mirror of
https://github.com/explosion/spaCy.git
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7b33b2854f
* Add Armenian sentence-final verchaket * Add Greek and Arabic question marks, and contributor agreement * Check box
1652 lines
64 KiB
Cython
1652 lines
64 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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import srsly
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import random
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import warnings
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from collections import OrderedDict
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from thinc.api import chain
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from thinc.v2v import Affine, Maxout, Softmax
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from thinc.misc import LayerNorm
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from thinc.neural.util import to_categorical
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from thinc.neural.util import get_array_module
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from ..compat import basestring_
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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.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 .functions import merge_subtokens
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from ..language import Language, component
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from ..syntax import nonproj
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from ..attrs import POS, ID
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from ..parts_of_speech import X
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from ..kb import KnowledgeBase
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from .._ml import Tok2Vec, build_tagger_model, cosine, get_cossim_loss
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from .._ml import build_text_classifier, build_simple_cnn_text_classifier
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from .._ml import build_bow_text_classifier, build_nel_encoder
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from .._ml import link_vectors_to_models, zero_init, flatten
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from .._ml import masked_language_model, create_default_optimizer, get_cossim_loss
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from .._ml import MultiSoftmax, get_characters_loss
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from ..errors import Errors, TempErrors, Warnings
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from .. import util
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def _load_cfg(path):
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if path.exists():
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return srsly.read_json(path)
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else:
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return {}
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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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@classmethod
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def from_nlp(cls, nlp, **cfg):
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return cls(nlp.vocab, **cfg)
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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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self.require_model()
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predictions = self.predict([doc])
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if isinstance(predictions, tuple) and len(predictions) == 2:
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scores, tensors = predictions
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self.set_annotations([doc], scores, tensors=tensors)
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else:
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self.set_annotations([doc], predictions)
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return doc
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def require_model(self):
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"""Raise an error if the component's model is not initialized."""
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if getattr(self, "model", None) in (None, True, False):
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raise ValueError(Errors.E109.format(name=self.name))
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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 util.minibatch(stream, size=batch_size):
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docs = list(docs)
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predictions = self.predict(docs)
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if isinstance(predictions, tuple) and len(tuple) == 2:
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scores, tensors = predictions
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self.set_annotations(docs, scores, tensors=tensors)
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else:
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self.set_annotations(docs, predictions)
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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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self.require_model()
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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=0.0, 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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pass
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def rehearse(self, docs, sgd=None, losses=None, **config):
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pass
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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 pretrained 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, **self.cfg.get("optimizer", {}))
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def begin_training(
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self, get_gold_tuples=lambda: [], pipeline=None, sgd=None, **kwargs
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):
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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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if hasattr(self, "vocab"):
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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=tuple(), **kwargs):
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"""Serialize the pipe to a bytestring.
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exclude (list): String names of serialization fields to exclude.
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RETURNS (bytes): The serialized object.
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"""
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serialize = OrderedDict()
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serialize["cfg"] = lambda: srsly.json_dumps(self.cfg)
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if self.model not in (True, False, None):
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serialize["model"] = self.model.to_bytes
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if hasattr(self, "vocab"):
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serialize["vocab"] = self.vocab.to_bytes
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exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
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return util.to_bytes(serialize, exclude)
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def from_bytes(self, bytes_data, exclude=tuple(), **kwargs):
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"""Load the pipe from a bytestring."""
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def load_model(b):
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# TODO: Remove this once we don't have to handle previous models
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if self.cfg.get("pretrained_dims") and "pretrained_vectors" not in self.cfg:
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self.cfg["pretrained_vectors"] = self.vocab.vectors.name
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if self.model is True:
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self.model = self.Model(**self.cfg)
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try:
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self.model.from_bytes(b)
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except AttributeError:
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raise ValueError(Errors.E149)
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deserialize = OrderedDict()
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deserialize["cfg"] = lambda b: self.cfg.update(srsly.json_loads(b))
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if hasattr(self, "vocab"):
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deserialize["vocab"] = lambda b: self.vocab.from_bytes(b)
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deserialize["model"] = load_model
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exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
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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=tuple(), **kwargs):
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"""Serialize the pipe to disk."""
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serialize = OrderedDict()
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serialize["cfg"] = lambda p: srsly.write_json(p, 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: self.model.to_disk(p)
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exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
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util.to_disk(path, serialize, exclude)
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def from_disk(self, path, exclude=tuple(), **kwargs):
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"""Load the pipe from disk."""
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def load_model(p):
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# TODO: Remove this once we don't have to handle previous models
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if self.cfg.get("pretrained_dims") and "pretrained_vectors" not in self.cfg:
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self.cfg["pretrained_vectors"] = self.vocab.vectors.name
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if self.model is True:
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self.model = self.Model(**self.cfg)
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try:
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self.model.from_bytes(p.open("rb").read())
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except AttributeError:
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raise ValueError(Errors.E149)
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deserialize = OrderedDict()
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deserialize["cfg"] = lambda p: self.cfg.update(_load_cfg(p))
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deserialize["vocab"] = lambda p: self.vocab.from_disk(p)
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deserialize["model"] = load_model
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exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
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util.from_disk(path, deserialize, exclude)
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return self
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@component("tensorizer", assigns=["doc.tensor"])
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class Tensorizer(Pipe):
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"""Pre-train position-sensitive vectors for tokens."""
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@classmethod
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def Model(cls, output_size=300, **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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input_size = util.env_opt("token_vector_width", cfg.get("input_size", 96))
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return zero_init(Affine(output_size, input_size, drop_factor=0.0))
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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` to 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.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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YIELDS (iterator): A sequence of `Doc` objects, in order of input.
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"""
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for docs in util.minibatch(stream, size=batch_size):
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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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self.require_model()
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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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if tensor.shape[0] != len(doc):
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raise ValueError(Errors.E076.format(rows=tensor.shape[0], words=len(doc)))
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doc.tensor = tensor
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def update(self, docs, golds, state=None, drop=0.0, 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 dropout 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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self.require_model()
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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, 0.0)
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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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ids = self.model.ops.flatten([doc.to_array(ID).ravel() for doc in docs])
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target = self.vocab.vectors.data[ids]
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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=lambda: [], pipeline=None, sgd=None, **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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if pipeline is not None:
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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.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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@component("tagger", assigns=["token.tag", "token.pos", "token.lemma"])
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class Tagger(Pipe):
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"""Pipeline component for part-of-speech tagging.
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DOCS: https://spacy.io/api/tagger
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"""
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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._rehearsal_model = None
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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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@property
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def labels(self):
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return tuple(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 util.minibatch(stream, size=batch_size):
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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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self.require_model()
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if not any(len(doc) for doc in docs):
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# Handle cases where there are no tokens in any docs.
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n_labels = len(self.labels)
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guesses = [self.model.ops.allocate((0, n_labels)) for doc in docs]
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tokvecs = self.model.ops.allocate((0, self.model.tok2vec.nO))
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return guesses, tokvecs
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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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assign_morphology = self.cfg.get("set_morphology", True)
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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:
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if doc.c[j].pos == 0 and assign_morphology:
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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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else:
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doc.c[j].tag = self.vocab.strings[self.labels[tag_id]]
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idx += 1
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if tensors is not None and len(tensors):
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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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|
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def update(self, docs, golds, drop=0., sgd=None, losses=None):
|
||
self.require_model()
|
||
if losses is not None and self.name not in losses:
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||
losses[self.name] = 0.
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||
|
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if not any(len(doc) for doc in docs):
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# Handle cases where there are no tokens in any docs.
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return
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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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|
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def rehearse(self, docs, drop=0., sgd=None, losses=None):
|
||
"""Perform a 'rehearsal' update, where we try to match the output of
|
||
an initial model.
|
||
"""
|
||
if self._rehearsal_model is None:
|
||
return
|
||
if not any(len(doc) for doc in docs):
|
||
# Handle cases where there are no tokens in any docs.
|
||
return
|
||
guesses, backprop = self.model.begin_update(docs, drop=drop)
|
||
target = self._rehearsal_model(docs)
|
||
gradient = guesses - target
|
||
backprop(gradient, sgd=sgd)
|
||
if losses is not None:
|
||
losses.setdefault(self.name, 0.0)
|
||
losses[self.name] += (gradient**2).sum()
|
||
|
||
def get_loss(self, docs, golds, scores):
|
||
scores = self.model.ops.flatten(scores)
|
||
tag_index = {tag: i for i, tag in enumerate(self.labels)}
|
||
cdef int idx = 0
|
||
correct = numpy.zeros((scores.shape[0],), dtype="i")
|
||
guesses = scores.argmax(axis=1)
|
||
known_labels = numpy.ones((scores.shape[0], 1), dtype="f")
|
||
for gold in golds:
|
||
for tag in gold.tags:
|
||
if tag is None:
|
||
correct[idx] = guesses[idx]
|
||
elif tag in tag_index:
|
||
correct[idx] = tag_index[tag]
|
||
else:
|
||
correct[idx] = 0
|
||
known_labels[idx] = 0.
|
||
idx += 1
|
||
correct = self.model.ops.xp.array(correct, dtype="i")
|
||
d_scores = scores - to_categorical(correct, nb_classes=scores.shape[1])
|
||
d_scores *= self.model.ops.asarray(known_labels)
|
||
loss = (d_scores**2).sum()
|
||
d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs])
|
||
return float(loss), d_scores
|
||
|
||
def begin_training(self, get_gold_tuples=lambda: [], pipeline=None, sgd=None,
|
||
**kwargs):
|
||
lemma_tables = ["lemma_rules", "lemma_index", "lemma_exc", "lemma_lookup"]
|
||
if not any(table in self.vocab.lookups for table in lemma_tables):
|
||
warnings.warn(Warnings.W022)
|
||
if len(self.vocab.lookups.get_table("lexeme_norm", {})) == 0:
|
||
warnings.warn(Warnings.W033.format(model="part-of-speech tagger"))
|
||
try:
|
||
import spacy_lookups_data
|
||
except ImportError:
|
||
if self.vocab.lang in ("da", "de", "el", "en", "id", "lb", "pt",
|
||
"ru", "sr", "ta", "th"):
|
||
warnings.warn(Warnings.W034.format(lang=self.vocab.lang))
|
||
orig_tag_map = dict(self.vocab.morphology.tag_map)
|
||
new_tag_map = OrderedDict()
|
||
for raw_text, annots_brackets in get_gold_tuples():
|
||
for annots, brackets in annots_brackets:
|
||
ids, words, tags, heads, deps, ents = annots
|
||
for tag in tags:
|
||
if tag in orig_tag_map:
|
||
new_tag_map[tag] = orig_tag_map[tag]
|
||
else:
|
||
new_tag_map[tag] = {POS: X}
|
||
cdef Vocab vocab = self.vocab
|
||
if new_tag_map:
|
||
if "_SP" in orig_tag_map:
|
||
new_tag_map["_SP"] = orig_tag_map["_SP"]
|
||
vocab.morphology = Morphology(vocab.strings, new_tag_map,
|
||
vocab.morphology.lemmatizer,
|
||
exc=vocab.morphology.exc)
|
||
self.cfg["pretrained_vectors"] = kwargs.get("pretrained_vectors")
|
||
if self.model is True:
|
||
for hp in ["token_vector_width", "conv_depth"]:
|
||
if hp in kwargs:
|
||
self.cfg[hp] = kwargs[hp]
|
||
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):
|
||
if cfg.get("pretrained_dims") and not cfg.get("pretrained_vectors"):
|
||
raise ValueError(TempErrors.T008)
|
||
return build_tagger_model(n_tags, **cfg)
|
||
|
||
def add_label(self, label, values=None):
|
||
if not isinstance(label, basestring_):
|
||
raise ValueError(Errors.E187)
|
||
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(TempErrors.T003)
|
||
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=tuple(), **kwargs):
|
||
serialize = OrderedDict()
|
||
if self.model not in (None, True, False):
|
||
serialize["model"] = self.model.to_bytes
|
||
serialize["vocab"] = self.vocab.to_bytes
|
||
serialize["cfg"] = lambda: srsly.json_dumps(self.cfg)
|
||
tag_map = OrderedDict(sorted(self.vocab.morphology.tag_map.items()))
|
||
serialize["tag_map"] = lambda: srsly.msgpack_dumps(tag_map)
|
||
exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
|
||
return util.to_bytes(serialize, exclude)
|
||
|
||
def from_bytes(self, bytes_data, exclude=tuple(), **kwargs):
|
||
def load_model(b):
|
||
# TODO: Remove this once we don't have to handle previous models
|
||
if self.cfg.get("pretrained_dims") and "pretrained_vectors" not in self.cfg:
|
||
self.cfg["pretrained_vectors"] = self.vocab.vectors.name
|
||
if self.model is True:
|
||
token_vector_width = util.env_opt(
|
||
"token_vector_width",
|
||
self.cfg.get("token_vector_width", 96))
|
||
self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
|
||
try:
|
||
self.model.from_bytes(b)
|
||
except AttributeError:
|
||
raise ValueError(Errors.E149)
|
||
|
||
def load_tag_map(b):
|
||
tag_map = srsly.msgpack_loads(b)
|
||
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(srsly.json_loads(b))),
|
||
("model", lambda b: load_model(b)),
|
||
))
|
||
exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
|
||
util.from_bytes(bytes_data, deserialize, exclude)
|
||
return self
|
||
|
||
def to_disk(self, path, exclude=tuple(), **kwargs):
|
||
tag_map = OrderedDict(sorted(self.vocab.morphology.tag_map.items()))
|
||
serialize = OrderedDict((
|
||
("vocab", lambda p: self.vocab.to_disk(p)),
|
||
("tag_map", lambda p: srsly.write_msgpack(p, tag_map)),
|
||
("model", lambda p: self.model.to_disk(p)),
|
||
("cfg", lambda p: srsly.write_json(p, self.cfg))
|
||
))
|
||
exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
|
||
util.to_disk(path, serialize, exclude)
|
||
|
||
def from_disk(self, path, exclude=tuple(), **kwargs):
|
||
def load_model(p):
|
||
# TODO: Remove this once we don't have to handle previous models
|
||
if self.cfg.get("pretrained_dims") and "pretrained_vectors" not in self.cfg:
|
||
self.cfg["pretrained_vectors"] = self.vocab.vectors.name
|
||
if self.model is True:
|
||
self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
|
||
with p.open("rb") as file_:
|
||
try:
|
||
self.model.from_bytes(file_.read())
|
||
except AttributeError:
|
||
raise ValueError(Errors.E149)
|
||
|
||
def load_tag_map(p):
|
||
tag_map = srsly.read_msgpack(p)
|
||
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),
|
||
))
|
||
exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
|
||
util.from_disk(path, deserialize, exclude)
|
||
return self
|
||
|
||
|
||
@component("nn_labeller")
|
||
class MultitaskObjective(Tagger):
|
||
"""Experimental: Assist training of a parser or tagger, by training a
|
||
side-objective.
|
||
"""
|
||
|
||
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(Errors.E016)
|
||
self.cfg = dict(cfg)
|
||
self.cfg.setdefault("cnn_maxout_pieces", 2)
|
||
|
||
@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, get_gold_tuples=lambda: [], pipeline=None, tok2vec=None,
|
||
sgd=None, **kwargs):
|
||
gold_tuples = nonproj.preprocess_training_data(get_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", 96)
|
||
softmax = Softmax(n_tags, token_vector_width*2)
|
||
model = chain(
|
||
tok2vec,
|
||
LayerNorm(Maxout(token_vector_width*2, token_vector_width, pieces=3)),
|
||
softmax
|
||
)
|
||
model.tok2vec = tok2vec
|
||
model.softmax = softmax
|
||
return model
|
||
|
||
def predict(self, docs):
|
||
self.require_model()
|
||
tokvecs = self.model.tok2vec(docs)
|
||
scores = self.model.softmax(tokvecs)
|
||
return tokvecs, scores
|
||
|
||
def get_loss(self, docs, golds, scores):
|
||
if len(docs) != len(golds):
|
||
raise ValueError(Errors.E077.format(value="loss", n_docs=len(docs),
|
||
n_golds=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])
|
||
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 ClozeMultitask(Pipe):
|
||
@classmethod
|
||
def Model(cls, vocab, tok2vec, **cfg):
|
||
if cfg["objective"] == "characters":
|
||
out_sizes = [256] * cfg.get("nr_char", 4)
|
||
output_layer = MultiSoftmax(out_sizes)
|
||
else:
|
||
output_size = vocab.vectors.data.shape[1]
|
||
output_layer = chain(
|
||
LayerNorm(Maxout(output_size, tok2vec.nO, pieces=3)),
|
||
zero_init(Affine(output_size, output_size, drop_factor=0.0))
|
||
)
|
||
model = chain(tok2vec, output_layer)
|
||
model = masked_language_model(vocab, model)
|
||
model.tok2vec = tok2vec
|
||
model.output_layer = output_layer
|
||
return model
|
||
|
||
def __init__(self, vocab, model=True, **cfg):
|
||
self.vocab = vocab
|
||
self.model = model
|
||
self.cfg = cfg
|
||
self.cfg.setdefault("objective", "characters")
|
||
self.cfg.setdefault("nr_char", 4)
|
||
|
||
def set_annotations(self, docs, dep_ids, tensors=None):
|
||
pass
|
||
|
||
def begin_training(self, get_gold_tuples=lambda: [], pipeline=None,
|
||
tok2vec=None, sgd=None, **kwargs):
|
||
link_vectors_to_models(self.vocab)
|
||
if self.model is True:
|
||
kwargs.update(self.cfg)
|
||
self.model = self.Model(self.vocab, tok2vec, **kwargs)
|
||
X = self.model.ops.allocate((5, self.model.tok2vec.nO))
|
||
self.model.output_layer.begin_training(X)
|
||
if sgd is None:
|
||
sgd = self.create_optimizer()
|
||
return sgd
|
||
|
||
def predict(self, docs):
|
||
self.require_model()
|
||
tokvecs = self.model.tok2vec(docs)
|
||
vectors = self.model.output_layer(tokvecs)
|
||
return tokvecs, vectors
|
||
|
||
def get_loss(self, docs, vectors, prediction):
|
||
if self.cfg["objective"] == "characters":
|
||
loss, gradient = get_characters_loss(self.model.ops, docs, prediction)
|
||
else:
|
||
# The simplest way to implement this would be to vstack the
|
||
# token.vector values, but that's a bit inefficient, especially on GPU.
|
||
# Instead we fetch the index into the vectors table for each of our tokens,
|
||
# and look them up all at once. This prevents data copying.
|
||
ids = self.model.ops.flatten([doc.to_array(ID).ravel() for doc in docs])
|
||
target = vectors[ids]
|
||
loss, gradient = get_cossim_loss(prediction, target, ignore_zeros=True)
|
||
return float(loss), gradient
|
||
|
||
def update(self, docs, golds, drop=0., sgd=None, losses=None):
|
||
pass
|
||
|
||
def rehearse(self, docs, drop=0., sgd=None, losses=None):
|
||
self.require_model()
|
||
if losses is not None and self.name not in losses:
|
||
losses[self.name] = 0.
|
||
predictions, bp_predictions = self.model.begin_update(docs, drop=drop)
|
||
loss, d_predictions = self.get_loss(docs, self.vocab.vectors.data, predictions)
|
||
bp_predictions(d_predictions, sgd=sgd)
|
||
|
||
if losses is not None:
|
||
losses[self.name] += loss
|
||
|
||
@staticmethod
|
||
def decode_utf8_predictions(char_array):
|
||
# The format alternates filling from start and end, and 255 is missing
|
||
words = []
|
||
char_array = char_array.reshape((char_array.shape[0], -1, 256))
|
||
nr_char = char_array.shape[1]
|
||
char_array = char_array.argmax(axis=-1)
|
||
for row in char_array:
|
||
starts = [chr(c) for c in row[::2] if c != 255]
|
||
ends = [chr(c) for c in row[1::2] if c != 255]
|
||
word = "".join(starts + list(reversed(ends)))
|
||
words.append(word)
|
||
return words
|
||
|
||
|
||
@component("textcat", assigns=["doc.cats"])
|
||
class TextCategorizer(Pipe):
|
||
"""Pipeline component for text classification.
|
||
|
||
DOCS: https://spacy.io/api/textcategorizer
|
||
"""
|
||
|
||
@classmethod
|
||
def Model(cls, nr_class=1, **cfg):
|
||
embed_size = util.env_opt("embed_size", 2000)
|
||
if "token_vector_width" in cfg:
|
||
token_vector_width = cfg["token_vector_width"]
|
||
else:
|
||
token_vector_width = util.env_opt("token_vector_width", 96)
|
||
if cfg.get("architecture") == "simple_cnn":
|
||
tok2vec = Tok2Vec(token_vector_width, embed_size, **cfg)
|
||
return build_simple_cnn_text_classifier(tok2vec, nr_class, **cfg)
|
||
elif cfg.get("architecture") == "bow":
|
||
return build_bow_text_classifier(nr_class, **cfg)
|
||
else:
|
||
return build_text_classifier(nr_class, **cfg)
|
||
|
||
@property
|
||
def tok2vec(self):
|
||
if self.model in (None, True, False):
|
||
return None
|
||
else:
|
||
return self.model.tok2vec
|
||
|
||
def __init__(self, vocab, model=True, **cfg):
|
||
self.vocab = vocab
|
||
self.model = model
|
||
self._rehearsal_model = None
|
||
self.cfg = dict(cfg)
|
||
|
||
@property
|
||
def labels(self):
|
||
return tuple(self.cfg.setdefault("labels", []))
|
||
|
||
def require_labels(self):
|
||
"""Raise an error if the component's model has no labels defined."""
|
||
if not self.labels:
|
||
raise ValueError(Errors.E143.format(name=self.name))
|
||
|
||
@labels.setter
|
||
def labels(self, value):
|
||
self.cfg["labels"] = tuple(value)
|
||
|
||
def pipe(self, stream, batch_size=128, n_threads=-1):
|
||
for docs in util.minibatch(stream, size=batch_size):
|
||
docs = list(docs)
|
||
scores, tensors = self.predict(docs)
|
||
self.set_annotations(docs, scores, tensors=tensors)
|
||
yield from docs
|
||
|
||
def predict(self, docs):
|
||
self.require_model()
|
||
tensors = [doc.tensor for doc in docs]
|
||
|
||
if not any(len(doc) for doc in docs):
|
||
# Handle cases where there are no tokens in any docs.
|
||
xp = get_array_module(tensors)
|
||
scores = xp.zeros((len(docs), len(self.labels)))
|
||
return scores, tensors
|
||
|
||
scores = self.model(docs)
|
||
scores = self.model.ops.asarray(scores)
|
||
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):
|
||
self.require_model()
|
||
if not any(len(doc) for doc in docs):
|
||
# Handle cases where there are no tokens in any docs.
|
||
return
|
||
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 rehearse(self, docs, drop=0., sgd=None, losses=None):
|
||
if self._rehearsal_model is None:
|
||
return
|
||
if not any(len(doc) for doc in docs):
|
||
# Handle cases where there are no tokens in any docs.
|
||
return
|
||
scores, bp_scores = self.model.begin_update(docs, drop=drop)
|
||
target = self._rehearsal_model(docs)
|
||
gradient = scores - target
|
||
bp_scores(gradient, sgd=sgd)
|
||
if losses is not None:
|
||
losses.setdefault(self.name, 0.0)
|
||
losses[self.name] += (gradient**2).sum()
|
||
|
||
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 = (d_scores**2).sum(axis=1).mean()
|
||
return float(mean_square_error), d_scores
|
||
|
||
def add_label(self, label):
|
||
if not isinstance(label, basestring_):
|
||
raise ValueError(Errors.E187)
|
||
if label in self.labels:
|
||
return 0
|
||
if self.model not in (None, True, False):
|
||
# This functionality was available previously, but was broken.
|
||
# The problem is that we resize the last layer, but the last layer
|
||
# is actually just an ensemble. We're not resizing the child layers
|
||
# - a huge problem.
|
||
raise ValueError(Errors.E116)
|
||
# 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 = tuple(list(self.labels) + [label])
|
||
return 1
|
||
|
||
def begin_training(self, get_gold_tuples=lambda: [], pipeline=None, sgd=None, **kwargs):
|
||
for raw_text, annot_brackets in get_gold_tuples():
|
||
for _, (cats, _2) in annot_brackets:
|
||
for cat in cats:
|
||
self.add_label(cat)
|
||
if self.model is True:
|
||
self.cfg["pretrained_vectors"] = kwargs.get("pretrained_vectors")
|
||
self.cfg["pretrained_dims"] = kwargs.get("pretrained_dims")
|
||
self.require_labels()
|
||
self.model = self.Model(len(self.labels), **self.cfg)
|
||
link_vectors_to_models(self.vocab)
|
||
if sgd is None:
|
||
sgd = self.create_optimizer()
|
||
return sgd
|
||
|
||
|
||
cdef class DependencyParser(Parser):
|
||
"""Pipeline component for dependency parsing.
|
||
|
||
DOCS: https://spacy.io/api/dependencyparser
|
||
"""
|
||
# cdef classes can't have decorators, so we're defining this here
|
||
name = "parser"
|
||
factory = "parser"
|
||
assigns = ["token.dep", "token.is_sent_start", "doc.sents"]
|
||
requires = []
|
||
TransitionSystem = ArcEager
|
||
nr_feature = 8
|
||
|
||
@property
|
||
def postprocesses(self):
|
||
output = [nonproj.deprojectivize]
|
||
if self.cfg.get("learn_tokens") is True:
|
||
output.append(merge_subtokens)
|
||
return tuple(output)
|
||
|
||
def add_multitask_objective(self, target):
|
||
if target == "cloze":
|
||
cloze = ClozeMultitask(self.vocab)
|
||
self._multitasks.append(cloze)
|
||
else:
|
||
labeller = MultitaskObjective(self.vocab, target=target)
|
||
self._multitasks.append(labeller)
|
||
|
||
def init_multitask_objectives(self, get_gold_tuples, pipeline, sgd=None, **cfg):
|
||
for labeller in self._multitasks:
|
||
tok2vec = self.model.tok2vec
|
||
labeller.begin_training(get_gold_tuples, pipeline=pipeline,
|
||
tok2vec=tok2vec, sgd=sgd)
|
||
|
||
def __reduce__(self):
|
||
return (DependencyParser, (self.vocab, self.moves, self.model), None, None)
|
||
|
||
@property
|
||
def labels(self):
|
||
labels = set()
|
||
# Get the labels from the model by looking at the available moves
|
||
for move in self.move_names:
|
||
if "-" in move:
|
||
label = move.split("-")[1]
|
||
if "||" in label:
|
||
label = label.split("||")[1]
|
||
labels.add(label)
|
||
return tuple(sorted(labels))
|
||
|
||
|
||
cdef class EntityRecognizer(Parser):
|
||
"""Pipeline component for named entity recognition.
|
||
|
||
DOCS: https://spacy.io/api/entityrecognizer
|
||
"""
|
||
name = "ner"
|
||
factory = "ner"
|
||
assigns = ["doc.ents", "token.ent_iob", "token.ent_type"]
|
||
requires = []
|
||
TransitionSystem = BiluoPushDown
|
||
nr_feature = 6
|
||
|
||
def add_multitask_objective(self, target):
|
||
if target == "cloze":
|
||
cloze = ClozeMultitask(self.vocab)
|
||
self._multitasks.append(cloze)
|
||
else:
|
||
labeller = MultitaskObjective(self.vocab, target=target)
|
||
self._multitasks.append(labeller)
|
||
|
||
def init_multitask_objectives(self, get_gold_tuples, pipeline, sgd=None, **cfg):
|
||
for labeller in self._multitasks:
|
||
tok2vec = self.model.tok2vec
|
||
labeller.begin_training(get_gold_tuples, pipeline=pipeline,
|
||
tok2vec=tok2vec)
|
||
|
||
def __reduce__(self):
|
||
return (EntityRecognizer, (self.vocab, self.moves, self.model),
|
||
None, None)
|
||
|
||
@property
|
||
def labels(self):
|
||
# Get the labels from the model by looking at the available moves, e.g.
|
||
# B-PERSON, I-PERSON, L-PERSON, U-PERSON
|
||
labels = set(move.split("-")[1] for move in self.move_names
|
||
if move[0] in ("B", "I", "L", "U"))
|
||
return tuple(sorted(labels))
|
||
|
||
|
||
@component(
|
||
"entity_linker",
|
||
requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
|
||
assigns=["token.ent_kb_id"]
|
||
)
|
||
class EntityLinker(Pipe):
|
||
"""Pipeline component for named entity linking.
|
||
|
||
DOCS: https://spacy.io/api/entitylinker
|
||
"""
|
||
NIL = "NIL" # string used to refer to a non-existing link
|
||
|
||
@classmethod
|
||
def Model(cls, **cfg):
|
||
embed_width = cfg.get("embed_width", 300)
|
||
hidden_width = cfg.get("hidden_width", 128)
|
||
type_to_int = cfg.get("type_to_int", dict())
|
||
|
||
model = build_nel_encoder(embed_width=embed_width, hidden_width=hidden_width, ner_types=len(type_to_int), **cfg)
|
||
return model
|
||
|
||
def __init__(self, vocab, **cfg):
|
||
self.vocab = vocab
|
||
self.model = True
|
||
self.kb = None
|
||
self.cfg = dict(cfg)
|
||
|
||
# how many neighbour sentences to take into account
|
||
self.n_sents = cfg.get("n_sents", 0)
|
||
|
||
def set_kb(self, kb):
|
||
self.kb = kb
|
||
|
||
def require_model(self):
|
||
# Raise an error if the component's model is not initialized.
|
||
if getattr(self, "model", None) in (None, True, False):
|
||
raise ValueError(Errors.E109.format(name=self.name))
|
||
|
||
def require_kb(self):
|
||
# Raise an error if the knowledge base is not initialized.
|
||
if getattr(self, "kb", None) in (None, True, False):
|
||
raise ValueError(Errors.E139.format(name=self.name))
|
||
|
||
def begin_training(self, get_gold_tuples=lambda: [], pipeline=None, sgd=None, **kwargs):
|
||
self.require_kb()
|
||
self.cfg["entity_width"] = self.kb.entity_vector_length
|
||
|
||
if self.model is True:
|
||
self.model = self.Model(**self.cfg)
|
||
|
||
if sgd is None:
|
||
sgd = self.create_optimizer()
|
||
|
||
return sgd
|
||
|
||
def update(self, docs, golds, state=None, drop=0.0, sgd=None, losses=None):
|
||
self.require_model()
|
||
self.require_kb()
|
||
|
||
if losses is not None:
|
||
losses.setdefault(self.name, 0.0)
|
||
|
||
if not docs or not golds:
|
||
return 0
|
||
|
||
if len(docs) != len(golds):
|
||
raise ValueError(Errors.E077.format(value="EL training", n_docs=len(docs),
|
||
n_golds=len(golds)))
|
||
|
||
if isinstance(docs, Doc):
|
||
docs = [docs]
|
||
golds = [golds]
|
||
|
||
sentence_docs = []
|
||
|
||
for doc, gold in zip(docs, golds):
|
||
ents_by_offset = dict()
|
||
|
||
sentences = [s for s in doc.sents]
|
||
|
||
for ent in doc.ents:
|
||
ents_by_offset[(ent.start_char, ent.end_char)] = ent
|
||
|
||
for entity, kb_dict in gold.links.items():
|
||
start, end = entity
|
||
mention = doc.text[start:end]
|
||
|
||
# the gold annotations should link to proper entities - if this fails, the dataset is likely corrupt
|
||
if not (start, end) in ents_by_offset:
|
||
raise RuntimeError(Errors.E188)
|
||
|
||
ent = ents_by_offset[(start, end)]
|
||
|
||
for kb_id, value in kb_dict.items():
|
||
# Currently only training on the positive instances
|
||
if value:
|
||
try:
|
||
# find the sentence in the list of sentences.
|
||
sent_index = sentences.index(ent.sent)
|
||
|
||
except AttributeError:
|
||
# Catch the exception when ent.sent is None and provide a user-friendly warning
|
||
raise RuntimeError(Errors.E030)
|
||
|
||
# get n previous sentences, if there are any
|
||
start_sentence = max(0, sent_index - self.n_sents)
|
||
|
||
# get n posterior sentences, or as many < n as there are
|
||
end_sentence = min(len(sentences) -1, sent_index + self.n_sents)
|
||
|
||
# get token positions
|
||
start_token = sentences[start_sentence].start
|
||
end_token = sentences[end_sentence].end
|
||
|
||
# append that span as a doc to training
|
||
sent_doc = doc[start_token:end_token].as_doc()
|
||
sentence_docs.append(sent_doc)
|
||
|
||
sentence_encodings, bp_context = self.model.begin_update(sentence_docs, drop=drop)
|
||
loss, d_scores = self.get_similarity_loss(scores=sentence_encodings, golds=golds, docs=None)
|
||
bp_context(d_scores, sgd=sgd)
|
||
|
||
if losses is not None:
|
||
losses[self.name] += loss
|
||
return loss
|
||
|
||
def get_similarity_loss(self, docs, golds, scores):
|
||
entity_encodings = []
|
||
for gold in golds:
|
||
for entity, kb_dict in gold.links.items():
|
||
for kb_id, value in kb_dict.items():
|
||
# this loss function assumes we're only using positive examples
|
||
if value:
|
||
entity_encoding = self.kb.get_vector(kb_id)
|
||
entity_encodings.append(entity_encoding)
|
||
|
||
entity_encodings = self.model.ops.asarray(entity_encodings, dtype="float32")
|
||
|
||
if scores.shape != entity_encodings.shape:
|
||
raise RuntimeError(Errors.E147.format(method="get_loss", msg="gold entities do not match up"))
|
||
|
||
loss, gradients = get_cossim_loss(yh=scores, y=entity_encodings)
|
||
loss = loss / len(entity_encodings)
|
||
return loss, gradients
|
||
|
||
def get_loss(self, docs, golds, scores):
|
||
cats = []
|
||
for gold in golds:
|
||
for entity, kb_dict in gold.links.items():
|
||
for kb_id, value in kb_dict.items():
|
||
cats.append([value])
|
||
|
||
cats = self.model.ops.asarray(cats, dtype="float32")
|
||
if len(scores) != len(cats):
|
||
raise RuntimeError(Errors.E147.format(method="get_loss", msg="gold entities do not match up"))
|
||
|
||
d_scores = (scores - cats)
|
||
loss = (d_scores ** 2).sum()
|
||
loss = loss / len(cats)
|
||
return loss, d_scores
|
||
|
||
def __call__(self, doc):
|
||
kb_ids, tensors = self.predict([doc])
|
||
self.set_annotations([doc], kb_ids, tensors=tensors)
|
||
return doc
|
||
|
||
def pipe(self, stream, batch_size=128, n_threads=-1):
|
||
for docs in util.minibatch(stream, size=batch_size):
|
||
docs = list(docs)
|
||
kb_ids, tensors = self.predict(docs)
|
||
self.set_annotations(docs, kb_ids, tensors=tensors)
|
||
yield from docs
|
||
|
||
def predict(self, docs):
|
||
""" Return the KB IDs for each entity in each doc, including NIL if there is no prediction """
|
||
self.require_model()
|
||
self.require_kb()
|
||
|
||
entity_count = 0
|
||
final_kb_ids = []
|
||
final_tensors = []
|
||
|
||
if not docs:
|
||
return final_kb_ids, final_tensors
|
||
|
||
if isinstance(docs, Doc):
|
||
docs = [docs]
|
||
|
||
|
||
for i, doc in enumerate(docs):
|
||
sentences = [s for s in doc.sents]
|
||
|
||
if len(doc) > 0:
|
||
# Looping through each sentence and each entity
|
||
# This may go wrong if there are entities across sentences - which shouldn't happen normally.
|
||
for sent_index, sent in enumerate(sentences):
|
||
if sent.ents:
|
||
# get n_neighbour sentences, clipped to the length of the document
|
||
start_sentence = max(0, sent_index - self.n_sents)
|
||
end_sentence = min(len(sentences) -1, sent_index + self.n_sents)
|
||
|
||
start_token = sentences[start_sentence].start
|
||
end_token = sentences[end_sentence].end
|
||
|
||
sent_doc = doc[start_token:end_token].as_doc()
|
||
|
||
# currently, the context is the same for each entity in a sentence (should be refined)
|
||
sentence_encoding = self.model([sent_doc])[0]
|
||
xp = get_array_module(sentence_encoding)
|
||
sentence_encoding_t = sentence_encoding.T
|
||
sentence_norm = xp.linalg.norm(sentence_encoding_t)
|
||
|
||
for ent in sent.ents:
|
||
entity_count += 1
|
||
|
||
to_discard = self.cfg.get("labels_discard", [])
|
||
if to_discard and ent.label_ in to_discard:
|
||
# ignoring this entity - setting to NIL
|
||
final_kb_ids.append(self.NIL)
|
||
final_tensors.append(sentence_encoding)
|
||
|
||
else:
|
||
candidates = self.kb.get_candidates(ent.text)
|
||
if not candidates:
|
||
# no prediction possible for this entity - setting to NIL
|
||
final_kb_ids.append(self.NIL)
|
||
final_tensors.append(sentence_encoding)
|
||
|
||
elif len(candidates) == 1:
|
||
# shortcut for efficiency reasons: take the 1 candidate
|
||
|
||
# TODO: thresholding
|
||
final_kb_ids.append(candidates[0].entity_)
|
||
final_tensors.append(sentence_encoding)
|
||
|
||
else:
|
||
random.shuffle(candidates)
|
||
|
||
# this will set all prior probabilities to 0 if they should be excluded from the model
|
||
prior_probs = xp.asarray([c.prior_prob for c in candidates])
|
||
if not self.cfg.get("incl_prior", True):
|
||
prior_probs = xp.asarray([0.0 for c in candidates])
|
||
scores = prior_probs
|
||
|
||
# add in similarity from the context
|
||
if self.cfg.get("incl_context", True):
|
||
entity_encodings = xp.asarray([c.entity_vector for c in candidates])
|
||
entity_norm = xp.linalg.norm(entity_encodings, axis=1)
|
||
|
||
if len(entity_encodings) != len(prior_probs):
|
||
raise RuntimeError(Errors.E147.format(method="predict", msg="vectors not of equal length"))
|
||
|
||
# cosine similarity
|
||
sims = xp.dot(entity_encodings, sentence_encoding_t) / (sentence_norm * entity_norm)
|
||
if sims.shape != prior_probs.shape:
|
||
raise ValueError(Errors.E161)
|
||
scores = prior_probs + sims - (prior_probs*sims)
|
||
|
||
# TODO: thresholding
|
||
best_index = scores.argmax()
|
||
best_candidate = candidates[best_index]
|
||
final_kb_ids.append(best_candidate.entity_)
|
||
final_tensors.append(sentence_encoding)
|
||
|
||
if not (len(final_tensors) == len(final_kb_ids) == entity_count):
|
||
raise RuntimeError(Errors.E147.format(method="predict", msg="result variables not of equal length"))
|
||
|
||
return final_kb_ids, final_tensors
|
||
|
||
def set_annotations(self, docs, kb_ids, tensors=None):
|
||
count_ents = len([ent for doc in docs for ent in doc.ents])
|
||
if count_ents != len(kb_ids):
|
||
raise ValueError(Errors.E148.format(ents=count_ents, ids=len(kb_ids)))
|
||
|
||
i=0
|
||
for doc in docs:
|
||
for ent in doc.ents:
|
||
kb_id = kb_ids[i]
|
||
i += 1
|
||
for token in ent:
|
||
token.ent_kb_id_ = kb_id
|
||
|
||
def to_disk(self, path, exclude=tuple(), **kwargs):
|
||
serialize = OrderedDict()
|
||
serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
|
||
serialize["vocab"] = lambda p: self.vocab.to_disk(p)
|
||
serialize["kb"] = lambda p: self.kb.dump(p)
|
||
if self.model not in (None, True, False):
|
||
serialize["model"] = lambda p: self.model.to_disk(p)
|
||
exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
|
||
util.to_disk(path, serialize, exclude)
|
||
|
||
def from_disk(self, path, exclude=tuple(), **kwargs):
|
||
def load_model(p):
|
||
if self.model is True:
|
||
self.model = self.Model(**self.cfg)
|
||
try:
|
||
self.model.from_bytes(p.open("rb").read())
|
||
except AttributeError:
|
||
raise ValueError(Errors.E149)
|
||
|
||
def load_kb(p):
|
||
kb = KnowledgeBase(vocab=self.vocab, entity_vector_length=self.cfg["entity_width"])
|
||
kb.load_bulk(p)
|
||
self.set_kb(kb)
|
||
|
||
deserialize = OrderedDict()
|
||
deserialize["cfg"] = lambda p: self.cfg.update(_load_cfg(p))
|
||
deserialize["vocab"] = lambda p: self.vocab.from_disk(p)
|
||
deserialize["kb"] = load_kb
|
||
deserialize["model"] = load_model
|
||
exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
|
||
util.from_disk(path, deserialize, exclude)
|
||
return self
|
||
|
||
def rehearse(self, docs, sgd=None, losses=None, **config):
|
||
raise NotImplementedError
|
||
|
||
def add_label(self, label):
|
||
raise NotImplementedError
|
||
|
||
|
||
@component("sentencizer", assigns=["token.is_sent_start", "doc.sents"])
|
||
class Sentencizer(object):
|
||
"""Segment the Doc into sentences using a rule-based strategy.
|
||
|
||
DOCS: https://spacy.io/api/sentencizer
|
||
"""
|
||
|
||
default_punct_chars = ['!', '.', '?', '։', '؟', '۔', '܀', '܁', '܂', '߹',
|
||
':', ';', '؟',
|
||
'।', '॥', '၊', '။', '።', '፧', '፨', '᙮', '᜵', '᜶', '᠃', '᠉', '᥄',
|
||
'᥅', '᪨', '᪩', '᪪', '᪫', '᭚', '᭛', '᭞', '᭟', '᰻', '᰼', '᱾', '᱿',
|
||
'‼', '‽', '⁇', '⁈', '⁉', '⸮', '⸼', '꓿', '꘎', '꘏', '꛳', '꛷', '꡶',
|
||
'꡷', '꣎', '꣏', '꤯', '꧈', '꧉', '꩝', '꩞', '꩟', '꫰', '꫱', '꯫', '﹒',
|
||
'﹖', '﹗', '！', '．', '？', '𐩖', '𐩗', '𑁇', '𑁈', '𑂾', '𑂿', '𑃀',
|
||
'𑃁', '𑅁', '𑅂', '𑅃', '𑇅', '𑇆', '𑇍', '𑇞', '𑇟', '𑈸', '𑈹', '𑈻', '𑈼',
|
||
'𑊩', '𑑋', '𑑌', '𑗂', '𑗃', '𑗉', '𑗊', '𑗋', '𑗌', '𑗍', '𑗎', '𑗏', '𑗐',
|
||
'𑗑', '𑗒', '𑗓', '𑗔', '𑗕', '𑗖', '𑗗', '𑙁', '𑙂', '𑜼', '𑜽', '𑜾', '𑩂',
|
||
'𑩃', '𑪛', '𑪜', '𑱁', '𑱂', '𖩮', '𖩯', '𖫵', '𖬷', '𖬸', '𖭄', '𛲟', '𝪈',
|
||
'｡', '。']
|
||
|
||
def __init__(self, punct_chars=None, **kwargs):
|
||
"""Initialize the sentencizer.
|
||
|
||
punct_chars (list): Punctuation characters to split on. Will be
|
||
serialized with the nlp object.
|
||
RETURNS (Sentencizer): The sentencizer component.
|
||
|
||
DOCS: https://spacy.io/api/sentencizer#init
|
||
"""
|
||
if punct_chars:
|
||
self.punct_chars = set(punct_chars)
|
||
else:
|
||
self.punct_chars = set(self.default_punct_chars)
|
||
|
||
@classmethod
|
||
def from_nlp(cls, nlp, **cfg):
|
||
return cls(**cfg)
|
||
|
||
def __call__(self, doc):
|
||
"""Apply the sentencizer to a Doc and set Token.is_sent_start.
|
||
|
||
doc (Doc): The document to process.
|
||
RETURNS (Doc): The processed Doc.
|
||
|
||
DOCS: https://spacy.io/api/sentencizer#call
|
||
"""
|
||
tags = self.predict([doc])
|
||
self.set_annotations([doc], tags)
|
||
return doc
|
||
|
||
def pipe(self, stream, batch_size=128, n_threads=-1):
|
||
for docs in util.minibatch(stream, size=batch_size):
|
||
docs = list(docs)
|
||
tag_ids = self.predict(docs)
|
||
self.set_annotations(docs, tag_ids)
|
||
yield from docs
|
||
|
||
def predict(self, docs):
|
||
"""Apply the pipeline's model to a batch of docs, without
|
||
modifying them.
|
||
"""
|
||
if not any(len(doc) for doc in docs):
|
||
# Handle cases where there are no tokens in any docs.
|
||
guesses = [[] for doc in docs]
|
||
return guesses
|
||
guesses = []
|
||
for doc in docs:
|
||
doc_guesses = [False] * len(doc)
|
||
if len(doc) > 0:
|
||
start = 0
|
||
seen_period = False
|
||
doc_guesses[0] = True
|
||
for i, token in enumerate(doc):
|
||
is_in_punct_chars = token.text in self.punct_chars
|
||
if seen_period and not token.is_punct and not is_in_punct_chars:
|
||
doc_guesses[start] = True
|
||
start = token.i
|
||
seen_period = False
|
||
elif is_in_punct_chars:
|
||
seen_period = True
|
||
if start < len(doc):
|
||
doc_guesses[start] = True
|
||
guesses.append(doc_guesses)
|
||
return guesses
|
||
|
||
def set_annotations(self, docs, batch_tag_ids, tensors=None):
|
||
if isinstance(docs, Doc):
|
||
docs = [docs]
|
||
cdef Doc doc
|
||
cdef int idx = 0
|
||
for i, doc in enumerate(docs):
|
||
doc_tag_ids = batch_tag_ids[i]
|
||
for j, tag_id in enumerate(doc_tag_ids):
|
||
# Don't clobber existing sentence boundaries
|
||
if doc.c[j].sent_start == 0:
|
||
if tag_id:
|
||
doc.c[j].sent_start = 1
|
||
else:
|
||
doc.c[j].sent_start = -1
|
||
|
||
def to_bytes(self, **kwargs):
|
||
"""Serialize the sentencizer to a bytestring.
|
||
|
||
RETURNS (bytes): The serialized object.
|
||
|
||
DOCS: https://spacy.io/api/sentencizer#to_bytes
|
||
"""
|
||
return srsly.msgpack_dumps({"punct_chars": list(self.punct_chars)})
|
||
|
||
def from_bytes(self, bytes_data, **kwargs):
|
||
"""Load the sentencizer from a bytestring.
|
||
|
||
bytes_data (bytes): The data to load.
|
||
returns (Sentencizer): The loaded object.
|
||
|
||
DOCS: https://spacy.io/api/sentencizer#from_bytes
|
||
"""
|
||
cfg = srsly.msgpack_loads(bytes_data)
|
||
self.punct_chars = set(cfg.get("punct_chars", self.default_punct_chars))
|
||
return self
|
||
|
||
def to_disk(self, path, exclude=tuple(), **kwargs):
|
||
"""Serialize the sentencizer to disk.
|
||
|
||
DOCS: https://spacy.io/api/sentencizer#to_disk
|
||
"""
|
||
path = util.ensure_path(path)
|
||
path = path.with_suffix(".json")
|
||
srsly.write_json(path, {"punct_chars": list(self.punct_chars)})
|
||
|
||
|
||
def from_disk(self, path, exclude=tuple(), **kwargs):
|
||
"""Load the sentencizer from disk.
|
||
|
||
DOCS: https://spacy.io/api/sentencizer#from_disk
|
||
"""
|
||
path = util.ensure_path(path)
|
||
path = path.with_suffix(".json")
|
||
cfg = srsly.read_json(path)
|
||
self.punct_chars = set(cfg.get("punct_chars", self.default_punct_chars))
|
||
return self
|
||
|
||
|
||
# Cython classes can't be decorated, so we need to add the factories here
|
||
Language.factories["parser"] = lambda nlp, **cfg: DependencyParser.from_nlp(nlp, **cfg)
|
||
Language.factories["ner"] = lambda nlp, **cfg: EntityRecognizer.from_nlp(nlp, **cfg)
|
||
|
||
|
||
__all__ = ["Tagger", "DependencyParser", "EntityRecognizer", "Tensorizer", "TextCategorizer", "EntityLinker", "Sentencizer"]
|