from typing import Optional, Iterable, Callable, Dict, Sequence, Union, List, Any from typing import cast from numpy import dtype from thinc.types import Floats1d, Floats2d, Ints1d, Ragged from pathlib import Path from itertools import islice import srsly import random from thinc.api import CosineDistance, Model, Optimizer, Config from thinc.api import set_dropout_rate from ..kb import KnowledgeBase, Candidate from ..ml import empty_kb from ..tokens import Doc, Span from .pipe import deserialize_config from .legacy.entity_linker import EntityLinker_v1 from .trainable_pipe import TrainablePipe from ..language import Language from ..vocab import Vocab from ..training import Example, validate_examples, validate_get_examples from ..errors import Errors from ..util import SimpleFrozenList, registry from .. import util from ..scorer import Scorer ActivationsT = Dict[str, Union[List[Ragged], List[str]]] KNOWLEDGE_BASE_IDS = "kb_ids" # See #9050 BACKWARD_OVERWRITE = True default_model_config = """ [model] @architectures = "spacy.EntityLinker.v2" [model.tok2vec] @architectures = "spacy.HashEmbedCNN.v2" pretrained_vectors = null width = 96 depth = 2 embed_size = 2000 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={ "model": DEFAULT_NEL_MODEL, "labels_discard": [], "n_sents": 0, "incl_prior": True, "incl_context": True, "entity_vector_length": 64, "get_candidates": {"@misc": "spacy.CandidateGenerator.v1"}, "get_candidates_batch": {"@misc": "spacy.CandidateBatchGenerator.v1"}, "overwrite": True, "scorer": {"@scorers": "spacy.entity_linker_scorer.v1"}, "use_gold_ents": True, "candidates_batch_size": 1, "threshold": None, "save_activations": False, }, default_score_weights={ "nel_micro_f": 1.0, "nel_micro_r": None, "nel_micro_p": None, }, ) def make_entity_linker( nlp: Language, name: str, model: Model, *, labels_discard: Iterable[str], n_sents: int, incl_prior: bool, incl_context: bool, entity_vector_length: int, get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]], get_candidates_batch: Callable[ [KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]] ], overwrite: bool, scorer: Optional[Callable], use_gold_ents: bool, candidates_batch_size: int, threshold: Optional[float] = None, save_activations: bool, ): """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. labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction. n_sents (int): The number of neighbouring sentences to take into account. 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. entity_vector_length (int): Size of encoding vectors in the KB. get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that produces a list of candidates, given a certain knowledge base and a textual mention. get_candidates_batch ( Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]], Iterable[Candidate]] ): Function that produces a list of candidates, given a certain knowledge base and several textual mentions. scorer (Optional[Callable]): The scoring method. use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another component must provide entity annotations. candidates_batch_size (int): Size of batches for entity candidate generation. threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the threshold, prediction is discarded. If None, predictions are not filtered by any threshold. save_activations (bool): save model activations in Doc when annotating. """ if not model.attrs.get("include_span_maker", False): # The only difference in arguments here is that use_gold_ents and threshold aren't available. return EntityLinker_v1( nlp.vocab, model, name, labels_discard=labels_discard, n_sents=n_sents, incl_prior=incl_prior, incl_context=incl_context, entity_vector_length=entity_vector_length, get_candidates=get_candidates, overwrite=overwrite, scorer=scorer, ) return EntityLinker( nlp.vocab, model, name, labels_discard=labels_discard, n_sents=n_sents, incl_prior=incl_prior, incl_context=incl_context, entity_vector_length=entity_vector_length, get_candidates=get_candidates, get_candidates_batch=get_candidates_batch, overwrite=overwrite, scorer=scorer, use_gold_ents=use_gold_ents, candidates_batch_size=candidates_batch_size, threshold=threshold, save_activations=save_activations, ) def entity_linker_score(examples, **kwargs): return Scorer.score_links(examples, negative_labels=[EntityLinker.NIL], **kwargs) @registry.scorers("spacy.entity_linker_scorer.v1") def make_entity_linker_scorer(): return entity_linker_score class EntityLinker(TrainablePipe): """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", *, labels_discard: Iterable[str], n_sents: int, incl_prior: bool, incl_context: bool, entity_vector_length: int, get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]], get_candidates_batch: Callable[ [KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]] ], overwrite: bool = BACKWARD_OVERWRITE, scorer: Optional[Callable] = entity_linker_score, use_gold_ents: bool, candidates_batch_size: int, threshold: Optional[float] = None, save_activations: bool = False, ) -> 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. labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction. n_sents (int): The number of neighbouring sentences to take into account. 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. entity_vector_length (int): Size of encoding vectors in the KB. get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that produces a list of candidates, given a certain knowledge base and a textual mention. get_candidates_batch ( Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]], Iterable[Candidate]] ): Function that produces a list of candidates, given a certain knowledge base and several textual mentions. scorer (Optional[Callable]): The scoring method. Defaults to Scorer.score_links. use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another component must provide entity annotations. candidates_batch_size (int): Size of batches for entity candidate generation. threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the threshold, prediction is discarded. If None, predictions are not filtered by any threshold. DOCS: https://spacy.io/api/entitylinker#init """ if threshold is not None and not (0 <= threshold <= 1): raise ValueError( Errors.E1043.format( range_start=0, range_end=1, value=threshold, ) ) self.vocab = vocab self.model = model self.name = name self.labels_discard = list(labels_discard) self.n_sents = n_sents self.incl_prior = incl_prior self.incl_context = incl_context self.get_candidates = get_candidates self.get_candidates_batch = get_candidates_batch self.cfg: Dict[str, Any] = {"overwrite": overwrite} self.distance = CosineDistance(normalize=False) # how many neighbour sentences to take into account # create an empty KB by default self.kb = empty_kb(entity_vector_length)(self.vocab) self.scorer = scorer self.use_gold_ents = use_gold_ents self.candidates_batch_size = candidates_batch_size self.threshold = threshold self.save_activations = save_activations if candidates_batch_size < 1: raise ValueError(Errors.E1044) def set_kb(self, kb_loader: Callable[[Vocab], KnowledgeBase]): """Define the KB of this pipe by providing a function that will create it using this object's vocab.""" if not callable(kb_loader): raise ValueError(Errors.E885.format(arg_type=type(kb_loader))) self.kb = kb_loader(self.vocab) # type: ignore def validate_kb(self) -> None: # Raise an error if the knowledge base is not initialized. if self.kb is None: raise ValueError(Errors.E1018.format(name=self.name)) if len(self.kb) == 0: raise ValueError(Errors.E139.format(name=self.name)) def initialize( self, get_examples: Callable[[], Iterable[Example]], *, nlp: Optional[Language] = None, kb_loader: Optional[Callable[[Vocab], KnowledgeBase]] = None, ): """Initialize the pipe for training, using a representative set of data examples. get_examples (Callable[[], Iterable[Example]]): Function that returns a representative sample of gold-standard Example objects. nlp (Language): The current nlp object the component is part of. kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab instance. Note that providing this argument will overwrite all data accumulated in the current KB. Use this only when loading a KB as-such from file. DOCS: https://spacy.io/api/entitylinker#initialize """ validate_get_examples(get_examples, "EntityLinker.initialize") if kb_loader is not None: self.set_kb(kb_loader) self.validate_kb() nO = self.kb.entity_vector_length doc_sample = [] vector_sample = [] for eg in islice(get_examples(), 10): doc = eg.x if self.use_gold_ents: ents, _ = eg.get_aligned_ents_and_ner() doc.ents = ents doc_sample.append(doc) vector_sample.append(self.model.ops.alloc1f(nO)) assert len(doc_sample) > 0, Errors.E923.format(name=self.name) assert len(vector_sample) > 0, Errors.E923.format(name=self.name) # XXX In order for size estimation to work, there has to be at least # one entity. It's not used for training so it doesn't have to be real, # so we add a fake one if none are present. # We can't use Doc.has_annotation here because it can be True for docs # that have been through an NER component but got no entities. has_annotations = any([doc.ents for doc in doc_sample]) if not has_annotations: doc = doc_sample[0] ent = doc[0:1] ent.label_ = "XXX" doc.ents = (ent,) self.model.initialize( X=doc_sample, Y=self.model.ops.asarray(vector_sample, dtype="float32") ) if not has_annotations: # Clean up dummy annotation doc.ents = [] def batch_has_learnable_example(self, examples): """Check if a batch contains a learnable example. If one isn't present, then the update step needs to be skipped. """ for eg in examples: for ent in eg.predicted.ents: candidates = list(self.get_candidates(self.kb, ent)) if candidates: return True return False def update( self, examples: Iterable[Example], *, 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. 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.validate_kb() if losses is None: losses = {} losses.setdefault(self.name, 0.0) if not examples: return losses validate_examples(examples, "EntityLinker.update") set_dropout_rate(self.model, drop) docs = [eg.predicted for eg in examples] # save to restore later old_ents = [doc.ents for doc in docs] for doc, ex in zip(docs, examples): if self.use_gold_ents: ents, _ = ex.get_aligned_ents_and_ner() doc.ents = ents else: # only keep matching ents doc.ents = ex.get_matching_ents() # make sure we have something to learn from, if not, short-circuit if not self.batch_has_learnable_example(examples): return losses sentence_encodings, bp_context = self.model.begin_update(docs) # now restore the ents for doc, old in zip(docs, old_ents): doc.ents = old loss, d_scores = self.get_loss( sentence_encodings=sentence_encodings, examples=examples ) bp_context(d_scores) if sgd is not None: self.finish_update(sgd) losses[self.name] += loss return losses def get_loss(self, examples: Iterable[Example], sentence_encodings: Floats2d): validate_examples(examples, "EntityLinker.get_loss") entity_encodings = [] eidx = 0 # indices in gold entities to keep keep_ents = [] # indices in sentence_encodings to keep for eg in examples: kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True) for ent in eg.get_matching_ents(): kb_id = kb_ids[ent.start] if kb_id: entity_encoding = self.kb.get_vector(kb_id) entity_encodings.append(entity_encoding) keep_ents.append(eidx) eidx += 1 entity_encodings = self.model.ops.asarray2f(entity_encodings, dtype="float32") selected_encodings = sentence_encodings[keep_ents] # if there are no matches, short circuit if not keep_ents: out = self.model.ops.alloc2f(*sentence_encodings.shape) return 0, out if selected_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(selected_encodings, entity_encodings) # to match the input size, we need to give a zero gradient for items not in the kb out = self.model.ops.alloc2f(*sentence_encodings.shape) out[keep_ents] = gradients loss = self.distance.get_loss(selected_encodings, entity_encodings) loss = loss / len(entity_encodings) return float(loss), out def predict(self, docs: Iterable[Doc]) -> ActivationsT: """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[str]): The models prediction for each document. DOCS: https://spacy.io/api/entitylinker#predict """ self.validate_kb() entity_count = 0 final_kb_ids: List[str] = [] ops = self.model.ops xp = ops.xp docs_ents: List[Ragged] = [] docs_scores: List[Ragged] = [] if not docs: return {KNOWLEDGE_BASE_IDS: final_kb_ids, "ents": docs_ents, "scores": docs_scores} if isinstance(docs, Doc): docs = [docs] for doc in docs: doc_ents: List[Ints1d] = [] doc_scores: List[Floats1d] = [] if len(doc) == 0: docs_scores.append(Ragged(ops.alloc1f(0), ops.alloc1i(0))) docs_ents.append(Ragged(xp.zeros(0, dtype="uint64"), ops.alloc1i(0))) continue sentences = [s for s in doc.sents] # Loop over entities in batches. for ent_idx in range(0, len(doc.ents), self.candidates_batch_size): ent_batch = doc.ents[ent_idx : ent_idx + self.candidates_batch_size] # Look up candidate entities. valid_ent_idx = [ idx for idx in range(len(ent_batch)) if ent_batch[idx].label_ not in self.labels_discard ] batch_candidates = list( self.get_candidates_batch( self.kb, [ent_batch[idx] for idx in valid_ent_idx] ) if self.candidates_batch_size > 1 else [ self.get_candidates(self.kb, ent_batch[idx]) for idx in valid_ent_idx ] ) # Looping through each entity in batch (TODO: rewrite) for j, ent in enumerate(ent_batch): sent_index = sentences.index(ent.sent) assert sent_index >= 0 if self.incl_context: # 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.predict([sent_doc])[0] sentence_encoding_t = sentence_encoding.T sentence_norm = xp.linalg.norm(sentence_encoding_t) entity_count += 1 if ent.label_ in self.labels_discard: # ignoring this entity - setting to NIL final_kb_ids.append(self.NIL) self._add_activations( doc_scores=doc_scores, doc_ents=doc_ents, scores=[0.0], ents=[0], ) else: candidates = list(batch_candidates[j]) if not candidates: # no prediction possible for this entity - setting to NIL final_kb_ids.append(self.NIL) self._add_activations( doc_scores=doc_scores, doc_ents=doc_ents, scores=[0.0], ents=[0], ) elif len(candidates) == 1 and self.threshold is None: # shortcut for efficiency reasons: take the 1 candidate final_kb_ids.append(candidates[0].entity_) self._add_activations( doc_scores=doc_scores, doc_ents=doc_ents, scores=[1.0], ents=[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.incl_prior: prior_probs = xp.asarray([0.0 for _ in candidates]) scores = prior_probs # add in similarity from the context if self.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) final_kb_ids.append( candidates[scores.argmax().item()].entity_ if self.threshold is None or scores.max() >= self.threshold else EntityLinker.NIL ) self._add_activations( doc_scores=doc_scores, doc_ents=doc_ents, scores=scores, ents=[c.entity for c in candidates], ) self._add_doc_activations( docs_scores=docs_scores, docs_ents=docs_ents, doc_scores=doc_scores, doc_ents=doc_ents, ) 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 {KNOWLEDGE_BASE_IDS: final_kb_ids, "ents": docs_ents, "scores": docs_scores} def set_annotations(self, docs: Iterable[Doc], activations: ActivationsT) -> None: """Modify a batch of documents, using pre-computed scores. docs (Iterable[Doc]): The documents to modify. activations (ActivationsT): The activations used for setting annotations, produced by EntityLinker.predict. DOCS: https://spacy.io/api/entitylinker#set_annotations """ kb_ids = cast(List[str], activations[KNOWLEDGE_BASE_IDS]) 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 overwrite = self.cfg["overwrite"] for j, doc in enumerate(docs): if self.save_activations: doc.activations[self.name] = {} for act_name, acts in activations.items(): if act_name != KNOWLEDGE_BASE_IDS: # We only copy activations that are Ragged. doc.activations[self.name][act_name] = cast(Ragged, acts[j]) for ent in doc.ents: kb_id = kb_ids[i] i += 1 for token in ent: if token.ent_kb_id == 0 or overwrite: token.ent_kb_id_ = kb_id def to_bytes(self, *, exclude=tuple()): """Serialize the pipe to a bytestring. exclude (Iterable[str]): String names of serialization fields to exclude. RETURNS (bytes): The serialized object. DOCS: https://spacy.io/api/entitylinker#to_bytes """ self._validate_serialization_attrs() serialize = {} if hasattr(self, "cfg") and self.cfg is not None: serialize["cfg"] = lambda: srsly.json_dumps(self.cfg) serialize["vocab"] = lambda: self.vocab.to_bytes(exclude=exclude) serialize["kb"] = self.kb.to_bytes serialize["model"] = self.model.to_bytes return util.to_bytes(serialize, exclude) def from_bytes(self, bytes_data, *, exclude=tuple()): """Load the pipe from a bytestring. exclude (Iterable[str]): String names of serialization fields to exclude. RETURNS (TrainablePipe): The loaded object. DOCS: https://spacy.io/api/entitylinker#from_bytes """ self._validate_serialization_attrs() def load_model(b): try: self.model.from_bytes(b) except AttributeError: raise ValueError(Errors.E149) from None deserialize = {} if hasattr(self, "cfg") and self.cfg is not None: deserialize["cfg"] = lambda b: self.cfg.update(srsly.json_loads(b)) deserialize["vocab"] = lambda b: self.vocab.from_bytes(b, exclude=exclude) deserialize["kb"] = lambda b: self.kb.from_bytes(b) deserialize["model"] = load_model util.from_bytes(bytes_data, deserialize, exclude) return self 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["vocab"] = lambda p: self.vocab.to_disk(p, exclude=exclude) serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg) 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: with p.open("rb") as infile: self.model.from_bytes(infile.read()) except AttributeError: raise ValueError(Errors.E149) from None deserialize: Dict[str, Callable[[Any], Any]] = {} deserialize["cfg"] = lambda p: self.cfg.update(deserialize_config(p)) deserialize["vocab"] = lambda p: self.vocab.from_disk(p, exclude=exclude) 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 def _add_doc_activations( self, *, docs_scores: List[Ragged], docs_ents: List[Ragged], doc_scores: List[Floats1d], doc_ents: List[Ints1d], ): if not self.save_activations: return ops = self.model.ops lengths = ops.asarray1i([s.shape[0] for s in doc_scores]) docs_scores.append(Ragged(ops.flatten(doc_scores), lengths)) docs_ents.append(Ragged(ops.flatten(doc_ents), lengths)) def _add_activations( self, *, doc_scores: List[Floats1d], doc_ents: List[Ints1d], scores: Sequence[float], ents: Sequence[int], ): if not self.save_activations: return ops = self.model.ops doc_scores.append(ops.asarray1f(scores)) doc_ents.append(ops.asarray1i(ents, dtype="uint64"))