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grouping clusters of instances per doc+mention
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svlandeg
parent
c6ca8649d7
commit
9d089c0410
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@ -7,7 +7,7 @@ from os import listdir
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from examples.pipeline.wiki_entity_linking import run_el, training_set_creator, kb_creator
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from spacy._ml import SpacyVectors, create_default_optimizer, zero_init
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from spacy._ml import SpacyVectors, create_default_optimizer, zero_init, cosine
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from thinc.api import chain
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from thinc.v2v import Model, Maxout, Softmax, Affine, ReLu
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@ -33,14 +33,12 @@ class EL_Model():
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self.article_encoder = self._simple_encoder(width=300)
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def train_model(self, training_dir, entity_descr_output, limit=None, to_print=True):
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instances, gold_vectors, entity_descriptions, doc_by_article = self._get_training_data(training_dir,
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instances, pos_entities, neg_entities, doc_by_article = self._get_training_data(training_dir,
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entity_descr_output,
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limit, to_print)
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if to_print:
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print("Training on", len(gold_vectors), "instances")
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print(" - pos:", len([x for x in gold_vectors if x]), "instances")
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print(" - pos:", len([x for x in gold_vectors if not x]), "instances")
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print("Training on", len(instances), "instance clusters")
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print()
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self.sgd_entity = self.begin_training(self.entity_encoder)
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@ -48,11 +46,20 @@ class EL_Model():
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losses = {}
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for inst, label, entity_descr in zip(instances, gold_vectors, entity_descriptions):
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article = inst.split(sep="_")[0]
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entity_id = inst.split(sep="_")[1]
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article_doc = doc_by_article[article]
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self.update(article_doc, entity_descr, label, losses=losses)
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for inst_cluster in instances:
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pos_ex = pos_entities.get(inst_cluster)
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neg_exs = neg_entities.get(inst_cluster, [])
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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 = doc_by_article[article]
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self.update(article_doc, pos_ex, neg_exs, losses=losses)
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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 _simple_encoder(self, width):
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with Model.define_operators({">>": chain}):
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@ -69,22 +76,29 @@ 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, entity_descr, label, drop=0., losses=None):
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entity_encoding, entity_bp = self.entity_encoder.begin_update([entity_descr], drop=drop)
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def update(self, article_doc, true_entity, false_entities, drop=0., losses=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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# true_similarity = cosine(true_entity_encoding, doc_encoding)
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# print("true_similarity", true_similarity)
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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_similarity = cosine(false_entity_encoding, doc_encoding)
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# print("false_similarity", false_similarity)
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# print("entity/article output dim", len(entity_encoding[0]), len(doc_encoding[0]))
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mse, diffs = self._calculate_similarity(entity_encoding, doc_encoding)
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mse, diffs = self._calculate_similarity(true_entity_encoding, doc_encoding)
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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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if label:
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entity_bp(diffs, sgd=self.sgd_entity)
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article_bp(diffs, sgd=self.sgd_article)
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print(mse)
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true_entity_bp(diffs, sgd=self.sgd_entity)
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article_bp(diffs, sgd=self.sgd_article)
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print(mse)
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# TODO delete ?
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@ -115,7 +129,7 @@ class EL_Model():
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raise ValueError("To calculate similarity, both vectors should be of equal length")
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diffs = (vector2 - vector1)
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error_sum = (diffs ** 2).sum(axis=1)
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error_sum = (diffs ** 2).sum()
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mean_square_error = error_sum / len(vector1)
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return float(mean_square_error), diffs
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@ -130,10 +144,10 @@ class EL_Model():
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collect_incorrect=True)
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instances = list()
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entity_descriptions = list()
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local_vectors = list() # TODO: local vectors
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gold_vectors = list()
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doc_by_article = dict()
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pos_entities = dict()
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neg_entities = dict()
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cnt = 0
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for f in listdir(training_dir):
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@ -149,25 +163,24 @@ class EL_Model():
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doc = self.nlp(text)
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doc_by_article[article_id] = doc
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for mention_pos, entity_pos in correct_entries[article_id].items():
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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 + "_" + entity_pos)
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doc = self.nlp(descr)
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entity_descriptions.append(doc)
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gold_vectors.append(True)
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instances.append(article_id + "_" + mention)
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doc_descr = self.nlp(descr)
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pos_entities[article_id + "_" + mention] = doc_descr
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for mention_neg, entity_negs in incorrect_entries[article_id].items():
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for mention, entity_negs in incorrect_entries[article_id].items():
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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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instances.append(article_id + "_" + entity_neg)
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doc = self.nlp(descr)
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entity_descriptions.append(doc)
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gold_vectors.append(False)
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doc_descr = self.nlp(descr)
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descr_list = neg_entities.get(article_id + "_" + mention, [])
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descr_list.append(doc_descr)
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neg_entities[article_id + "_" + mention] = descr_list
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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()
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return instances, gold_vectors, entity_descriptions, doc_by_article
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return instances, pos_entities, neg_entities, doc_by_article
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