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train and predict per article (saving time for doc encoding)
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svlandeg
parent
3b81b00954
commit
4142e8dd1b
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@ -46,11 +46,11 @@ class EL_Model():
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dev_instances, dev_pos, dev_neg, dev_doc = self._get_training_data(training_dir,
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entity_descr_output,
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True,
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limit, to_print)
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limit / 10, to_print)
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if to_print:
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print("Training on", len(train_instances), "instance clusters")
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print("Dev test on", len(dev_instances), "instance clusters")
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print("Training on", len(train_instances.values()), "articles")
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print("Dev test on", len(dev_instances.values()), "articles")
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print()
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self.sgd_entity = self.begin_training(self.entity_encoder)
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@ -60,49 +60,51 @@ class EL_Model():
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losses = {}
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for inst_cluster in train_instances:
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pos_ex = train_pos.get(inst_cluster)
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neg_exs = train_neg.get(inst_cluster, [])
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instance_count = 0
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for article_id, inst_cluster_set in train_instances.items():
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article_doc = train_doc[article_id]
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pos_ex_list = list()
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neg_exs_list = list()
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for inst_cluster in inst_cluster_set:
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instance_count += 1
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pos_ex_list.append(train_pos.get(inst_cluster))
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neg_exs_list.append(train_neg.get(inst_cluster, []))
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self.update(article_doc, pos_ex_list, neg_exs_list, losses=losses)
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p, r, fscore = self._test_dev(dev_instances, dev_pos, dev_neg, dev_doc)
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print(round(fscore, 1))
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if to_print:
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print("Trained on", instance_count, "instance clusters")
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if pos_ex and neg_exs:
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article = inst_cluster.split(sep="_")[0]
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entity_id = inst_cluster.split(sep="_")[1]
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article_doc = train_doc[article]
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self.update(article_doc, pos_ex, neg_exs, losses=losses)
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p, r, fscore = self._test_dev(dev_instances, dev_pos, dev_neg, dev_doc)
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print(round(fscore, 1))
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# TODO
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# elif not pos_ex:
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# print("Weird. Couldn't find pos example for", inst_cluster)
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# elif not neg_exs:
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# print("Weird. Couldn't find neg examples for", inst_cluster)
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def _test_dev(self, dev_instances, dev_pos, dev_neg, dev_doc):
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predictions = list()
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golds = list()
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for inst_cluster in dev_instances:
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pos_ex = dev_pos.get(inst_cluster)
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neg_exs = dev_neg.get(inst_cluster, [])
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ex_to_id = dict()
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for article_id, inst_cluster_set in dev_instances.items():
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for inst_cluster in inst_cluster_set:
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pos_ex = dev_pos.get(inst_cluster)
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neg_exs = dev_neg.get(inst_cluster, [])
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ex_to_id = dict()
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if pos_ex and neg_exs:
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ex_to_id[pos_ex] = pos_ex._.entity_id
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for neg_ex in neg_exs:
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ex_to_id[neg_ex] = neg_ex._.entity_id
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if pos_ex and neg_exs:
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ex_to_id[pos_ex] = pos_ex._.entity_id
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for neg_ex in neg_exs:
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ex_to_id[neg_ex] = neg_ex._.entity_id
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article = inst_cluster.split(sep="_")[0]
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entity_id = inst_cluster.split(sep="_")[1]
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article_doc = dev_doc[article]
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article = inst_cluster.split(sep="_")[0]
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entity_id = inst_cluster.split(sep="_")[1]
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article_doc = dev_doc[article]
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examples = list(neg_exs)
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examples.append(pos_ex)
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shuffle(examples)
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best_entity, lowest_mse = self._predict(examples, article_doc)
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predictions.append(ex_to_id[best_entity])
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golds.append(ex_to_id[pos_ex])
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examples = list(neg_exs)
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examples.append(pos_ex)
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shuffle(examples)
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best_entity, lowest_mse = self._predict(examples, article_doc)
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predictions.append(ex_to_id[best_entity])
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golds.append(ex_to_id[pos_ex])
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# TODO: use lowest_mse and combine with prior probability
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p, r, F = run_el.evaluate(predictions, golds, to_print=False)
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@ -161,60 +163,79 @@ class EL_Model():
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sgd = create_default_optimizer(model.ops)
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return sgd
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def update(self, article_doc, true_entity, false_entities, drop=0., losses=None):
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def update(self, article_doc, true_entity_list, false_entities_list, drop=0., losses=None):
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# TODO: one call only to begin_update ?
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entity_diffs = None
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doc_diffs = None
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doc_encoding, article_bp = self.article_encoder.begin_update([article_doc], drop=drop)
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true_entity_encoding, true_entity_bp = self.entity_encoder.begin_update([true_entity], drop=drop)
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# print("encoding dim", len(true_entity_encoding[0]))
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for i, true_entity in enumerate(true_entity_list):
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false_entities = false_entities_list[i]
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consensus_encoding = self._calculate_consensus(doc_encoding, true_entity_encoding)
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consensus_encoding_t = consensus_encoding.transpose()
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true_entity_encoding, true_entity_bp = self.entity_encoder.begin_update([true_entity], drop=drop)
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# print("encoding dim", len(true_entity_encoding[0]))
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doc_mse, doc_diffs = self._calculate_similarity(doc_encoding, consensus_encoding)
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consensus_encoding = self._calculate_consensus(doc_encoding, true_entity_encoding)
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# consensus_encoding_t = consensus_encoding.transpose()
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entity_mses = list()
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doc_mse, doc_diff = self._calculate_similarity(doc_encoding, consensus_encoding)
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true_mse, true_diffs = self._calculate_similarity(true_entity_encoding, consensus_encoding)
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# print("true_mse", true_mse)
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# print("true_diffs", true_diffs)
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entity_mses.append(true_mse)
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# true_exp = np.exp(true_entity_encoding.dot(consensus_encoding_t))
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# print("true_exp", true_exp)
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entity_mses = list()
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# false_exp_sum = 0
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true_mse, true_diffs = self._calculate_similarity(true_entity_encoding, consensus_encoding)
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# print("true_mse", true_mse)
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# print("true_diffs", true_diffs)
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entity_mses.append(true_mse)
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# true_exp = np.exp(true_entity_encoding.dot(consensus_encoding_t))
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# print("true_exp", true_exp)
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for false_entity in false_entities:
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false_entity_encoding, false_entity_bp = self.entity_encoder.begin_update([false_entity], drop=drop)
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false_mse, false_diffs = self._calculate_similarity(false_entity_encoding, consensus_encoding)
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# print("false_mse", false_mse)
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# false_exp = np.exp(false_entity_encoding.dot(consensus_encoding_t))
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# print("false_exp", false_exp)
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# print("false_diffs", false_diffs)
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entity_mses.append(false_mse)
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# if false_mse > true_mse:
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# true_diffs = true_diffs - false_diffs ???
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# false_exp_sum += false_exp
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# false_exp_sum = 0
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# prob = true_exp / false_exp_sum
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# print("prob", prob)
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if doc_diffs is not None:
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doc_diffs += doc_diff
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entity_diffs += true_diffs
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else:
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doc_diffs = doc_diff
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entity_diffs = true_diffs
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entity_mses = sorted(entity_mses)
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# mse_sum = sum(entity_mses)
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# entity_probs = [1 - x/mse_sum for x in entity_mses]
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# print("entity_mses", entity_mses)
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# print("entity_probs", entity_probs)
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true_index = entity_mses.index(true_mse)
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# print("true index", true_index)
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# print("true prob", entity_probs[true_index])
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for false_entity in false_entities:
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false_entity_encoding, false_entity_bp = self.entity_encoder.begin_update([false_entity], drop=drop)
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false_mse, false_diffs = self._calculate_similarity(false_entity_encoding, consensus_encoding)
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# print("false_mse", false_mse)
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# false_exp = np.exp(false_entity_encoding.dot(consensus_encoding_t))
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# print("false_exp", false_exp)
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# print("false_diffs", false_diffs)
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entity_mses.append(false_mse)
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# if false_mse > true_mse:
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# true_diffs = true_diffs - false_diffs ???
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# false_exp_sum += false_exp
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# print("training loss", true_mse)
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# prob = true_exp / false_exp_sum
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# print("prob", prob)
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# print()
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entity_mses = sorted(entity_mses)
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# mse_sum = sum(entity_mses)
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# entity_probs = [1 - x/mse_sum for x in entity_mses]
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# print("entity_mses", entity_mses)
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# print("entity_probs", entity_probs)
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true_index = entity_mses.index(true_mse)
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# print("true index", true_index)
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# print("true prob", entity_probs[true_index])
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# print("training loss", true_mse)
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# print()
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# TODO: proper backpropagation taking ranking of elements into account ?
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# TODO backpropagation also for negative examples
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true_entity_bp(true_diffs, sgd=self.sgd_entity)
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article_bp(doc_diffs, sgd=self.sgd_article)
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if doc_diffs is not None:
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doc_diffs = doc_diffs / len(true_entity_list)
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true_entity_bp(entity_diffs, sgd=self.sgd_entity)
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article_bp(doc_diffs, sgd=self.sgd_article)
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# TODO delete ?
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@ -268,7 +289,7 @@ class EL_Model():
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collect_incorrect=True)
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instances = list()
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instance_by_doc = dict()
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local_vectors = list() # TODO: local vectors
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doc_by_article = dict()
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pos_entities = dict()
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@ -280,18 +301,19 @@ class EL_Model():
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if dev == run_el.is_dev(f):
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article_id = f.replace(".txt", "")
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if cnt % 500 == 0 and to_print:
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print(datetime.datetime.now(), "processed", cnt, "files in the dev dataset")
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print(datetime.datetime.now(), "processed", cnt, "files in the training dataset")
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cnt += 1
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if article_id not in doc_by_article:
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with open(os.path.join(training_dir, f), mode="r", encoding='utf8') as file:
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text = file.read()
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doc = self.nlp(text)
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doc_by_article[article_id] = doc
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instance_by_doc[article_id] = set()
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for mention, entity_pos in correct_entries[article_id].items():
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descr = id_to_descr.get(entity_pos)
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if descr:
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instances.append(article_id + "_" + mention)
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instance_by_doc[article_id].add(article_id + "_" + mention)
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doc_descr = self.nlp(descr)
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doc_descr._.entity_id = entity_pos
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pos_entities[article_id + "_" + mention] = doc_descr
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@ -308,6 +330,6 @@ class EL_Model():
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if to_print:
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print()
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print("Processed", cnt, "dev articles")
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print("Processed", cnt, "training articles, dev=" + str(dev))
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print()
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return instances, pos_entities, neg_entities, doc_by_article
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return instance_by_doc, pos_entities, neg_entities, doc_by_article
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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, limit=50)
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trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, limit=500)
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print()
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# STEP 7: apply the EL algorithm on the dev dataset
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