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upsampling and batch processing
This commit is contained in:
svlandeg
parent
1a16490d20
commit
97241a3ed7
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@ -78,8 +78,15 @@ def evaluate(predictions, golds, to_print=True):
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fp = 0
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fn = 0
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corrects = 0
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incorrects = 0
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for pred, gold in zip(predictions, golds):
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is_correct = pred == gold
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if is_correct:
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corrects += 1
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else:
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incorrects += 1
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if not pred:
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if not is_correct: # we don't care about tn
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fn += 1
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@ -98,12 +105,15 @@ def evaluate(predictions, golds, to_print=True):
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recall = 100 * tp / (tp + fn + 0.0000001)
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fscore = 2 * recall * precision / (recall + precision + 0.0000001)
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accuracy = corrects / (corrects + incorrects)
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if to_print:
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print("precision", round(precision, 1), "%")
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print("recall", round(recall, 1), "%")
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print("Fscore", round(fscore, 1), "%")
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print("Accuracy", round(accuracy, 1), "%")
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return precision, recall, fscore
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return precision, recall, fscore, accuracy
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def _prepare_pipeline(nlp, kb):
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@ -6,6 +6,7 @@ import datetime
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from os import listdir
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import numpy as np
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import random
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from random import shuffle
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from thinc.neural._classes.convolution import ExtractWindow
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from examples.pipeline.wiki_entity_linking import run_el, training_set_creator, kb_creator
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@ -26,17 +27,17 @@ from spacy.tokens import Doc
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class EL_Model:
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PRINT_LOSS = False
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PRINT_F = True
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PRINT_TRAIN = False
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EPS = 0.0000000005
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CUTOFF = 0.5
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BATCH_SIZE = 5
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INPUT_DIM = 300
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HIDDEN_1_WIDTH = 256 # 10
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HIDDEN_1_WIDTH = 32 # 10
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HIDDEN_2_WIDTH = 32 # 6
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ENTITY_WIDTH = 64 # 4
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ARTICLE_WIDTH = 128 # 8
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DESC_WIDTH = 64 # 4
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ARTICLE_WIDTH = 64 # 8
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DROP = 0.1
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@ -48,7 +49,7 @@ class EL_Model:
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self.kb = kb
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self._build_cnn(in_width=self.INPUT_DIM,
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entity_width=self.ENTITY_WIDTH,
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desc_width=self.DESC_WIDTH,
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article_width=self.ARTICLE_WIDTH,
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hidden_1_width=self.HIDDEN_1_WIDTH,
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hidden_2_width=self.HIDDEN_2_WIDTH)
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@ -57,121 +58,118 @@ class EL_Model:
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# raise errors instead of runtime warnings in case of int/float overflow
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np.seterr(all='raise')
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train_inst, train_pos, train_neg, train_texts = self._get_training_data(training_dir,
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entity_descr_output,
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False,
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trainlimit,
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balance=True,
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to_print=False)
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train_ent, train_gold, train_desc, train_article, train_texts = self._get_training_data(training_dir,
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entity_descr_output,
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False,
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trainlimit,
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to_print=False)
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train_pos_entities = [k for k,v in train_gold.items() if v]
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train_neg_entities = [k for k,v in train_gold.items() if not v]
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train_pos_count = len(train_pos_entities)
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train_neg_count = len(train_neg_entities)
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# upsample positives to 50-50 distribution
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while train_pos_count < train_neg_count:
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train_ent.append(random.choice(train_pos_entities))
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train_pos_count += 1
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# upsample negatives to 50-50 distribution
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while train_neg_count < train_pos_count:
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train_ent.append(random.choice(train_neg_entities))
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train_neg_count += 1
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shuffle(train_ent)
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dev_ent, dev_gold, dev_desc, dev_article, dev_texts = self._get_training_data(training_dir,
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entity_descr_output,
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True,
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devlimit,
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to_print=False)
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shuffle(dev_ent)
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dev_pos_count = len([g for g in dev_gold.values() if g])
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dev_neg_count = len([g for g in dev_gold.values() if not g])
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dev_inst, dev_pos, dev_neg, dev_texts = self._get_training_data(training_dir,
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entity_descr_output,
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True,
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devlimit,
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balance=False,
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to_print=False)
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self._begin_training()
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print()
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self._test_dev(dev_inst, dev_pos, dev_neg, dev_texts, print_string="dev_random", calc_random=True)
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self._test_dev(dev_inst, dev_pos, dev_neg, dev_texts, print_string="dev_pre", avg=False)
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instance_pos_count = 0
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instance_neg_count = 0
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self._test_dev(dev_ent, dev_gold, dev_desc, dev_article, dev_texts, print_string="dev_random", calc_random=True)
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print()
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self._test_dev(dev_ent, dev_gold, dev_desc, dev_article, dev_texts, print_string="dev_pre", avg=True)
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if to_print:
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print()
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print("Training on", len(train_inst.values()), "articles")
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print("Dev test on", len(dev_inst.values()), "articles")
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print("Training on", len(train_ent), "entities in", len(train_texts), "articles")
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print("Training instances pos/neg", train_pos_count, train_neg_count)
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print()
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print("Dev test on", len(dev_ent), "entities in", len(dev_texts), "articles")
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print("Dev instances pos/neg", dev_pos_count, dev_neg_count)
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print()
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print(" CUTOFF", self.CUTOFF)
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print(" INPUT_DIM", self.INPUT_DIM)
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print(" HIDDEN_1_WIDTH", self.HIDDEN_1_WIDTH)
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print(" ENTITY_WIDTH", self.ENTITY_WIDTH)
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print(" DESC_WIDTH", self.DESC_WIDTH)
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print(" ARTICLE_WIDTH", self.ARTICLE_WIDTH)
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print(" HIDDEN_2_WIDTH", self.HIDDEN_2_WIDTH)
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print(" DROP", self.DROP)
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print()
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# TODO: proper batches. Currently 1 article at the time
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# TODO shuffle data (currently positive is always followed by several negatives)
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article_count = 0
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for article_id, inst_cluster_set in train_inst.items():
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try:
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# if to_print:
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# print()
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# print(article_count, "Training on article", article_id)
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article_count += 1
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article_text = train_texts[article_id]
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entities = list()
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golds = list()
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for inst_cluster in inst_cluster_set:
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entities.append(train_pos.get(inst_cluster))
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golds.append(float(1.0))
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instance_pos_count += 1
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for neg_entity in train_neg.get(inst_cluster, []):
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entities.append(neg_entity)
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golds.append(float(0.0))
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instance_neg_count += 1
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start = 0
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stop = min(self.BATCH_SIZE, len(train_ent))
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processed = 0
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self.update(article_text=article_text, entities=entities, golds=golds)
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while start < len(train_ent):
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next_batch = train_ent[start:stop]
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# dev eval
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self._test_dev(dev_inst, dev_pos, dev_neg, dev_texts, print_string="dev_inter_avg", avg=True)
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except ValueError as e:
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print("Error in article id", article_id)
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golds = [train_gold[e] for e in next_batch]
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descs = [train_desc[e] for e in next_batch]
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articles = [train_texts[train_article[e]] for e in next_batch]
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self.update(entities=next_batch, golds=golds, descs=descs, texts=articles)
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self._test_dev(dev_ent, dev_gold, dev_desc, dev_article, dev_texts, print_string="dev_inter", avg=True)
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processed += len(next_batch)
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start = start + self.BATCH_SIZE
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stop = min(stop + self.BATCH_SIZE, len(train_ent))
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if to_print:
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print()
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print("Trained on", instance_pos_count, "/", instance_neg_count, "instances pos/neg")
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print("Trained on", processed, "entities in total")
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def _test_dev(self, instances, pos, neg, texts_by_id, print_string, avg=False, calc_random=False):
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predictions = list()
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golds = list()
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def _test_dev(self, entities, gold_by_entity, desc_by_entity, article_by_entity, texts_by_id, print_string, avg=True, calc_random=False):
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golds = [gold_by_entity[e] for e in entities]
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for article_id, inst_cluster_set in instances.items():
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for inst_cluster in inst_cluster_set:
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pos_ex = pos.get(inst_cluster)
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neg_exs = neg.get(inst_cluster, [])
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if calc_random:
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predictions = self._predict_random(entities=entities)
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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 = self.nlp(texts_by_id[article])
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entities = [self.nlp(pos_ex)]
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golds.append(float(1.0))
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for neg_ex in neg_exs:
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entities.append(self.nlp(neg_ex))
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golds.append(float(0.0))
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if calc_random:
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preds = self._predict_random(entities=entities)
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else:
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preds = self._predict(article_doc=article_doc, entities=entities, avg=avg)
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predictions.extend(preds)
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else:
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desc_docs = self.nlp.pipe([desc_by_entity[e] for e in entities])
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article_docs = self.nlp.pipe([texts_by_id[article_by_entity[e]] for e in entities])
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predictions = self._predict(entities=entities, article_docs=article_docs, desc_docs=desc_docs, avg=avg)
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# TODO: combine with prior probability
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p, r, f = run_el.evaluate(predictions, golds, to_print=False)
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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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p, r, f, acc = run_el.evaluate(predictions, golds, to_print=False)
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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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print("p/r/F/acc/loss", print_string, round(p, 1), round(r, 1), round(f, 1), round(acc, 2), round(loss, 5))
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return loss, p, r, f
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def _predict(self, article_doc, entities, avg=False, apply_threshold=True):
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def _predict(self, entities, article_docs, desc_docs, avg=True, apply_threshold=True):
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if avg:
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with self.article_encoder.use_params(self.sgd_article.averages) \
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and self.entity_encoder.use_params(self.sgd_entity.averages):
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doc_encoding = self.article_encoder([article_doc])[0]
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entity_encodings = self.entity_encoder(entities)
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and self.desc_encoder.use_params(self.sgd_entity.averages):
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doc_encodings = self.article_encoder(article_docs)
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desc_encodings = self.desc_encoder(desc_docs)
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else:
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doc_encoding = self.article_encoder([article_doc])[0]
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entity_encodings = self.entity_encoder(entities)
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doc_encodings = self.article_encoder(article_docs)
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desc_encodings = self.desc_encoder(desc_docs)
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concat_encodings = [list(entity_encodings[i]) + list(doc_encoding) for i in range(len(entities))]
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concat_encodings = [list(desc_encodings[i]) + list(doc_encodings[i]) for i in range(len(entities))]
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np_array_list = np.asarray(concat_encodings)
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if avg:
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@ -189,16 +187,16 @@ class EL_Model:
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def _predict_random(self, entities, apply_threshold=True):
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if not apply_threshold:
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return [float(random.uniform(0,1)) for e in entities]
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return [float(random.uniform(0, 1)) for e in entities]
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else:
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return [float(1.0) if random.uniform(0,1) > self.CUTOFF else float(0.0) for e in entities]
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return [float(1.0) if random.uniform(0, 1) > self.CUTOFF else float(0.0) for e in entities]
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def _build_cnn(self, in_width, entity_width, article_width, hidden_1_width, hidden_2_width):
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def _build_cnn(self, in_width, desc_width, article_width, hidden_1_width, hidden_2_width):
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with Model.define_operators({">>": chain, "|": concatenate, "**": clone}):
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self.entity_encoder = self._encoder(in_width=in_width, hidden_with=hidden_1_width, end_width=entity_width)
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self.desc_encoder = self._encoder(in_width=in_width, hidden_with=hidden_1_width, end_width=desc_width)
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self.article_encoder = self._encoder(in_width=in_width, hidden_with=hidden_1_width, end_width=article_width)
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in_width = entity_width + article_width
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in_width = desc_width + article_width
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out_width = hidden_2_width
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self.model = Affine(out_width, in_width) \
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@ -229,80 +227,78 @@ class EL_Model:
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def _begin_training(self):
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self.sgd_article = create_default_optimizer(self.article_encoder.ops)
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self.sgd_entity = create_default_optimizer(self.entity_encoder.ops)
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self.sgd_entity = create_default_optimizer(self.desc_encoder.ops)
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self.sgd = create_default_optimizer(self.model.ops)
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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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gradient = d_scores.mean()
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loss = (d_scores ** 2).mean()
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return loss, d_scores
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return loss, gradient
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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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# entity_docs = list(self.nlp.pipe(entities))
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def update(self, entities, golds, descs, texts):
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golds = self.model.ops.asarray(golds)
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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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desc_docs = self.nlp.pipe(descs)
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article_docs = self.nlp.pipe(texts)
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entity_doc = self.nlp(entity)
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# print("entity_docs", type(entity_doc))
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doc_encodings, bp_doc = self.article_encoder.begin_update(article_docs, drop=self.DROP)
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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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desc_encodings, bp_entity = self.desc_encoder.begin_update(desc_docs, drop=self.DROP)
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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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concat_encodings = [list(desc_encodings[i]) + list(doc_encodings[i]) for i in range(len(entities))]
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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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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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# print("prediction", prediction)
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# golds = self.model.ops.asarray(golds)
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# print("gold", gold)
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# print("entities", entities)
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# print("predictions", predictions)
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# print("golds", golds)
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loss, gradient = self.get_loss(prediction, gold)
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loss, gradient = self.get_loss(predictions, golds)
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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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if self.PRINT_TRAIN:
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print("loss train", round(loss, 5))
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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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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(gradient, 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 = desc + doc, but doc is the same within this function (TODO: multiple docs/articles)
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doc_gradient = model_gradient[0][self.DESC_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.DESC_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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def _get_training_data(self, training_dir, entity_descr_output, dev, limit, to_print):
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id_to_descr = kb_creator._get_id_to_description(entity_descr_output)
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correct_entries, incorrect_entries = training_set_creator.read_training_entities(training_output=training_dir,
|
||||
collect_correct=True,
|
||||
collect_incorrect=True)
|
||||
|
||||
instance_by_article = dict()
|
||||
local_vectors = list() # TODO: local vectors
|
||||
text_by_article = dict()
|
||||
pos_entities = dict()
|
||||
neg_entities = dict()
|
||||
gold_by_entity = dict()
|
||||
desc_by_entity = dict()
|
||||
article_by_entity = dict()
|
||||
entities = list()
|
||||
|
||||
cnt = 0
|
||||
for f in listdir(training_dir):
|
||||
next_entity_nr = 0
|
||||
files = listdir(training_dir)
|
||||
shuffle(files)
|
||||
for f in files:
|
||||
if not limit or cnt < limit:
|
||||
if dev == run_el.is_dev(f):
|
||||
article_id = f.replace(".txt", "")
|
||||
|
|
@ -313,29 +309,29 @@ class EL_Model:
|
|||
with open(os.path.join(training_dir, f), mode="r", encoding='utf8') as file:
|
||||
text = file.read()
|
||||
text_by_article[article_id] = text
|
||||
instance_by_article[article_id] = set()
|
||||
|
||||
for mention, entity_pos in correct_entries[article_id].items():
|
||||
descr = id_to_descr.get(entity_pos)
|
||||
if descr:
|
||||
instance_by_article[article_id].add(article_id + "_" + mention)
|
||||
pos_entities[article_id + "_" + mention] = descr
|
||||
entities.append(next_entity_nr)
|
||||
gold_by_entity[next_entity_nr] = 1
|
||||
desc_by_entity[next_entity_nr] = descr
|
||||
article_by_entity[next_entity_nr] = article_id
|
||||
next_entity_nr += 1
|
||||
|
||||
for mention, entity_negs in incorrect_entries[article_id].items():
|
||||
if not balance or pos_entities.get(article_id + "_" + mention):
|
||||
neg_count = 0
|
||||
for entity_neg in entity_negs:
|
||||
# if balance, keep only 1 negative instance for each positive instance
|
||||
if neg_count < 1 or not balance:
|
||||
descr = id_to_descr.get(entity_neg)
|
||||
if descr:
|
||||
descr_list = neg_entities.get(article_id + "_" + mention, [])
|
||||
descr_list.append(descr)
|
||||
neg_entities[article_id + "_" + mention] = descr_list
|
||||
neg_count += 1
|
||||
for entity_neg in entity_negs:
|
||||
descr = id_to_descr.get(entity_neg)
|
||||
if descr:
|
||||
entities.append(next_entity_nr)
|
||||
gold_by_entity[next_entity_nr] = 0
|
||||
desc_by_entity[next_entity_nr] = descr
|
||||
article_by_entity[next_entity_nr] = article_id
|
||||
next_entity_nr += 1
|
||||
|
||||
if to_print:
|
||||
print()
|
||||
print("Processed", cnt, "training articles, dev=" + str(dev))
|
||||
print()
|
||||
return instance_by_article, pos_entities, neg_entities, text_by_article
|
||||
return entities, gold_by_entity, desc_by_entity, article_by_entity, text_by_article
|
||||
|
||||
|
|
|
|||
|
|
@ -111,7 +111,7 @@ if __name__ == "__main__":
|
|||
print("STEP 6: training", datetime.datetime.now())
|
||||
my_nlp = spacy.load('en_core_web_md')
|
||||
trainer = EL_Model(kb=my_kb, nlp=my_nlp)
|
||||
trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=100, devlimit=20)
|
||||
trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=400, devlimit=50)
|
||||
print()
|
||||
|
||||
# STEP 7: apply the EL algorithm on the dev dataset
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user