def erun(self):
model_str = self.model
# formatted data
feas = format_data(self.data_name)
# print('feas:{}'.format(feas))
# Define placeholders
placeholders = get_placeholder(feas['adj'])
# construct model
d_real, discriminator, ae_model = get_model(model_str,
placeholders,
feas['num_features'],
feas['num_nodes'],
feas['features_nonzero'],
cat=self.cat)
# Optimizer
opt = get_optimizer(model_str, ae_model, discriminator, placeholders,
feas['pos_weight'], feas['norm'], d_real,
feas['num_nodes'])
# Initialize session
sess = tf.Session()
sess.run(tf.global_variables_initializer())
val_roc_score = []
# Train model
for epoch in range(self.iteration):
emb, avg_cost = update(ae_model, opt, sess, feas['adj_norm'],
feas['adj_label'], feas['features'],
placeholders, feas['adj'])
lm_train = linkpred_metrics(feas['val_edges'],
feas['val_edges_false'])
roc_curr, ap_curr, _ = lm_train.get_roc_score(emb, feas)
val_roc_score.append(roc_curr)
print("Epoch:", '%04d' % (epoch + 1), "train_loss=",
"{:.5f}".format(avg_cost), "val_roc=",
"{:.5f}".format(val_roc_score[-1]), "val_ap=",
"{:.5f}".format(ap_curr))
fh = open('recoder_link_prediction.txt', 'a')
fh.write('Epoch=%04d, train_loss=%.5f, val_roc=%.5f, val_ap=%.5f' %
(epoch + 1, avg_cost, val_roc_score[-1], ap_curr))
fh.write('\r\n')
if (epoch + 1) % 10 == 0:
lm_test = linkpred_metrics(feas['test_edges'],
feas['test_edges_false'])
roc_score, ap_score, _ = lm_test.get_roc_score(emb, feas)
print('Test ROC score: ' + str(roc_score))
print('Test AP score: ' + str(ap_score))
fh.write('Test ROC score=%f, Test AP score=%f' %
(roc_score, ap_score))
fh.write('\r\n')
fh.flush()
fh.close()
def erun(self):
model_str = self.model
# formatted data
feas = format_data(self.data_name)
# Define placeholders
placeholders = get_placeholder(feas['adj'])
# construct model
d_real, discriminator, ae_model = get_model(model_str, placeholders,
feas['num_features'],
feas['num_nodes'],
feas['features_nonzero'])
# Optimizer
opt = get_optimizer(model_str, ae_model, discriminator, placeholders,
feas['pos_weight'], feas['norm'], d_real,
feas['num_nodes'])
# Initialize session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
val_roc_score = []
# Train model
for epoch in range(self.iteration):
emb, avg_cost = update(ae_model, opt, sess, feas['adj_norm'],
feas['adj_label'], feas['features'],
placeholders, feas['adj'])
lm_train = linkpred_metrics(feas['val_edges'],
feas['val_edges_false'])
roc_curr, ap_curr, _ = lm_train.get_roc_score(emb, feas)
val_roc_score.append(roc_curr)
print("Epoch:", '%04d' % (epoch + 1), "train_loss=",
"{:.5f}".format(avg_cost), "val_roc=",
"{:.5f}".format(val_roc_score[-1]), "val_ap=",
"{:.5f}".format(ap_curr))
if (epoch + 1) % 10 == 0:
lm_test = linkpred_metrics(feas['test_edges'],
feas['test_edges_false'])
roc_score, ap_score, _ = lm_test.get_roc_score(emb, feas)
print('Test ROC score: ' + str(roc_score))
print('Test AP score: ' + str(ap_score))
sess.close()
def erun(self):
model_str = self.model
# formatted data
feas = format_data(self.data_name)
# Define placeholders
placeholders = get_placeholder(feas['adj'])
# construct model
d_real, discriminator, ae_model = get_model(model_str, placeholders,
feas['num_features'],
feas['num_nodes'],
feas['features_nonzero'])
# Optimizer
opt = get_optimizer(model_str, ae_model, discriminator, placeholders,
feas['pos_weight'], feas['norm'], d_real,
feas['num_nodes'])
# Initialize session
sess = tf.Session()
sess.run(tf.global_variables_initializer())
val_roc_score = []
# Train model
max_acc = 0
for epoch in range(self.iteration):
emb, avg_cost = update(ae_model, opt, sess, feas['adj_norm'],
feas['adj_label'], feas['features'],
placeholders, feas['adj'])
lm_train = linkpred_metrics(feas['val_edges'],
feas['val_edges_false'])
roc_curr, ap_curr, _ = lm_train.get_roc_score(emb, feas)
val_roc_score.append(roc_curr)
print("Epoch:", '%04d' % (epoch + 1), "train_loss=",
"{:.5f}".format(avg_cost), "val_roc=",
"{:.5f}".format(val_roc_score[-1]), "val_ap=",
"{:.5f}".format(ap_curr))
# print(emb[feas['test_mask']].shape)
# print(feas['y_train'].shape)
# print(feas['y_test'].shape)
if (epoch + 1) % 1 == 0:
# train_mask = ~feas['test_mask']
lin_model = LogisticRegression().fit(
emb[feas['train_mask']], np.argmax(feas['y_train'],
axis=1))
# lm_test = nodepred_metrics(feas['test_mask'], lin_model.predict(emb['test_mask']))
ac_score = accuracy_score(
np.argmax(feas['y_test'], axis=1),
lin_model.predict(emb[feas['test_mask']]))
if ac_score > max_acc:
max_acc = ac_score
print('Accuracy: ' + str(ac_score))
print('Max Accuracy: ' + str(max_acc))
def erun(self):
model_str = self.model
# formatted data
feas = format_data(self.data_name)
# Define placeholders
# 定义placeholders,get_placeholder函数中只需要传入一个参数，即adj，函数中需要用到adj.shape
placeholders = get_placeholder(feas['adj'], feas['num_features'])
#定义由Dpp和密度估计出来的混合高斯
DPP = FiniteDPP('correlation', **{'K': feas['adj'].toarray()})
#DPP.sample_exact_k_dpp(size=4)
pca = PCA(n_components=FLAGS.hidden2)
#index = DPP.list_of_samples[0]
if self.data_name == 'cora':
DPP.sample_exact_k_dpp(size=21)
index = DPP.list_of_samples[0]
pass
elif self.data_name == 'citeseer':
index = np.array([
1782, 741, 3258, 3189, 3112, 2524, 2895, 1780, 1100, 2735,
1318, 2944, 1825, 18, 987, 2564, 463, 6, 3173, 701, 1901, 2349,
2786, 2412, 646, 2626, 2648, 1793, 432, 538, 1729, 1217, 1397,
1932, 2850, 458, 2129, 702, 2934, 2030, 2882, 1393, 308, 1271,
1106, 2688, 629, 1145, 3251, 1903, 1004, 1149, 1385, 285, 858,
2977, 844, 335, 532, 404, 3174, 528
])
elif self.data_name == 'pubmed':
index = np.array(
[842, 3338, 5712, 17511, 10801, 2714, 6970, 13296, 5466, 2230])
feature_sample = feas['features_dense']
feature_sample = pca.fit_transform(feature_sample)
featuresCompress = np.array([feature_sample[i] for i in index])
#featuresCompress = np.array(feature_sample)
kde = KernelDensity(bandwidth=0.7).fit(featuresCompress)
# construct model
d_real, discriminator, ae_model, model_z2g, D_Graph, GD_real = get_model(
model_str, placeholders, feas['num_features'], feas['num_nodes'],
feas['features_nonzero'])
# Optimizer
opt = get_optimizer(model_str, ae_model, model_z2g, D_Graph,
discriminator, placeholders, feas['pos_weight'],
feas['norm'], d_real, feas['num_nodes'], GD_real)
# Initialize session
#config = tf.ConfigProto()
#config.gpu_options.allow_growth = True
#sess = tf.Session(config = config)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
val_roc_score = []
record = []
record_emb = []
# Train model
for epoch in range(self.iteration):
emb, avg_cost = update(ae_model, opt, sess, feas['adj_norm'],
feas['adj_label'], feas['features'],
placeholders, feas['adj'], kde,
feas['features_dense'])
lm_train = linkpred_metrics(feas['val_edges'],
feas['val_edges_false'])
roc_curr, ap_curr, _ = lm_train.get_roc_score(emb, feas)
val_roc_score.append(roc_curr)
print(
"Epoch:", '%04d' % (epoch + 1),
"train_loss= {:.5f}, d_loss= {:.5f}, g_loss= {:.5f}, GD_loss= {:.5f}, GG_loss= {:.5f}"
.format(avg_cost[0], avg_cost[1], avg_cost[2], avg_cost[3],
avg_cost[4]), "val_roc=",
"{:.5f}".format(val_roc_score[-1]), "val_ap=",
"{:.5f}".format(ap_curr))
if (epoch + 1) % 10 == 0:
lm_test = linkpred_metrics(feas['test_edges'],
feas['test_edges_false'])
roc_score, ap_score, _ = lm_test.get_roc_score(emb, feas)
print('Test ROC score: ' + str(roc_score))
print('Test AP score: ' + str(ap_score))
record.append([roc_score, ap_score])
record_emb.append(emb)
rec = np.array(record)
index = rec[:, 0].tolist().index(max(rec[:, 0].tolist()))
emb = record_emb[index]
ana = record[index]
scio.savemat('result/{}_link_64_64_new.mat'.format(self.data_name), {
'embedded': emb,
'labels': feas['true_labels']
})
print('The peak val_roc=%f, ap = %f' % (ana[0], ana[1]))