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Minor NEL type fixes (#10860)
* Fix TODO about typing Fix was simple: just request an array2f. * Add type ignore Maxout has a more restrictive type than the residual layer expects (only Floats2d vs any Floats). * Various cleanup This moves a lot of lines around but doesn't change any functionality. Details: 1. use `continue` to reduce indentation 2. move sentence doc building inside conditional since it's otherwise unused 3. reduces some temporary assignments
This commit is contained in:
Paul O'Leary McCann
GitHub
parent
56d4055d96
commit
dca2e8c644
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@ -23,7 +23,7 @@ def build_nel_encoder(
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((tok2vec >> list2ragged()) & build_span_maker())
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>> extract_spans()
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>> reduce_mean()
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>> residual(Maxout(nO=token_width, nI=token_width, nP=2, dropout=0.0))
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>> residual(Maxout(nO=token_width, nI=token_width, nP=2, dropout=0.0)) # type: ignore
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>> output_layer
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)
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model.set_ref("output_layer", output_layer)
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@ -355,7 +355,7 @@ class EntityLinker(TrainablePipe):
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keep_ents.append(eidx)
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eidx += 1
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entity_encodings = self.model.ops.asarray(entity_encodings, dtype="float32")
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entity_encodings = self.model.ops.asarray2f(entity_encodings, dtype="float32")
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selected_encodings = sentence_encodings[keep_ents]
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# if there are no matches, short circuit
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@ -368,13 +368,12 @@ class EntityLinker(TrainablePipe):
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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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# TODO: fix typing issue here
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gradients = self.distance.get_grad(selected_encodings, entity_encodings) # type: ignore
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gradients = self.distance.get_grad(selected_encodings, entity_encodings)
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# to match the input size, we need to give a zero gradient for items not in the kb
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out = self.model.ops.alloc2f(*sentence_encodings.shape)
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out[keep_ents] = gradients
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loss = self.distance.get_loss(selected_encodings, entity_encodings) # type: ignore
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loss = self.distance.get_loss(selected_encodings, entity_encodings)
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loss = loss / len(entity_encodings)
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return float(loss), out
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@ -391,18 +390,21 @@ class EntityLinker(TrainablePipe):
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self.validate_kb()
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entity_count = 0
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final_kb_ids: List[str] = []
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xp = self.model.ops.xp
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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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if len(doc) == 0:
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continue
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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 entity (TODO: rewrite)
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for ent in doc.ents:
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sent = ent.sent
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sent_index = sentences.index(sent)
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assert sent_index >= 0
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# Looping through each entity (TODO: rewrite)
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for ent in doc.ents:
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sent_index = sentences.index(ent.sent)
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assert sent_index >= 0
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if self.incl_context:
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# get n_neighbour 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(len(sentences) - 1, sent_index + self.n_sents)
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@ -410,55 +412,53 @@ class EntityLinker(TrainablePipe):
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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.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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entity_count += 1
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if ent.label_ in self.labels_discard:
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# ignoring this entity - setting to NIL
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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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entity_count += 1
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if ent.label_ in self.labels_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 = list(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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candidates = list(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([c.prior_prob for c in candidates])
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if not self.incl_prior:
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prior_probs = xp.asarray([0.0 for _ in candidates])
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scores = prior_probs
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# add in similarity from the context
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if self.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(entity_encodings, axis=1)
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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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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([c.prior_prob for c in candidates])
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if not self.incl_prior:
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prior_probs = xp.asarray([0.0 for _ in candidates])
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scores = prior_probs
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# add in similarity from the context
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if self.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(entity_encodings, axis=1)
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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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# cosine similarity
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sims = xp.dot(entity_encodings, sentence_encoding_t) / (
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sentence_norm * entity_norm
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)
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if sims.shape != prior_probs.shape:
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raise ValueError(Errors.E161)
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scores = prior_probs + sims - (prior_probs * sims)
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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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# cosine similarity
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sims = xp.dot(entity_encodings, sentence_encoding_t) / (
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sentence_norm * entity_norm
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)
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if sims.shape != prior_probs.shape:
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raise ValueError(Errors.E161)
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scores = prior_probs + sims - (prior_probs * sims)
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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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