From 475689562055c5c5adc7ffd08dc219284cc03016 Mon Sep 17 00:00:00 2001 From: lab-pc Date: Thu, 16 Mar 2023 16:53:41 +0800 Subject: [PATCH] -- --- train.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/train.py b/train.py index 2476e26..143f97a 100644 --- a/train.py +++ b/train.py @@ -19,13 +19,13 @@ def parse_args(): parser.add_argument('--hidden2', type=int, default=32, help='隐藏层2神经元数量.') parser.add_argument('--hidden3', type=int, default=64, help='隐藏层3神经元数量.') parser.add_argument('--learning_rate', type=float, default=.6 * 0.001, help='学习率') - parser.add_argument('--discriminator_learning_rate', type=float, default=0.0001, help='判别器学习率') # luo 判别器学习率0.0001， 其它数据集0.001 + parser.add_argument('--discriminator_learning_rate', type=float, default=0.001, help='判别器学习率') # luo 判别器学习率0.0001， 其它数据集0.001 parser.add_argument('--epoch', type=int, default=250, help='迭代次数') parser.add_argument('--seed', type=int, default=50, help='用来打乱数据集') parser.add_argument('--features', type=int, default=1, help='是(1)否(0)使用特征') parser.add_argument('--dropout', type=float, default=0., help='Dropout rate (1 - keep probability).') parser.add_argument('--weight_decay', type=float, default=0., help='Weight for L2 loss on embedding matrix.') - parser.add_argument('--dataset', type=str, default='luo', help='使用的数据集') + parser.add_argument('--dataset', type=str, default='nr', help='使用的数据集') args = parser.parse_args() return args @@ -40,7 +40,7 @@ if __name__ == "__main__": # DPP采样和PCA降维 DPP = FiniteDPP('correlation', **{'K': feas['adj'].toarray()}) pca = PCA(n_components=settings.hidden2) - DPP.sample_exact_k_dpp(size=20) # e 21 ic 6 gpcr 3 + DPP.sample_exact_k_dpp(size=2) # e 21 ic 6 gpcr 3 index = DPP.list_of_samples[0] feature_sample = feas['features_dense'] feature_sample = pca.fit_transform(feature_sample) @@ -94,6 +94,7 @@ if __name__ == "__main__": print('Test ROC score: ' + str(roc_score), 'Test AUPR score: ' + str(aupr_score), 'Test AP score: ' + str(ap_score)) record.append([roc_score, aupr_score, ap_score]) record_emb.append(emb) + np.savetxt('data/emb/{0}/emb_{1}.csv'.format(settings.dataset,epoch+1), emb, delimiter=',') rec = np.array(record) emb = record_emb[rec[:, 0].tolist().index(max(rec[:, 0].tolist()))] ana = record[rec[:, 0].tolist().index(max(rec[:, 0].tolist()))]