
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@staticmethod
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def split_on_punct(doc):
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start = 0
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seen_period = False
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for i, word in enumerate(doc):
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if seen_period and not word.is_punct:
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yield doc[start:word.i]
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start = word.i
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seen_period = False
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elif word.text in ['.', '!', '?']:
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seen_period = True
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if start < len(doc):
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yield doc[start:len(doc)]
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class Pipe(object):
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"""This class is not instantiated directly. Components inherit from it, and
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it defines the interface that components should follow to function as
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components in a spaCy analysis pipeline.
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"""
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name = None
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@classmethod
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def Model(cls, *shape, **kwargs):
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"""Initialize a model for the pipe."""
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raise NotImplementedError
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def __init__(self, vocab, model=True, **cfg):
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"""Create a new pipe instance."""
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raise NotImplementedError
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def __call__(self, doc):
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"""Apply the pipe to one document. The document is
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modified in-place, and returned.
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Both __call__ and pipe should delegate to the `predict()`
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and `set_annotations()` methods.
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"""
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scores, tensors = self.predict([doc])
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self.set_annotations([doc], scores, tensors=tensors)
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return doc
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def pipe(self, stream, batch_size=128, n_threads=-1):
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"""Apply the pipe to a stream of documents.
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Both __call__ and pipe should delegate to the `predict()`
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and `set_annotations()` methods.
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"""
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for docs in cytoolz.partition_all(batch_size, stream):
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docs = list(docs)
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scores, tensors = self.predict(docs)
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self.set_annotations(docs, scores, tensor=tensors)
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yield from docs
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def predict(self, docs):
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"""Apply the pipeline's model to a batch of docs, without
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modifying them.
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"""
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raise NotImplementedError
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def set_annotations(self, docs, scores, tensors=None):
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"""Modify a batch of documents, using pre-computed scores."""
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raise NotImplementedError
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def update(self, docs, golds, drop=0., sgd=None, losses=None):
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"""Learn from a batch of documents and gold-standard information,
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updating the pipe's model.
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Delegates to predict() and get_loss().
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"""
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raise NotImplementedError
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def get_loss(self, docs, golds, scores):
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"""Find the loss and gradient of loss for the batch of
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documents and their predicted scores."""
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raise NotImplementedError
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def add_label(self, label):
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"""Add an output label, to be predicted by the model.
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It's possible to extend pre-trained models with new labels,
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but care should be taken to avoid the "catastrophic forgetting"
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problem.
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"""
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raise NotImplementedError
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def create_optimizer(self):
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return create_default_optimizer(self.model.ops,
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**self.cfg.get('optimizer', {}))
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def begin_training(self, gold_tuples=tuple(), pipeline=None, sgd=None,
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**kwargs):
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"""Initialize the pipe for training, using data exampes if available.
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If no model has been initialized yet, the model is added."""
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if self.model is True:
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self.model = self.Model(**self.cfg)
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link_vectors_to_models(self.vocab)
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if sgd is None:
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sgd = self.create_optimizer()
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return sgd
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def use_params(self, params):
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"""Modify the pipe's model, to use the given parameter values."""
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with self.model.use_params(params):
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yield
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def to_bytes(self, **exclude):
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"""Serialize the pipe to a bytestring."""
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serialize = OrderedDict()
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serialize['cfg'] = lambda: json_dumps(self.cfg)
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if self.model in (True, False, None):
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serialize['model'] = lambda: self.model
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else:
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serialize['model'] = self.model.to_bytes
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serialize['vocab'] = self.vocab.to_bytes
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return util.to_bytes(serialize, exclude)
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def from_bytes(self, bytes_data, **exclude):
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"""Load the pipe from a bytestring."""
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def load_model(b):
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if self.model is True:
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self.cfg.setdefault('pretrained_dims', self.vocab.vectors_length)
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self.model = self.Model(**self.cfg)
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self.model.from_bytes(b)
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deserialize = OrderedDict((
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('cfg', lambda b: self.cfg.update(ujson.loads(b))),
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('vocab', lambda b: self.vocab.from_bytes(b)),
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('model', load_model),
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))
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util.from_bytes(bytes_data, deserialize, exclude)
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return self
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def to_disk(self, path, **exclude):
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"""Serialize the pipe to disk."""
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serialize = OrderedDict()
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serialize['cfg'] = lambda p: p.open('w').write(json_dumps(self.cfg))
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serialize['vocab'] = lambda p: self.vocab.to_disk(p)
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if self.model not in (None, True, False):
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serialize['model'] = lambda p: p.open('wb').write(self.model.to_bytes())
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util.to_disk(path, serialize, exclude)
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def from_disk(self, path, **exclude):
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"""Load the pipe from disk."""
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def load_model(p):
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if self.model is True:
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self.cfg.setdefault('pretrained_dims', self.vocab.vectors_length)
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self.model = self.Model(**self.cfg)
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self.model.from_bytes(p.open('rb').read())
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deserialize = OrderedDict((
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('cfg', lambda p: self.cfg.update(_load_cfg(p))),
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('vocab', lambda p: self.vocab.from_disk(p)),
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('model', load_model),
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))
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util.from_disk(path, deserialize, exclude)
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return self
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def _load_cfg(path):
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if path.exists():
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with path.open() as file_:
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return ujson.load(file_)
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else:
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return {}
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class Tensorizer(Pipe):
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"""Assign position-sensitive vectors to tokens, using a CNN or RNN."""
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name = 'tensorizer'
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@classmethod
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def Model(cls, output_size=300, input_size=384, **cfg):
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"""Create a new statistical model for the class.
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width (int): Output size of the model.
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embed_size (int): Number of vectors in the embedding table.
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**cfg: Config parameters.
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RETURNS (Model): A `thinc.neural.Model` or similar instance.
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"""
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model = chain(
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SELU(output_size, input_size),
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SELU(output_size, output_size),
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zero_init(Affine(output_size, output_size)))
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return model
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def __init__(self, vocab, model=True, **cfg):
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"""Construct a new statistical model. Weights are not allocated on
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initialisation.
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vocab (Vocab): A `Vocab` instance. The model must share the same
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`Vocab` instance with the `Doc` objects it will process.
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model (Model): A `Model` instance or `True` allocate one later.
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**cfg: Config parameters.
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EXAMPLE:
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>>> from spacy.pipeline import TokenVectorEncoder
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>>> tok2vec = TokenVectorEncoder(nlp.vocab)
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>>> tok2vec.model = tok2vec.Model(128, 5000)
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"""
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self.vocab = vocab
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self.model = model
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self.input_models = []
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self.cfg = dict(cfg)
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self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
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self.cfg.setdefault('cnn_maxout_pieces', 3)
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def __call__(self, doc):
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"""Add context-sensitive vectors to a `Doc`, e.g. from a CNN or LSTM
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model. Vectors are set to the `Doc.tensor` attribute.
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docs (Doc or iterable): One or more documents to add vectors to.
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RETURNS (dict or None): Intermediate computations.
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"""
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tokvecses = self.predict([doc])
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self.set_annotations([doc], tokvecses)
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return doc
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def pipe(self, stream, batch_size=128, n_threads=-1):
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"""Process `Doc` objects as a stream.
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stream (iterator): A sequence of `Doc` objects to process.
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batch_size (int): Number of `Doc` objects to group.
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n_threads (int): Number of threads.
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YIELDS (iterator): A sequence of `Doc` objects, in order of input.
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"""
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for docs in cytoolz.partition_all(batch_size, stream):
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docs = list(docs)
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tensors = self.predict(docs)
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self.set_annotations(docs, tensors)
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yield from docs
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def predict(self, docs):
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"""Return a single tensor for a batch of documents.
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docs (iterable): A sequence of `Doc` objects.
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RETURNS (object): Vector representations for each token in the docs.
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"""
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inputs = self.model.ops.flatten([doc.tensor for doc in docs])
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outputs = self.model(inputs)
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return self.model.ops.unflatten(outputs, [len(d) for d in docs])
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def set_annotations(self, docs, tensors):
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"""Set the tensor attribute for a batch of documents.
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docs (iterable): A sequence of `Doc` objects.
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tensors (object): Vector representation for each token in the docs.
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"""
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for doc, tensor in zip(docs, tensors):
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assert tensor.shape[0] == len(doc)
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doc.tensor = tensor
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def update(self, docs, golds, state=None, drop=0., sgd=None, losses=None):
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"""Update the model.
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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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"""
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if isinstance(docs, Doc):
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docs = [docs]
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inputs = []
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bp_inputs = []
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for tok2vec in self.input_models:
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tensor, bp_tensor = tok2vec.begin_update(docs, drop=drop)
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inputs.append(tensor)
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bp_inputs.append(bp_tensor)
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inputs = self.model.ops.xp.hstack(inputs)
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scores, bp_scores = self.model.begin_update(inputs, drop=drop)
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loss, d_scores = self.get_loss(docs, golds, scores)
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d_inputs = bp_scores(d_scores, sgd=sgd)
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d_inputs = self.model.ops.xp.split(d_inputs, len(self.input_models), axis=1)
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for d_input, bp_input in zip(d_inputs, bp_inputs):
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bp_input(d_input, sgd=sgd)
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if losses is not None:
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losses.setdefault(self.name, 0.)
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losses[self.name] += loss
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return loss
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def get_loss(self, docs, golds, prediction):
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target = []
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i = 0
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for doc in docs:
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vectors = self.model.ops.xp.vstack([w.vector for w in doc])
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target.append(vectors)
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target = self.model.ops.xp.vstack(target)
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d_scores = (prediction - target) / prediction.shape[0]
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loss = (d_scores**2).sum()
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return loss, d_scores
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def begin_training(self, gold_tuples=tuple(), pipeline=None, sgd=None,
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**kwargs):
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"""Allocate models, pre-process training data and acquire an
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optimizer.
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gold_tuples (iterable): Gold-standard training data.
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pipeline (list): The pipeline the model is part of.
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"""
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for name, model in pipeline:
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if getattr(model, 'tok2vec', None):
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self.input_models.append(model.tok2vec)
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if self.model is True:
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self.cfg['input_size'] = 384
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self.cfg['output_size'] = 300
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self.model = self.Model(**self.cfg)
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link_vectors_to_models(self.vocab)
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if sgd is None:
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sgd = self.create_optimizer()
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return sgd
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class Tagger(Pipe):
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name = 'tagger'
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def __init__(self, vocab, model=True, **cfg):
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self.vocab = vocab
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self.model = model
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self.cfg = OrderedDict(sorted(cfg.items()))
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self.cfg.setdefault('cnn_maxout_pieces', 2)
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self.cfg.setdefault('pretrained_dims',
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self.vocab.vectors.data.shape[1])
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@property
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def labels(self):
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return self.vocab.morphology.tag_names
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@property
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def tok2vec(self):
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if self.model in (None, True, False):
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return None
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else:
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return chain(self.model.tok2vec, flatten)
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def __call__(self, doc):
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tags, tokvecs = self.predict([doc])
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self.set_annotations([doc], tags, tensors=tokvecs)
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return doc
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def pipe(self, stream, batch_size=128, n_threads=-1):
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for docs in cytoolz.partition_all(batch_size, stream):
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docs = list(docs)
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tag_ids, tokvecs = self.predict(docs)
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self.set_annotations(docs, tag_ids, tensors=tokvecs)
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yield from docs
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def predict(self, docs):
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tokvecs = self.model.tok2vec(docs)
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scores = self.model.softmax(tokvecs)
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guesses = []
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for doc_scores in scores:
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doc_guesses = doc_scores.argmax(axis=1)
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if not isinstance(doc_guesses, numpy.ndarray):
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doc_guesses = doc_guesses.get()
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guesses.append(doc_guesses)
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return guesses, tokvecs
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def set_annotations(self, docs, batch_tag_ids, tensors=None):
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if isinstance(docs, Doc):
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docs = [docs]
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cdef Doc doc
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cdef int idx = 0
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cdef Vocab vocab = self.vocab
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for i, doc in enumerate(docs):
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doc_tag_ids = batch_tag_ids[i]
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if hasattr(doc_tag_ids, 'get'):
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doc_tag_ids = doc_tag_ids.get()
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for j, tag_id in enumerate(doc_tag_ids):
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# Don't clobber preset POS tags
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if doc.c[j].tag == 0 and doc.c[j].pos == 0:
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# Don't clobber preset lemmas
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lemma = doc.c[j].lemma
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vocab.morphology.assign_tag_id(&doc.c[j], tag_id)
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if lemma != 0 and lemma != doc.c[j].lex.orth:
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doc.c[j].lemma = lemma
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idx += 1
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if tensors is not None:
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if isinstance(doc.tensor, numpy.ndarray) \
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and not isinstance(tensors[i], numpy.ndarray):
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doc.extend_tensor(tensors[i].get())
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else:
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doc.extend_tensor(tensors[i])
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doc.is_tagged = True
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def update(self, docs, golds, drop=0., sgd=None, losses=None):
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if losses is not None and self.name not in losses:
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losses[self.name] = 0.
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tag_scores, bp_tag_scores = self.model.begin_update(docs, drop=drop)
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loss, d_tag_scores = self.get_loss(docs, golds, tag_scores)
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bp_tag_scores(d_tag_scores, sgd=sgd)
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if losses is not None:
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losses[self.name] += loss
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def get_loss(self, docs, golds, scores):
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scores = self.model.ops.flatten(scores)
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tag_index = {tag: i for i, tag in enumerate(self.labels)}
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cdef int idx = 0
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correct = numpy.zeros((scores.shape[0],), dtype='i')
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guesses = scores.argmax(axis=1)
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for gold in golds:
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for tag in gold.tags:
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if tag is None:
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correct[idx] = guesses[idx]
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else:
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correct[idx] = tag_index[tag]
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idx += 1
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correct = self.model.ops.xp.array(correct, dtype='i')
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d_scores = scores - to_categorical(correct, nb_classes=scores.shape[1])
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d_scores /= d_scores.shape[0]
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loss = (d_scores**2).sum()
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d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs])
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return float(loss), d_scores
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def begin_training(self, gold_tuples=tuple(), pipeline=None, sgd=None,
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**kwargs):
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orig_tag_map = dict(self.vocab.morphology.tag_map)
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new_tag_map = OrderedDict()
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for raw_text, annots_brackets in gold_tuples:
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for annots, brackets in annots_brackets:
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ids, words, tags, heads, deps, ents = annots
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for tag in tags:
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if tag in orig_tag_map:
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new_tag_map[tag] = orig_tag_map[tag]
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else:
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new_tag_map[tag] = {POS: X}
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cdef Vocab vocab = self.vocab
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if new_tag_map:
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vocab.morphology = Morphology(vocab.strings, new_tag_map,
|
|
vocab.morphology.lemmatizer,
|
|
exc=vocab.morphology.exc)
|
|
if self.model is True:
|
|
self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
|
|
self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
|
|
link_vectors_to_models(self.vocab)
|
|
if sgd is None:
|
|
sgd = self.create_optimizer()
|
|
return sgd
|
|
|
|
@classmethod
|
|
def Model(cls, n_tags, **cfg):
|
|
return build_tagger_model(n_tags, **cfg)
|
|
|
|
def add_label(self, label, values=None):
|
|
if label in self.labels:
|
|
return 0
|
|
if self.model not in (True, False, None):
|
|
# Here's how the model resizing will work, once the
|
|
# neuron-to-tag mapping is no longer controlled by
|
|
# the Morphology class, which sorts the tag names.
|
|
# The sorting makes adding labels difficult.
|
|
# smaller = self.model._layers[-1]
|
|
# larger = Softmax(len(self.labels)+1, smaller.nI)
|
|
# copy_array(larger.W[:smaller.nO], smaller.W)
|
|
# copy_array(larger.b[:smaller.nO], smaller.b)
|
|
# self.model._layers[-1] = larger
|
|
raise ValueError(
|
|
"Resizing pre-trained Tagger models is not "
|
|
"currently supported.")
|
|
tag_map = dict(self.vocab.morphology.tag_map)
|
|
if values is None:
|
|
values = {POS: "X"}
|
|
tag_map[label] = values
|
|
self.vocab.morphology = Morphology(
|
|
self.vocab.strings, tag_map=tag_map,
|
|
lemmatizer=self.vocab.morphology.lemmatizer,
|
|
exc=self.vocab.morphology.exc)
|
|
return 1
|
|
|
|
def use_params(self, params):
|
|
with self.model.use_params(params):
|
|
yield
|
|
|
|
def to_bytes(self, **exclude):
|
|
serialize = OrderedDict()
|
|
if self.model in (None, True, False):
|
|
serialize['model'] = lambda: self.model
|
|
else:
|
|
serialize['model'] = self.model.to_bytes
|
|
serialize['vocab'] = self.vocab.to_bytes
|
|
serialize['cfg'] = lambda: ujson.dumps(self.cfg)
|
|
tag_map = OrderedDict(sorted(self.vocab.morphology.tag_map.items()))
|
|
serialize['tag_map'] = lambda: msgpack.dumps(
|
|
tag_map, use_bin_type=True, encoding='utf8')
|
|
return util.to_bytes(serialize, exclude)
|
|
|
|
def from_bytes(self, bytes_data, **exclude):
|
|
def load_model(b):
|
|
if self.model is True:
|
|
token_vector_width = util.env_opt(
|
|
'token_vector_width',
|
|
self.cfg.get('token_vector_width', 128))
|
|
self.model = self.Model(self.vocab.morphology.n_tags,
|
|
**self.cfg)
|
|
self.model.from_bytes(b)
|
|
|
|
def load_tag_map(b):
|
|
tag_map = msgpack.loads(b, encoding='utf8')
|
|
self.vocab.morphology = Morphology(
|
|
self.vocab.strings, tag_map=tag_map,
|
|
lemmatizer=self.vocab.morphology.lemmatizer,
|
|
exc=self.vocab.morphology.exc)
|
|
|
|
deserialize = OrderedDict((
|
|
('vocab', lambda b: self.vocab.from_bytes(b)),
|
|
('tag_map', load_tag_map),
|
|
('cfg', lambda b: self.cfg.update(ujson.loads(b))),
|
|
('model', lambda b: load_model(b)),
|
|
))
|
|
util.from_bytes(bytes_data, deserialize, exclude)
|
|
return self
|
|
|
|
def to_disk(self, path, **exclude):
|
|
self.cfg.setdefault('pretrained_dims', self.vocab.vectors.data.shape[1])
|
|
tag_map = OrderedDict(sorted(self.vocab.morphology.tag_map.items()))
|
|
serialize = OrderedDict((
|
|
('vocab', lambda p: self.vocab.to_disk(p)),
|
|
('tag_map', lambda p: p.open('wb').write(msgpack.dumps(
|
|
tag_map, use_bin_type=True, encoding='utf8'))),
|
|
('model', lambda p: p.open('wb').write(self.model.to_bytes())),
|
|
('cfg', lambda p: p.open('w').write(json_dumps(self.cfg)))
|
|
))
|
|
util.to_disk(path, serialize, exclude)
|
|
|
|
def from_disk(self, path, **exclude):
|
|
def load_model(p):
|
|
if self.model is True:
|
|
self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
|
|
with p.open('rb') as file_:
|
|
self.model.from_bytes(file_.read())
|
|
|
|
def load_tag_map(p):
|
|
with p.open('rb') as file_:
|
|
tag_map = msgpack.loads(file_.read(), encoding='utf8')
|
|
self.vocab.morphology = Morphology(
|
|
self.vocab.strings, tag_map=tag_map,
|
|
lemmatizer=self.vocab.morphology.lemmatizer,
|
|
exc=self.vocab.morphology.exc)
|
|
|
|
deserialize = OrderedDict((
|
|
('cfg', lambda p: self.cfg.update(_load_cfg(p))),
|
|
('vocab', lambda p: self.vocab.from_disk(p)),
|
|
('tag_map', load_tag_map),
|
|
('model', load_model),
|
|
))
|
|
util.from_disk(path, deserialize, exclude)
|
|
return self
|
|
|
|
|
|
class MultitaskObjective(Tagger):
|
|
"""Experimental: Assist training of a parser or tagger, by training a
|
|
side-objective.
|
|
"""
|
|
name = 'nn_labeller'
|
|
|
|
def __init__(self, vocab, model=True, target='dep_tag_offset', **cfg):
|
|
self.vocab = vocab
|
|
self.model = model
|
|
if target == 'dep':
|
|
self.make_label = self.make_dep
|
|
elif target == 'tag':
|
|
self.make_label = self.make_tag
|
|
elif target == 'ent':
|
|
self.make_label = self.make_ent
|
|
elif target == 'dep_tag_offset':
|
|
self.make_label = self.make_dep_tag_offset
|
|
elif target == 'ent_tag':
|
|
self.make_label = self.make_ent_tag
|
|
elif target == 'sent_start':
|
|
self.make_label = self.make_sent_start
|
|
elif hasattr(target, '__call__'):
|
|
self.make_label = target
|
|
else:
|
|
raise ValueError("MultitaskObjective target should be function or "
|
|
"one of: dep, tag, ent, sent_start, dep_tag_offset, ent_tag.")
|
|
self.cfg = dict(cfg)
|
|
self.cfg.setdefault('cnn_maxout_pieces', 2)
|
|
self.cfg.setdefault('pretrained_dims',
|
|
self.vocab.vectors.data.shape[1])
|
|
|
|
@property
|
|
def labels(self):
|
|
return self.cfg.setdefault('labels', {})
|
|
|
|
@labels.setter
|
|
def labels(self, value):
|
|
self.cfg['labels'] = value
|
|
|
|
def set_annotations(self, docs, dep_ids, tensors=None):
|
|
pass
|
|
|
|
def begin_training(self, gold_tuples=tuple(), pipeline=None, tok2vec=None,
|
|
sgd=None, **kwargs):
|
|
gold_tuples = nonproj.preprocess_training_data(gold_tuples)
|
|
for raw_text, annots_brackets in gold_tuples:
|
|
for annots, brackets in annots_brackets:
|
|
ids, words, tags, heads, deps, ents = annots
|
|
for i in range(len(ids)):
|
|
label = self.make_label(i, words, tags, heads, deps, ents)
|
|
if label is not None and label not in self.labels:
|
|
self.labels[label] = len(self.labels)
|
|
if self.model is True:
|
|
token_vector_width = util.env_opt('token_vector_width')
|
|
self.model = self.Model(len(self.labels), tok2vec=tok2vec)
|
|
link_vectors_to_models(self.vocab)
|
|
if sgd is None:
|
|
sgd = self.create_optimizer()
|
|
return sgd
|
|
|
|
@classmethod
|
|
def Model(cls, n_tags, tok2vec=None, **cfg):
|
|
token_vector_width = util.env_opt('token_vector_width', 128)
|
|
softmax = Softmax(n_tags, token_vector_width)
|
|
model = chain(
|
|
tok2vec,
|
|
softmax
|
|
)
|
|
model.tok2vec = tok2vec
|
|
model.softmax = softmax
|
|
return model
|
|
|
|
def predict(self, docs):
|
|
tokvecs = self.model.tok2vec(docs)
|
|
scores = self.model.softmax(tokvecs)
|
|
return tokvecs, scores
|
|
|
|
def get_loss(self, docs, golds, scores):
|
|
assert len(docs) == len(golds)
|
|
cdef int idx = 0
|
|
correct = numpy.zeros((scores.shape[0],), dtype='i')
|
|
guesses = scores.argmax(axis=1)
|
|
for i, gold in enumerate(golds):
|
|
for j in range(len(docs[i])):
|
|
# Handes alignment for tokenization differences
|
|
label = self.make_label(j, gold.words, gold.tags,
|
|
gold.heads, gold.labels, gold.ents)
|
|
if label is None or label not in self.labels:
|
|
correct[idx] = guesses[idx]
|
|
else:
|
|
correct[idx] = self.labels[label]
|
|
idx += 1
|
|
correct = self.model.ops.xp.array(correct, dtype='i')
|
|
d_scores = scores - to_categorical(correct, nb_classes=scores.shape[1])
|
|
d_scores /= d_scores.shape[0]
|
|
loss = (d_scores**2).sum()
|
|
return float(loss), d_scores
|
|
|
|
@staticmethod
|
|
def make_dep(i, words, tags, heads, deps, ents):
|
|
if deps[i] is None or heads[i] is None:
|
|
return None
|
|
return deps[i]
|
|
|
|
@staticmethod
|
|
def make_tag(i, words, tags, heads, deps, ents):
|
|
return tags[i]
|
|
|
|
@staticmethod
|
|
def make_ent(i, words, tags, heads, deps, ents):
|
|
if ents is None:
|
|
return None
|
|
return ents[i]
|
|
|
|
@staticmethod
|
|
def make_dep_tag_offset(i, words, tags, heads, deps, ents):
|
|
if deps[i] is None or heads[i] is None:
|
|
return None
|
|
offset = heads[i] - i
|
|
offset = min(offset, 2)
|
|
offset = max(offset, -2)
|
|
return '%s-%s:%d' % (deps[i], tags[i], offset)
|
|
|
|
@staticmethod
|
|
def make_ent_tag(i, words, tags, heads, deps, ents):
|
|
if ents is None or ents[i] is None:
|
|
return None
|
|
else:
|
|
return '%s-%s' % (tags[i], ents[i])
|
|
|
|
@staticmethod
|
|
def make_sent_start(target, words, tags, heads, deps, ents, cache=True, _cache={}):
|
|
'''A multi-task objective for representing sentence boundaries,
|
|
using BILU scheme. (O is impossible)
|
|
|
|
The implementation of this method uses an internal cache that relies
|
|
on the identity of the heads array, to avoid requiring a new piece
|
|
of gold data. You can pass cache=False if you know the cache will
|
|
do the wrong thing.
|
|
'''
|
|
assert len(words) == len(heads)
|
|
assert target < len(words), (target, len(words))
|
|
if cache:
|
|
if id(heads) in _cache:
|
|
return _cache[id(heads)][target]
|
|
else:
|
|
for key in list(_cache.keys()):
|
|
_cache.pop(key)
|
|
sent_tags = ['I-SENT'] * len(words)
|
|
_cache[id(heads)] = sent_tags
|
|
else:
|
|
sent_tags = ['I-SENT'] * len(words)
|
|
|
|
def _find_root(child):
|
|
seen = set([child])
|
|
while child is not None and heads[child] != child:
|
|
seen.add(child)
|
|
child = heads[child]
|
|
return child
|
|
|
|
sentences = {}
|
|
for i in range(len(words)):
|
|
root = _find_root(i)
|
|
if root is None:
|
|
sent_tags[i] = None
|
|
else:
|
|
sentences.setdefault(root, []).append(i)
|
|
for root, span in sorted(sentences.items()):
|
|
if len(span) == 1:
|
|
sent_tags[span[0]] = 'U-SENT'
|
|
else:
|
|
sent_tags[span[0]] = 'B-SENT'
|
|
sent_tags[span[-1]] = 'L-SENT'
|
|
return sent_tags[target]
|
|
|
|
|
|
class SimilarityHook(Pipe):
|
|
"""
|
|
Experimental: A pipeline component to install a hook for supervised
|
|
similarity into `Doc` objects. Requires a `Tensorizer` to pre-process
|
|
documents. The similarity model can be any object obeying the Thinc `Model`
|
|
interface. By default, the model concatenates the elementwise mean and
|
|
elementwise max of the two tensors, and compares them using the
|
|
Cauchy-like similarity function from Chen (2013):
|
|
|
|
>>> similarity = 1. / (1. + (W * (vec1-vec2)**2).sum())
|
|
|
|
Where W is a vector of dimension weights, initialized to 1.
|
|
"""
|
|
name = 'similarity'
|
|
|
|
def __init__(self, vocab, model=True, **cfg):
|
|
self.vocab = vocab
|
|
self.model = model
|
|
self.cfg = dict(cfg)
|
|
|
|
@classmethod
|
|
def Model(cls, length):
|
|
return Siamese(Pooling(max_pool, mean_pool), CauchySimilarity(length))
|
|
|
|
def __call__(self, doc):
|
|
"""Install similarity hook"""
|
|
doc.user_hooks['similarity'] = self.predict
|
|
return doc
|
|
|
|
def pipe(self, docs, **kwargs):
|
|
for doc in docs:
|
|
yield self(doc)
|
|
|
|
def predict(self, doc1, doc2):
|
|
return self.model.predict([(doc1, doc2)])
|
|
|
|
def update(self, doc1_doc2, golds, sgd=None, drop=0.):
|
|
sims, bp_sims = self.model.begin_update(doc1_doc2, drop=drop)
|
|
|
|
def begin_training(self, _=tuple(), pipeline=None, sgd=None, **kwargs):
|
|
"""Allocate model, using width from tensorizer in pipeline.
|
|
|
|
gold_tuples (iterable): Gold-standard training data.
|
|
pipeline (list): The pipeline the model is part of.
|
|
"""
|
|
if self.model is True:
|
|
self.model = self.Model(pipeline[0].model.nO)
|
|
link_vectors_to_models(self.vocab)
|
|
if sgd is None:
|
|
sgd = self.create_optimizer()
|
|
return sgd
|
|
|
|
|
|
class TextCategorizer(Pipe):
|
|
name = 'textcat'
|
|
|
|
@classmethod
|
|
def Model(cls, nr_class=1, width=64, **cfg):
|
|
return build_text_classifier(nr_class, width, **cfg)
|
|
|
|
def __init__(self, vocab, model=True, **cfg):
|
|
self.vocab = vocab
|
|
self.model = model
|
|
self.cfg = dict(cfg)
|
|
|
|
@property
|
|
def labels(self):
|
|
return self.cfg.setdefault('labels', [])
|
|
|
|
@labels.setter
|
|
def labels(self, value):
|
|
self.cfg['labels'] = value
|
|
|
|
def __call__(self, doc):
|
|
scores, tensors = self.predict([doc])
|
|
self.set_annotations([doc], scores, tensors=tensors)
|
|
return doc
|
|
|
|
def pipe(self, stream, batch_size=128, n_threads=-1):
|
|
for docs in cytoolz.partition_all(batch_size, stream):
|
|
docs = list(docs)
|
|
scores, tensors = self.predict(docs)
|
|
self.set_annotations(docs, scores, tensors=tensors)
|
|
yield from docs
|
|
|
|
def predict(self, docs):
|
|
scores = self.model(docs)
|
|
scores = self.model.ops.asarray(scores)
|
|
tensors = [doc.tensor for doc in docs]
|
|
return scores, tensors
|
|
|
|
def set_annotations(self, docs, scores, tensors=None):
|
|
for i, doc in enumerate(docs):
|
|
for j, label in enumerate(self.labels):
|
|
doc.cats[label] = float(scores[i, j])
|
|
|
|
def update(self, docs, golds, state=None, drop=0., sgd=None, losses=None):
|
|
scores, bp_scores = self.model.begin_update(docs, drop=drop)
|
|
loss, d_scores = self.get_loss(docs, golds, scores)
|
|
bp_scores(d_scores, sgd=sgd)
|
|
if losses is not None:
|
|
losses.setdefault(self.name, 0.0)
|
|
losses[self.name] += loss
|
|
|
|
def get_loss(self, docs, golds, scores):
|
|
truths = numpy.zeros((len(golds), len(self.labels)), dtype='f')
|
|
not_missing = numpy.ones((len(golds), len(self.labels)), dtype='f')
|
|
for i, gold in enumerate(golds):
|
|
for j, label in enumerate(self.labels):
|
|
if label in gold.cats:
|
|
truths[i, j] = gold.cats[label]
|
|
else:
|
|
not_missing[i, j] = 0.
|
|
truths = self.model.ops.asarray(truths)
|
|
not_missing = self.model.ops.asarray(not_missing)
|
|
d_scores = (scores-truths) / scores.shape[0]
|
|
d_scores *= not_missing
|
|
mean_square_error = ((scores-truths)**2).sum(axis=1).mean()
|
|
return mean_square_error, d_scores
|
|
|
|
def add_label(self, label):
|
|
if label in self.labels:
|
|
return 0
|
|
if self.model not in (None, True, False):
|
|
smaller = self.model._layers[-1]
|
|
larger = Affine(len(self.labels)+1, smaller.nI)
|
|
copy_array(larger.W[:smaller.nO], smaller.W)
|
|
copy_array(larger.b[:smaller.nO], smaller.b)
|
|
self.model._layers[-1] = larger
|
|
self.labels.append(label)
|
|
return 1
|
|
|
|
def begin_training(self, gold_tuples=tuple(), pipeline=None, sgd=None):
|
|
if pipeline and getattr(pipeline[0], 'name', None) == 'tensorizer':
|
|
token_vector_width = pipeline[0].model.nO
|
|
else:
|
|
token_vector_width = 64
|
|
if self.model is True:
|
|
self.cfg['pretrained_dims'] = self.vocab.vectors_length
|
|
self.model = self.Model(len(self.labels), token_vector_width,
|
|
**self.cfg)
|
|
link_vectors_to_models(self.vocab)
|
|
if sgd is None:
|
|
sgd = self.create_optimizer()
|
|
return sgd
|
|
|
|
|
|
cdef class DependencyParser(Parser):
|
|
name = 'parser'
|
|
TransitionSystem = ArcEager
|
|
|
|
@property
|
|
def postprocesses(self):
|
|
return [nonproj.deprojectivize]
|
|
|
|
def add_multitask_objective(self, target):
|
|
labeller = MultitaskObjective(self.vocab, target=target)
|
|
self._multitasks.append(labeller)
|
|
|
|
def init_multitask_objectives(self, gold_tuples, pipeline, sgd=None, **cfg):
|
|
for labeller in self._multitasks:
|
|
tok2vec = self.model[0]
|
|
labeller.begin_training(gold_tuples, pipeline=pipeline,
|
|
tok2vec=tok2vec, sgd=sgd)
|
|
|
|
def __reduce__(self):
|
|
return (DependencyParser, (self.vocab, self.moves, self.model),
|
|
None, None)
|
|
|
|
|
|
cdef class EntityRecognizer(Parser):
|
|
name = 'ner'
|
|
TransitionSystem = BiluoPushDown
|
|
|
|
nr_feature = 6
|
|
|
|
def add_multitask_objective(self, target):
|
|
labeller = MultitaskObjective(self.vocab, target=target)
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self._multitasks.append(labeller)
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def init_multitask_objectives(self, gold_tuples, pipeline, sgd=None, **cfg):
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for labeller in self._multitasks:
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tok2vec = self.model[0]
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labeller.begin_training(gold_tuples, pipeline=pipeline,
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tok2vec=tok2vec)
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def __reduce__(self):
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return (EntityRecognizer, (self.vocab, self.moves, self.model),
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None, None)
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__all__ = ['Tagger', 'DependencyParser', 'EntityRecognizer', 'Tensorizer']
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