from typing import Optional, Iterable, Callable, Dict, Iterator, Union, List, Tuple from pathlib import Path import srsly import random from thinc.api import CosineDistance, Model, Optimizer, Config from thinc.api import set_dropout_rate import warnings from ..kb import KnowledgeBase, Candidate from ..tokens import Doc from .pipe import Pipe, deserialize_config from ..language import Language from ..vocab import Vocab from ..gold import Example, validate_examples from ..errors import Errors, Warnings from ..util import SimpleFrozenList from .. import util default_model_config = """ [model] @architectures = "spacy.EntityLinker.v1" [model.tok2vec] @architectures = "spacy.HashEmbedCNN.v1" pretrained_vectors = null width = 96 depth = 2 embed_size = 300 window_size = 1 maxout_pieces = 3 subword_features = true """ DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"] @Language.factory( "entity_linker", requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"], assigns=["token.ent_kb_id"], default_config={ "kb_loader": {"@assets": "spacy.EmptyKB.v1", "entity_vector_length": 64}, "model": DEFAULT_NEL_MODEL, "labels_discard": [], "incl_prior": True, "incl_context": True, "get_candidates": {"@assets": "spacy.CandidateGenerator.v1"}, }, ) def make_entity_linker( nlp: Language, name: str, model: Model, kb_loader: Callable[[Vocab], KnowledgeBase], *, labels_discard: Iterable[str], incl_prior: bool, incl_context: bool, get_candidates: Callable[[KnowledgeBase, "Span"], Iterable[Candidate]], ): """Construct an EntityLinker component. model (Model[List[Doc], Floats2d]): A model that learns document vector representations. Given a batch of Doc objects, it should return a single array, with one row per item in the batch. kb (KnowledgeBase): The knowledge-base to link entities to. labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction. incl_prior (bool): Whether or not to include prior probabilities from the KB in the model. incl_context (bool): Whether or not to include the local context in the model. """ return EntityLinker( nlp.vocab, model, name, kb_loader=kb_loader, labels_discard=labels_discard, incl_prior=incl_prior, incl_context=incl_context, get_candidates=get_candidates, ) 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 def __init__( self, vocab: Vocab, model: Model, name: str = "entity_linker", *, kb_loader: Callable[[Vocab], KnowledgeBase], labels_discard: Iterable[str], incl_prior: bool, incl_context: bool, get_candidates: Callable[[KnowledgeBase, "Span"], Iterable[Candidate]], ) -> None: """Initialize an entity linker. vocab (Vocab): The shared vocabulary. model (thinc.api.Model): The Thinc Model powering the pipeline component. name (str): The component instance name, used to add entries to the losses during training. kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab instance. labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction. incl_prior (bool): Whether or not to include prior probabilities from the KB in the model. incl_context (bool): Whether or not to include the local context in the model. DOCS: https://spacy.io/api/entitylinker#init """ self.vocab = vocab self.model = model self.name = name cfg = { "labels_discard": list(labels_discard), "incl_prior": incl_prior, "incl_context": incl_context, } self.kb = kb_loader(self.vocab) self.get_candidates = get_candidates self.cfg = dict(cfg) self.distance = CosineDistance(normalize=False) # how many neightbour sentences to take into account self.n_sents = cfg.get("n_sents", 0) def require_kb(self) -> None: # Raise an error if the knowledge base is not initialized. if len(self.kb) == 0: raise ValueError(Errors.E139.format(name=self.name)) def begin_training( self, get_examples: Callable[[], Iterable[Example]], *, pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None, sgd: Optional[Optimizer] = None, ) -> Optimizer: """Initialize the pipe for training, using data examples if available. get_examples (Callable[[], Iterable[Example]]): Optional function that returns gold-standard Example objects. pipeline (List[Tuple[str, Callable]]): Optional list of pipeline components that this component is part of. Corresponds to nlp.pipeline. sgd (thinc.api.Optimizer): Optional optimizer. Will be created with create_optimizer if it doesn't exist. RETURNS (thinc.api.Optimizer): The optimizer. DOCS: https://spacy.io/api/entitylinker#begin_training """ self.require_kb() nO = self.kb.entity_vector_length self.set_output(nO) self.model.initialize() if sgd is None: sgd = self.create_optimizer() return sgd def update( self, examples: Iterable[Example], *, set_annotations: bool = False, drop: float = 0.0, sgd: Optional[Optimizer] = None, losses: Optional[Dict[str, float]] = None, ) -> Dict[str, float]: """Learn from a batch of documents and gold-standard information, updating the pipe's model. Delegates to predict and get_loss. examples (Iterable[Example]): A batch of Example objects. drop (float): The dropout rate. set_annotations (bool): Whether or not to update the Example objects with the predictions. sgd (thinc.api.Optimizer): The optimizer. losses (Dict[str, float]): Optional record of the loss during training. Updated using the component name as the key. RETURNS (Dict[str, float]): The updated losses dictionary. DOCS: https://spacy.io/api/entitylinker#update """ self.require_kb() if losses is None: losses = {} losses.setdefault(self.name, 0.0) if not examples: return losses validate_examples(examples, "EntityLinker.update") sentence_docs = [] docs = [eg.predicted for eg in examples] if set_annotations: # This seems simpler than other ways to get that exact output -- but # it does run the model twice :( predictions = self.model.predict(docs) for eg in examples: sentences = [s for s in eg.predicted.sents] kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True) for ent in eg.predicted.ents: kb_id = kb_ids[ ent.start ] # KB ID of the first token is the same as the whole span if kb_id: 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) from None # 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 = eg.predicted[start_token:end_token].as_doc() sentence_docs.append(sent_doc) set_dropout_rate(self.model, drop) if not sentence_docs: warnings.warn(Warnings.W093.format(name="Entity Linker")) return losses sentence_encodings, bp_context = self.model.begin_update(sentence_docs) loss, d_scores = self.get_loss( sentence_encodings=sentence_encodings, examples=examples ) bp_context(d_scores) if sgd is not None: self.model.finish_update(sgd) losses[self.name] += loss if set_annotations: self.set_annotations(docs, predictions) return losses def get_loss(self, examples: Iterable[Example], sentence_encodings): validate_examples(examples, "EntityLinker.get_loss") entity_encodings = [] for eg in examples: kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True) for ent in eg.predicted.ents: kb_id = kb_ids[ent.start] if kb_id: entity_encoding = self.kb.get_vector(kb_id) entity_encodings.append(entity_encoding) entity_encodings = self.model.ops.asarray(entity_encodings, dtype="float32") if sentence_encodings.shape != entity_encodings.shape: err = Errors.E147.format( method="get_loss", msg="gold entities do not match up" ) raise RuntimeError(err) gradients = self.distance.get_grad(sentence_encodings, entity_encodings) loss = self.distance.get_loss(sentence_encodings, entity_encodings) loss = loss / len(entity_encodings) return loss, gradients def __call__(self, doc: Doc) -> Doc: """Apply the pipe to a Doc. doc (Doc): The document to process. RETURNS (Doc): The processed Doc. DOCS: https://spacy.io/api/entitylinker#call """ kb_ids = self.predict([doc]) self.set_annotations([doc], kb_ids) return doc def pipe(self, stream: Iterable[Doc], *, batch_size: int = 128) -> Iterator[Doc]: """Apply the pipe to a stream of documents. This usually happens under the hood when the nlp object is called on a text and all components are applied to the Doc. stream (Iterable[Doc]): A stream of documents. batch_size (int): The number of documents to buffer. YIELDS (Doc): Processed documents in order. DOCS: https://spacy.io/api/entitylinker#pipe """ for docs in util.minibatch(stream, size=batch_size): kb_ids = self.predict(docs) self.set_annotations(docs, kb_ids) yield from docs def predict(self, docs: Iterable[Doc]) -> List[str]: """Apply the pipeline's model to a batch of docs, without modifying them. Returns the KB IDs for each entity in each doc, including NIL if there is no prediction. docs (Iterable[Doc]): The documents to predict. RETURNS (List[int]): The models prediction for each document. DOCS: https://spacy.io/api/entitylinker#predict """ self.require_kb() entity_count = 0 final_kb_ids = [] if not docs: return final_kb_ids 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_neightbour 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) xp = self.model.ops.xp if self.cfg.get("incl_context"): sentence_encoding = self.model.predict([sent_doc])[0] 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) else: candidates = self.get_candidates(self.kb, ent) if not candidates: # no prediction possible for this entity - setting to NIL final_kb_ids.append(self.NIL) elif len(candidates) == 1: # shortcut for efficiency reasons: take the 1 candidate # TODO: thresholding final_kb_ids.append(candidates[0].entity_) else: random.shuffle(candidates) # set all prior probabilities to 0 if incl_prior=False prior_probs = xp.asarray( [c.prior_prob for c in candidates] ) if not self.cfg.get("incl_prior"): prior_probs = xp.asarray( [0.0 for _ in candidates] ) scores = prior_probs # add in similarity from the context if self.cfg.get("incl_context"): 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().item() best_candidate = candidates[best_index] final_kb_ids.append(best_candidate.entity_) if not (len(final_kb_ids) == entity_count): err = Errors.E147.format( method="predict", msg="result variables not of equal length" ) raise RuntimeError(err) return final_kb_ids def set_annotations(self, docs: Iterable[Doc], kb_ids: List[str]) -> None: """Modify a batch of documents, using pre-computed scores. docs (Iterable[Doc]): The documents to modify. kb_ids (List[str]): The IDs to set, produced by EntityLinker.predict. DOCS: https://spacy.io/api/entitylinker#set_annotations """ 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: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList(), ) -> None: """Serialize the pipe to disk. path (str / Path): Path to a directory. exclude (Iterable[str]): String names of serialization fields to exclude. DOCS: https://spacy.io/api/entitylinker#to_disk """ serialize = {} 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.to_disk(p) serialize["model"] = lambda p: self.model.to_disk(p) util.to_disk(path, serialize, exclude) def from_disk( self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList(), ) -> "EntityLinker": """Load the pipe from disk. Modifies the object in place and returns it. path (str / Path): Path to a directory. exclude (Iterable[str]): String names of serialization fields to exclude. RETURNS (EntityLinker): The modified EntityLinker object. DOCS: https://spacy.io/api/entitylinker#from_disk """ def load_model(p): try: self.model.from_bytes(p.open("rb").read()) except AttributeError: raise ValueError(Errors.E149) from None deserialize = {} deserialize["vocab"] = lambda p: self.vocab.from_disk(p) deserialize["cfg"] = lambda p: self.cfg.update(deserialize_config(p)) deserialize["kb"] = lambda p: self.kb.from_disk(p) deserialize["model"] = load_model util.from_disk(path, deserialize, exclude) return self def rehearse(self, examples, *, sgd=None, losses=None, **config): raise NotImplementedError def add_label(self, label): raise NotImplementedError