mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 04:08:09 +03:00
455 lines
19 KiB
Python
455 lines
19 KiB
Python
from typing import Optional, Iterable, Callable, Dict, Iterator, Union, List, Tuple
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from pathlib import Path
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import srsly
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import random
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from thinc.api import CosineDistance, Model, Optimizer, Config
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from thinc.api import set_dropout_rate
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import warnings
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from ..kb import KnowledgeBase, Candidate
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from ..tokens import Doc
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from .pipe import Pipe, deserialize_config
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from ..language import Language
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from ..vocab import Vocab
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from ..gold import Example, validate_examples
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from ..errors import Errors, Warnings
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from ..util import SimpleFrozenList
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from .. import util
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default_model_config = """
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[model]
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@architectures = "spacy.EntityLinker.v1"
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[model.tok2vec]
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@architectures = "spacy.HashEmbedCNN.v1"
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pretrained_vectors = null
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width = 96
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depth = 2
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embed_size = 300
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window_size = 1
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maxout_pieces = 3
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subword_features = true
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"""
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DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
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@Language.factory(
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"entity_linker",
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requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
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assigns=["token.ent_kb_id"],
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default_config={
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"kb_loader": {"@assets": "spacy.EmptyKB.v1", "entity_vector_length": 64},
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"model": DEFAULT_NEL_MODEL,
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"labels_discard": [],
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"incl_prior": True,
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"incl_context": True,
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"get_candidates": {"@assets": "spacy.CandidateGenerator.v1"},
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},
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)
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def make_entity_linker(
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nlp: Language,
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name: str,
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model: Model,
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kb_loader: Callable[[Vocab], KnowledgeBase],
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*,
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labels_discard: Iterable[str],
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incl_prior: bool,
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incl_context: bool,
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get_candidates: Callable[[KnowledgeBase, "Span"], Iterable[Candidate]],
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):
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"""Construct an EntityLinker component.
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model (Model[List[Doc], Floats2d]): A model that learns document vector
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representations. Given a batch of Doc objects, it should return a single
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array, with one row per item in the batch.
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kb (KnowledgeBase): The knowledge-base to link entities to.
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labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction.
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incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
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incl_context (bool): Whether or not to include the local context in the model.
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"""
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return EntityLinker(
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nlp.vocab,
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model,
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name,
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kb_loader=kb_loader,
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labels_discard=labels_discard,
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incl_prior=incl_prior,
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incl_context=incl_context,
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get_candidates=get_candidates,
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)
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class EntityLinker(Pipe):
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"""Pipeline component for named entity linking.
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DOCS: https://spacy.io/api/entitylinker
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"""
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NIL = "NIL" # string used to refer to a non-existing link
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def __init__(
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self,
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vocab: Vocab,
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model: Model,
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name: str = "entity_linker",
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*,
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kb_loader: Callable[[Vocab], KnowledgeBase],
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labels_discard: Iterable[str],
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incl_prior: bool,
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incl_context: bool,
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get_candidates: Callable[[KnowledgeBase, "Span"], Iterable[Candidate]],
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) -> None:
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"""Initialize an entity linker.
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vocab (Vocab): The shared vocabulary.
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model (thinc.api.Model): The Thinc Model powering the pipeline component.
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name (str): The component instance name, used to add entries to the
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losses during training.
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kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab instance.
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labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction.
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incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
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incl_context (bool): Whether or not to include the local context in the model.
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DOCS: https://spacy.io/api/entitylinker#init
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"""
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self.vocab = vocab
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self.model = model
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self.name = name
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cfg = {
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"labels_discard": list(labels_discard),
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"incl_prior": incl_prior,
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"incl_context": incl_context,
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}
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self.kb = kb_loader(self.vocab)
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self.get_candidates = get_candidates
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self.cfg = dict(cfg)
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self.distance = CosineDistance(normalize=False)
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# how many neightbour sentences to take into account
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self.n_sents = cfg.get("n_sents", 0)
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def require_kb(self) -> None:
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# Raise an error if the knowledge base is not initialized.
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if len(self.kb) == 0:
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raise ValueError(Errors.E139.format(name=self.name))
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def begin_training(
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self,
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get_examples: Callable[[], Iterable[Example]],
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*,
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pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None,
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sgd: Optional[Optimizer] = None,
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) -> Optimizer:
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"""Initialize the pipe for training, using data examples if available.
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get_examples (Callable[[], Iterable[Example]]): Optional function that
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returns gold-standard Example objects.
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pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
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components that this component is part of. Corresponds to
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nlp.pipeline.
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sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
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create_optimizer if it doesn't exist.
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RETURNS (thinc.api.Optimizer): The optimizer.
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DOCS: https://spacy.io/api/entitylinker#begin_training
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"""
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self.require_kb()
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nO = self.kb.entity_vector_length
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self.set_output(nO)
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self.model.initialize()
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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 update(
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self,
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examples: Iterable[Example],
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*,
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set_annotations: bool = False,
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drop: float = 0.0,
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sgd: Optional[Optimizer] = None,
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losses: Optional[Dict[str, float]] = None,
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) -> Dict[str, float]:
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"""Learn from a batch of documents and gold-standard information,
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updating the pipe's model. Delegates to predict and get_loss.
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examples (Iterable[Example]): A batch of Example objects.
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drop (float): The dropout rate.
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set_annotations (bool): Whether or not to update the Example objects
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with the predictions.
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sgd (thinc.api.Optimizer): The optimizer.
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losses (Dict[str, float]): Optional record of the loss during training.
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Updated using the component name as the key.
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RETURNS (Dict[str, float]): The updated losses dictionary.
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DOCS: https://spacy.io/api/entitylinker#update
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"""
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self.require_kb()
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if losses is None:
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losses = {}
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losses.setdefault(self.name, 0.0)
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if not examples:
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return losses
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validate_examples(examples, "EntityLinker.update")
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sentence_docs = []
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docs = [eg.predicted for eg in examples]
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if set_annotations:
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# This seems simpler than other ways to get that exact output -- but
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# it does run the model twice :(
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predictions = self.model.predict(docs)
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for eg in examples:
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sentences = [s for s in eg.predicted.sents]
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kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
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for ent in eg.predicted.ents:
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kb_id = kb_ids[
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ent.start
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] # KB ID of the first token is the same as the whole span
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if kb_id:
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try:
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# find the sentence in the list of sentences.
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sent_index = sentences.index(ent.sent)
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except AttributeError:
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# Catch the exception when ent.sent is None and provide a user-friendly warning
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raise RuntimeError(Errors.E030) from None
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# get n previous sentences, if there are any
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start_sentence = max(0, sent_index - self.n_sents)
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# get n posterior sentences, or as many < n as there are
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end_sentence = min(len(sentences) - 1, sent_index + self.n_sents)
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# get token positions
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start_token = sentences[start_sentence].start
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end_token = sentences[end_sentence].end
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# append that span as a doc to training
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sent_doc = eg.predicted[start_token:end_token].as_doc()
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sentence_docs.append(sent_doc)
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set_dropout_rate(self.model, drop)
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if not sentence_docs:
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warnings.warn(Warnings.W093.format(name="Entity Linker"))
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return losses
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sentence_encodings, bp_context = self.model.begin_update(sentence_docs)
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loss, d_scores = self.get_loss(
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sentence_encodings=sentence_encodings, examples=examples
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)
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bp_context(d_scores)
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if sgd is not None:
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self.model.finish_update(sgd)
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losses[self.name] += loss
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if set_annotations:
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self.set_annotations(docs, predictions)
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return losses
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def get_loss(self, examples: Iterable[Example], sentence_encodings):
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validate_examples(examples, "EntityLinker.get_loss")
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entity_encodings = []
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for eg in examples:
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kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
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for ent in eg.predicted.ents:
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kb_id = kb_ids[ent.start]
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if kb_id:
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entity_encoding = self.kb.get_vector(kb_id)
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entity_encodings.append(entity_encoding)
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entity_encodings = self.model.ops.asarray(entity_encodings, dtype="float32")
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if sentence_encodings.shape != entity_encodings.shape:
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err = Errors.E147.format(
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method="get_loss", msg="gold entities do not match up"
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)
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raise RuntimeError(err)
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gradients = self.distance.get_grad(sentence_encodings, entity_encodings)
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loss = self.distance.get_loss(sentence_encodings, entity_encodings)
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loss = loss / len(entity_encodings)
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return loss, gradients
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def __call__(self, doc: Doc) -> Doc:
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"""Apply the pipe to a Doc.
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doc (Doc): The document to process.
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RETURNS (Doc): The processed Doc.
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DOCS: https://spacy.io/api/entitylinker#call
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"""
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kb_ids = self.predict([doc])
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self.set_annotations([doc], kb_ids)
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return doc
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def pipe(self, stream: Iterable[Doc], *, batch_size: int = 128) -> Iterator[Doc]:
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"""Apply the pipe to a stream of documents. This usually happens under
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the hood when the nlp object is called on a text and all components are
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applied to the Doc.
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stream (Iterable[Doc]): A stream of documents.
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batch_size (int): The number of documents to buffer.
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YIELDS (Doc): Processed documents in order.
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DOCS: https://spacy.io/api/entitylinker#pipe
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"""
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for docs in util.minibatch(stream, size=batch_size):
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kb_ids = self.predict(docs)
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self.set_annotations(docs, kb_ids)
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yield from docs
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def predict(self, docs: Iterable[Doc]) -> List[str]:
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"""Apply the pipeline's model to a batch of docs, without modifying them.
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Returns the KB IDs for each entity in each doc, including NIL if there is
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no prediction.
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docs (Iterable[Doc]): The documents to predict.
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RETURNS (List[int]): The models prediction for each document.
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DOCS: https://spacy.io/api/entitylinker#predict
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"""
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self.require_kb()
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entity_count = 0
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final_kb_ids = []
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if not docs:
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return final_kb_ids
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if isinstance(docs, Doc):
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docs = [docs]
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for i, doc in enumerate(docs):
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sentences = [s for s in doc.sents]
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if len(doc) > 0:
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# Looping through each sentence and each entity
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# This may go wrong if there are entities across sentences - which shouldn't happen normally.
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for sent_index, sent in enumerate(sentences):
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if sent.ents:
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# get n_neightbour sentences, clipped to the length of the document
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start_sentence = max(0, sent_index - self.n_sents)
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end_sentence = min(
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len(sentences) - 1, sent_index + self.n_sents
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)
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start_token = sentences[start_sentence].start
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end_token = sentences[end_sentence].end
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sent_doc = doc[start_token:end_token].as_doc()
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# currently, the context is the same for each entity in a sentence (should be refined)
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xp = self.model.ops.xp
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if self.cfg.get("incl_context"):
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sentence_encoding = self.model.predict([sent_doc])[0]
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sentence_encoding_t = sentence_encoding.T
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sentence_norm = xp.linalg.norm(sentence_encoding_t)
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for ent in sent.ents:
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entity_count += 1
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to_discard = self.cfg.get("labels_discard", [])
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if to_discard and ent.label_ in to_discard:
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# ignoring this entity - setting to NIL
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final_kb_ids.append(self.NIL)
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else:
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candidates = self.get_candidates(self.kb, ent)
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if not candidates:
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# no prediction possible for this entity - setting to NIL
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final_kb_ids.append(self.NIL)
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elif len(candidates) == 1:
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# shortcut for efficiency reasons: take the 1 candidate
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# TODO: thresholding
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final_kb_ids.append(candidates[0].entity_)
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else:
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random.shuffle(candidates)
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# set all prior probabilities to 0 if incl_prior=False
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prior_probs = xp.asarray(
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[c.prior_prob for c in candidates]
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)
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if not self.cfg.get("incl_prior"):
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prior_probs = xp.asarray(
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[0.0 for _ in candidates]
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)
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scores = prior_probs
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# add in similarity from the context
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if self.cfg.get("incl_context"):
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entity_encodings = xp.asarray(
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[c.entity_vector for c in candidates]
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)
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entity_norm = xp.linalg.norm(
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entity_encodings, axis=1
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)
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if len(entity_encodings) != len(prior_probs):
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raise RuntimeError(
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Errors.E147.format(
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method="predict",
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msg="vectors not of equal length",
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)
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)
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# cosine similarity
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sims = xp.dot(
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entity_encodings, sentence_encoding_t
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) / (sentence_norm * entity_norm)
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if sims.shape != prior_probs.shape:
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raise ValueError(Errors.E161)
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scores = (
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prior_probs + sims - (prior_probs * sims)
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)
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# TODO: thresholding
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best_index = scores.argmax().item()
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best_candidate = candidates[best_index]
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final_kb_ids.append(best_candidate.entity_)
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if not (len(final_kb_ids) == entity_count):
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err = Errors.E147.format(
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method="predict", msg="result variables not of equal length"
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)
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raise RuntimeError(err)
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return final_kb_ids
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def set_annotations(self, docs: Iterable[Doc], kb_ids: List[str]) -> None:
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"""Modify a batch of documents, using pre-computed scores.
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docs (Iterable[Doc]): The documents to modify.
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kb_ids (List[str]): The IDs to set, produced by EntityLinker.predict.
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DOCS: https://spacy.io/api/entitylinker#set_annotations
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"""
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count_ents = len([ent for doc in docs for ent in doc.ents])
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if count_ents != len(kb_ids):
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raise ValueError(Errors.E148.format(ents=count_ents, ids=len(kb_ids)))
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i = 0
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for doc in docs:
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for ent in doc.ents:
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kb_id = kb_ids[i]
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i += 1
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for token in ent:
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token.ent_kb_id_ = kb_id
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def to_disk(
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self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList(),
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) -> None:
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"""Serialize the pipe to disk.
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path (str / Path): Path to a directory.
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exclude (Iterable[str]): String names of serialization fields to exclude.
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DOCS: https://spacy.io/api/entitylinker#to_disk
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"""
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serialize = {}
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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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serialize["kb"] = lambda p: self.kb.to_disk(p)
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serialize["model"] = lambda p: self.model.to_disk(p)
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util.to_disk(path, serialize, exclude)
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def from_disk(
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self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList(),
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) -> "EntityLinker":
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"""Load the pipe from disk. Modifies the object in place and returns it.
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path (str / Path): Path to a directory.
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exclude (Iterable[str]): String names of serialization fields to exclude.
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RETURNS (EntityLinker): The modified EntityLinker object.
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DOCS: https://spacy.io/api/entitylinker#from_disk
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"""
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def load_model(p):
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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) from None
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deserialize = {}
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deserialize["vocab"] = lambda p: self.vocab.from_disk(p)
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deserialize["cfg"] = lambda p: self.cfg.update(deserialize_config(p))
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deserialize["kb"] = lambda p: self.kb.from_disk(p)
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deserialize["model"] = load_model
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util.from_disk(path, deserialize, exclude)
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return self
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def rehearse(self, examples, *, sgd=None, losses=None, **config):
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raise NotImplementedError
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def add_label(self, label):
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raise NotImplementedError
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