--

input.py

@@ -25,9 +25,9 @@ def preprocess_graph(adj):
 
 
 def load_data(dataset):
-    adj = np.loadtxt('../data/partitioned_data/{0}/orig/{0}_adj_orig.txt'.format(dataset), dtype=int)
+    adj = np.loadtxt('./data/partitioned_data/{0}/orig/{0}_adj_orig.txt'.format(dataset), dtype=int)
     adj = sp.csr_matrix(adj)
-    features = pickle.load(open("../data/partitioned_data/{0}/feature/{0}_feature.pkl".format(dataset), 'rb'))
+    features = pickle.load(open("./data/partitioned_data/{0}/feature/{0}_feature.pkl".format(dataset), 'rb'))
     y_test = 0
     tx = 0
     ty = 0

train.py

@@ -11,6 +11,7 @@ from evaluation import Evaluator
 from model import BGAN
 from optimizer import Optimizer, update
 
+
 # 解析参数
 def parse_args():
     parser = argparse.ArgumentParser(description='BGANDTI')
@@ -19,7 +20,7 @@ def parse_args():
     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=0.001, help='判别器学习率')
-    parser.add_argument('--epoch', type=int, default=20, help='迭代次数')
+    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).')
@@ -56,11 +57,9 @@ if __name__ == "__main__":
     }
 
     # 构造模型
-    # d_real, discriminator, ae_model, model_z2g, D_Graph, GD_real = DBGAN(placeholders, feas['num_features'], feas['num_nodes'], feas['features_nonzero'], settings)
     model = BGAN(placeholders, feas['num_features'], feas['num_nodes'], feas['features_nonzero'], settings)
 
     # 定义优化器
-    # opt = Optimizer(ae_model, model_z2g, D_Graph, discriminator, placeholders, feas['pos_weight'], feas['norm'], d_real, feas['num_nodes'], GD_real)
     optimizer = Optimizer(model.ae_model, model.model_z2g, model.D_Graph, model.discriminator, placeholders, feas['pos_weight'], feas['norm'], model.d_real, feas['num_nodes'], model.GD_real,
                           settings)
 
@@ -96,10 +95,8 @@ if __name__ == "__main__":
             record.append([roc_score, aupr_score, ap_score])
             record_emb.append(emb)
     rec = np.array(record)
-    # index = rec[:, 0].tolist().index(max(rec[:, 0].tolist()))
-    # index_pr = rec[:, 1].tolist().index(max(rec[:, 1].tolist()))
     emb = record_emb[rec[:, 0].tolist().index(max(rec[:, 0].tolist()))]
     ana = record[rec[:, 0].tolist().index(max(rec[:, 0].tolist()))]
-    ana_pr = record[rec[:, 1].tolist().index(max(rec[:, 1].tolist()))]
+    # ana_pr = record[rec[:, 1].tolist().index(max(rec[:, 1].tolist()))]
     print('The peak [auc] test_roc={:.7f}, aupr={:.7f}, ap={:.7f}'.format(ana[0], ana[1], ana[2]))
-    print('The peak [aupr] test_roc={:.7f}, aupr={:.7f}, ap={:.7f}'.format(ana_pr[0], ana_pr[1], ana_pr[2]))
+    # print('The peak [aupr] test_roc={:.7f}, aupr={:.7f}, ap={:.7f}'.format(ana_pr[0], ana_pr[1], ana_pr[2]))