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@ -19,13 +19,13 @@ def parse_args(): |
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parser.add_argument('--hidden2', type=int, default=32, help='隐藏层2神经元数量.') |
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parser.add_argument('--hidden2', type=int, default=32, help='隐藏层2神经元数量.') |
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parser.add_argument('--hidden3', type=int, default=64, help='隐藏层3神经元数量.') |
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parser.add_argument('--hidden3', type=int, default=64, help='隐藏层3神经元数量.') |
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parser.add_argument('--learning_rate', type=float, default=.6 * 0.001, help='学习率') |
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parser.add_argument('--learning_rate', type=float, default=.6 * 0.001, help='学习率') |
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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 |
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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 |
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parser.add_argument('--epoch', type=int, default=250, help='迭代次数') |
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parser.add_argument('--epoch', type=int, default=250, help='迭代次数') |
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parser.add_argument('--seed', type=int, default=50, help='用来打乱数据集') |
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parser.add_argument('--seed', type=int, default=50, help='用来打乱数据集') |
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parser.add_argument('--features', type=int, default=1, help='是(1)否(0)使用特征') |
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parser.add_argument('--features', type=int, default=1, help='是(1)否(0)使用特征') |
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parser.add_argument('--dropout', type=float, default=0., help='Dropout rate (1 - keep probability).') |
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parser.add_argument('--dropout', type=float, default=0., help='Dropout rate (1 - keep probability).') |
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parser.add_argument('--weight_decay', type=float, default=0., help='Weight for L2 loss on embedding matrix.') |
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parser.add_argument('--weight_decay', type=float, default=0., help='Weight for L2 loss on embedding matrix.') |
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parser.add_argument('--dataset', type=str, default='luo', help='使用的数据集') |
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parser.add_argument('--dataset', type=str, default='nr', help='使用的数据集') |
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args = parser.parse_args() |
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args = parser.parse_args() |
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return args |
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return args |
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@ -40,7 +40,7 @@ if __name__ == "__main__": |
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# DPP采样和PCA降维 |
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# DPP采样和PCA降维 |
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DPP = FiniteDPP('correlation', **{'K': feas['adj'].toarray()}) |
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DPP = FiniteDPP('correlation', **{'K': feas['adj'].toarray()}) |
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pca = PCA(n_components=settings.hidden2) |
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pca = PCA(n_components=settings.hidden2) |
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DPP.sample_exact_k_dpp(size=20) # e 21 ic 6 gpcr 3 |
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DPP.sample_exact_k_dpp(size=2) # e 21 ic 6 gpcr 3 |
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index = DPP.list_of_samples[0] |
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index = DPP.list_of_samples[0] |
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feature_sample = feas['features_dense'] |
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feature_sample = feas['features_dense'] |
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feature_sample = pca.fit_transform(feature_sample) |
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feature_sample = pca.fit_transform(feature_sample) |
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@ -94,6 +94,7 @@ if __name__ == "__main__": |
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print('Test ROC score: ' + str(roc_score), 'Test AUPR score: ' + str(aupr_score), 'Test AP score: ' + str(ap_score)) |
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print('Test ROC score: ' + str(roc_score), 'Test AUPR score: ' + str(aupr_score), 'Test AP score: ' + str(ap_score)) |
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record.append([roc_score, aupr_score, ap_score]) |
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record.append([roc_score, aupr_score, ap_score]) |
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record_emb.append(emb) |
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record_emb.append(emb) |
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np.savetxt('data/emb/{0}/emb_{1}.csv'.format(settings.dataset,epoch+1), emb, delimiter=',') |
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rec = np.array(record) |
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rec = np.array(record) |
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emb = record_emb[rec[:, 0].tolist().index(max(rec[:, 0].tolist()))] |
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emb = record_emb[rec[:, 0].tolist().index(max(rec[:, 0].tolist()))] |
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ana = record[rec[:, 0].tolist().index(max(rec[:, 0].tolist()))] |
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ana = record[rec[:, 0].tolist().index(max(rec[:, 0].tolist()))] |
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