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update per entity
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svlandeg
parent
eb08bdb11f
commit
1a16490d20
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@ -154,7 +154,7 @@ class EL_Model:
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if self.PRINT_F:
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print("p/r/F", print_string, round(p, 1), round(r, 1), round(f, 1))
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loss, d_scores = self.get_loss(self.model.ops.asarray(predictions), self.model.ops.asarray(golds))
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loss, gradient = self.get_loss(self.model.ops.asarray(predictions), self.model.ops.asarray(golds))
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if self.PRINT_LOSS:
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print("loss", print_string, round(loss, 5))
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@ -235,62 +235,58 @@ class EL_Model:
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@staticmethod
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def get_loss(predictions, golds):
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d_scores = (predictions - golds)
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loss = (d_scores ** 2).sum()
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loss = (d_scores ** 2).mean()
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return loss, d_scores
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# TODO: multiple docs/articles
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def update(self, article_text, entities, golds, apply_threshold=True):
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article_doc = self.nlp(article_text)
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doc_encodings, bp_doc = self.article_encoder.begin_update([article_doc], drop=self.DROP)
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doc_encoding = doc_encodings[0]
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# entity_docs = list(self.nlp.pipe(entities))
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entity_docs = list(self.nlp.pipe(entities))
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# print("entity_docs", type(entity_docs))
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for entity, gold in zip(entities, golds):
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doc_encodings, bp_doc = self.article_encoder.begin_update([article_doc], drop=self.DROP)
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doc_encoding = doc_encodings[0]
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entity_encodings, bp_entity = self.entity_encoder.begin_update(entity_docs, drop=self.DROP)
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# print("entity_encodings", len(entity_encodings), entity_encodings)
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entity_doc = self.nlp(entity)
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# print("entity_docs", type(entity_doc))
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concat_encodings = [list(entity_encodings[i]) + list(doc_encoding) for i in range(len(entities))]
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# print("concat_encodings", len(concat_encodings), concat_encodings)
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entity_encodings, bp_entity = self.entity_encoder.begin_update([entity_doc], drop=self.DROP)
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entity_encoding = entity_encodings[0]
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# print("entity_encoding", len(entity_encoding), entity_encoding)
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predictions, bp_model = self.model.begin_update(np.asarray(concat_encodings), drop=self.DROP)
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predictions = self.model.ops.flatten(predictions)
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concat_encodings = [list(entity_encoding) + list(doc_encoding)] # for i in range(len(entities))
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# print("concat_encodings", len(concat_encodings), concat_encodings)
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# print("predictions", predictions)
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golds = self.model.ops.asarray(golds)
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# print("golds", golds)
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prediction, bp_model = self.model.begin_update(np.asarray(concat_encodings), drop=self.DROP)
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# predictions = self.model.ops.flatten(predictions)
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loss, d_scores = self.get_loss(predictions, golds)
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# print("prediction", prediction)
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# golds = self.model.ops.asarray(golds)
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# print("gold", gold)
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if self.PRINT_LOSS and self.PRINT_TRAIN:
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print("loss train", round(loss, 5))
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loss, gradient = self.get_loss(prediction, gold)
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if self.PRINT_F and self.PRINT_TRAIN:
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predictions_f = [x for x in predictions]
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if apply_threshold:
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predictions_f = [float(1.0) if x > self.CUTOFF else float(0.0) for x in predictions_f]
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p, r, f = run_el.evaluate(predictions_f, golds, to_print=False)
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print("p/r/F train", round(p, 1), round(r, 1), round(f, 1))
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if self.PRINT_LOSS and self.PRINT_TRAIN:
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print("loss train", round(loss, 5))
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d_scores = d_scores.reshape((-1, 1))
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d_scores = d_scores.astype(np.float32)
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# print("d_scores", d_scores)
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gradient = float(gradient)
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# print("gradient", gradient)
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# print("loss", loss)
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model_gradient = bp_model(d_scores, sgd=self.sgd)
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# print("model_gradient", model_gradient)
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model_gradient = bp_model(gradient, sgd=self.sgd)
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# print("model_gradient", model_gradient)
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# concat = entity + doc, but doc is the same within this function (TODO: multiple docs/articles)
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doc_gradient = model_gradient[0][self.ENTITY_WIDTH:]
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entity_gradients = list()
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for x in model_gradient:
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entity_gradients.append(list(x[0:self.ENTITY_WIDTH]))
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# concat = entity + doc, but doc is the same within this function (TODO: multiple docs/articles)
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doc_gradient = model_gradient[0][self.ENTITY_WIDTH:]
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entity_gradients = list()
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for x in model_gradient:
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entity_gradients.append(list(x[0:self.ENTITY_WIDTH]))
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# print("doc_gradient", doc_gradient)
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# print("entity_gradients", entity_gradients)
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# print("doc_gradient", doc_gradient)
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# print("entity_gradients", entity_gradients)
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bp_doc([doc_gradient], sgd=self.sgd_article)
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bp_entity(entity_gradients, sgd=self.sgd_entity)
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bp_doc([doc_gradient], sgd=self.sgd_article)
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bp_entity(entity_gradients, sgd=self.sgd_entity)
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def _get_training_data(self, training_dir, entity_descr_output, dev, limit, balance, to_print):
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id_to_descr = kb_creator._get_id_to_description(entity_descr_output)
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@ -326,16 +322,17 @@ class EL_Model:
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pos_entities[article_id + "_" + mention] = descr
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for mention, entity_negs in incorrect_entries[article_id].items():
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neg_count = 0
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for entity_neg in entity_negs:
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descr = id_to_descr.get(entity_neg)
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if descr:
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if not balance or pos_entities.get(article_id + "_" + mention):
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neg_count = 0
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for entity_neg in entity_negs:
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# if balance, keep only 1 negative instance for each positive instance
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if neg_count < 1 or not balance:
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descr_list = neg_entities.get(article_id + "_" + mention, [])
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descr_list.append(descr)
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neg_entities[article_id + "_" + mention] = descr_list
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neg_count += 1
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descr = id_to_descr.get(entity_neg)
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if descr:
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descr_list = neg_entities.get(article_id + "_" + mention, [])
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descr_list.append(descr)
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neg_entities[article_id + "_" + mention] = descr_list
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neg_count += 1
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if to_print:
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print()
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@ -111,7 +111,7 @@ if __name__ == "__main__":
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print("STEP 6: training", datetime.datetime.now())
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my_nlp = spacy.load('en_core_web_md')
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trainer = EL_Model(kb=my_kb, nlp=my_nlp)
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trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=1000, devlimit=20)
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trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=100, devlimit=20)
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print()
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# STEP 7: apply the EL algorithm on the dev dataset
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