def init_real_metric(self): from utils.metrics.DocEmbSim import DocEmbSim docsim = DocEmbSim(oracle_file=self.oracle_file, generator_file=self.generator_file, num_vocabulary=self.vocab_size) self.add_metric(docsim) inll = Nll(data_loader=self.gen_data_loader, rnn=self.generator, sess=self.sess) inll.set_name('nll-test') self.add_metric(inll) print("Metrics Applied: " + inll.get_name() + ", " + docsim.get_name())
def get_metrics(config, oracle_loader, test_file, gen_file, g_pretrain_loss, x_real, sess): # set up evaluation metric metrics = [] if config['nll_gen']: nll_gen = Nll(oracle_loader, g_pretrain_loss, x_real, sess, name='nll_gen') metrics.append(nll_gen) if config['doc_embsim']: doc_embsim = DocEmbSim(test_file, gen_file, config['vocab_size'], name='doc_embsim') metrics.append(doc_embsim) if config['bleu']: for i in range(2, 6): bleu = Bleu(test_text=gen_file, real_text=test_file, gram=i, name='bleu' + str(i)) metrics.append(bleu) if config['selfbleu']: for i in range(2, 6): selfbleu = SelfBleu(test_text=gen_file, gram=i, name='selfbleu' + str(i)) metrics.append(selfbleu) return metrics
def get_metrics(config, oracle_loader, test_file, gen_file, g_pretrain_loss, x_real, x_topic, sess, json_file): # set up evaluation metric metrics = [] if config['nll_gen']: nll_gen = NllTopic(oracle_loader, g_pretrain_loss, x_real, sess, name='nll_gen', x_topic=x_topic) metrics.append(nll_gen) if config['doc_embsim']: doc_embsim = DocEmbSim(test_file, gen_file, config['vocab_size'], name='doc_embsim') metrics.append(doc_embsim) if config['bleu']: for i in [2, 4]: # range(2, 6): bleu = Bleu(test_text=json_file, real_text=test_file, gram=i, name='bleu' + str(i)) metrics.append(bleu) if config['selfbleu']: for i in [4]: selfbleu = SelfBleu(test_text=json_file, gram=i, name='selfbleu' + str(i)) metrics.append(selfbleu) if config['KL']: KL_div = KL_divergence(oracle_loader, json_file, name='KL_divergence') metrics.append(KL_div) if config['earth_mover']: EM_div = EarthMover(oracle_loader, json_file, name='Earth_Mover_Distance') metrics.append(EM_div) return metrics
def get_metrics(config, oracle_loader, gen_loader, oracle_file, gen_file, oracle_model, g_pretrain_loss, x_real, sess): # set up evaluation metric metrics = [] if config['nll_oracle']: nll_oracle = Nll(gen_loader, oracle_model.pretrain_loss, oracle_model.x, sess, name='nll_oracle') metrics.append(nll_oracle) if config['nll_gen']: nll_gen = Nll(oracle_loader, g_pretrain_loss, x_real, sess, name='nll_gen') metrics.append(nll_gen) if config['doc_embsim']: doc_embsim = DocEmbSim(oracle_file, gen_file, config['vocab_size'], name='doc_embsim') metrics.append(doc_embsim) return metrics
def init_metric(self): nll = Nll(data_loader=self.oracle_data_loader, rnn=self.oracle, sess=self.sess) self.add_metric(nll) inll = Nll(data_loader=self.gen_data_loader, rnn=self.generator, sess=self.sess) inll.set_name('nll-test') self.add_metric(inll) from utils.metrics.DocEmbSim import DocEmbSim docsim = DocEmbSim(oracle_file=self.oracle_file, generator_file=self.generator_file, num_vocabulary=self.vocab_size) self.add_metric(docsim)
def init_real_metric(self): from utils.metrics.Nll import Nll from utils.metrics.PPL import PPL from utils.metrics.DocEmbSim import DocEmbSim from utils.others.Bleu import Bleu from utils.metrics.SelfBleu import SelfBleu if self.valid_ppl: valid_ppl = PPL(self.valid_data_loader, self.generator, self.sess) valid_ppl.set_name('valid_ppl') self.add_metric(valid_ppl) if self.nll_gen: nll_gen = Nll(self.gen_data_loader, self.generator, self.sess) nll_gen.set_name('nll_gen') self.add_metric(nll_gen) if self.doc_embsim: doc_embsim = DocEmbSim(self.oracle_file, self.generator_file, self.vocab_size) doc_embsim.set_name('doc_embsim') self.add_metric(doc_embsim) if self.bleu: FLAGS = tf.app.flags.FLAGS dataset = FLAGS.data if dataset == "image_coco": real_text = 'data/testdata/test_image_coco.txt' elif dataset == "emnlp_news": real_text = 'data/testdata/test_emnlp_news.txt' else: raise ValueError for i in range(3, 4): bleu = Bleu(test_text=self.text_file, real_text=real_text, gram=i) bleu.set_name(f"Bleu{i}") self.add_metric(bleu) if self.selfbleu: for i in range(2, 6): selfbleu = SelfBleu(test_text=self.text_file, gram=i) selfbleu.set_name(f"Selfbleu{i}") self.add_metric(selfbleu)
def init_real_metric(self): from utils.metrics.DocEmbSim import DocEmbSim docsim = DocEmbSim(oracle_file=self.oracle_file, generator_file=self.generator_file, num_vocabulary=self.vocab_size) self.add_metric(docsim) inll = Nll(data_loader=self.gen_data_loader, rnn=self.generator, sess=self.sess) inll.set_name('nll-test') self.add_metric(inll) bleu = Bleu(test_text=self.test_file, real_text='data/image_coco.txt', gram=2) self.add_metric(bleu) sbleu = SelfBleu(test_text=self.test_file, gram=2) self.add_metric(sbleu)