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113 lines
5.1 KiB
113 lines
5.1 KiB
from __future__ import division |
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from __future__ import print_function |
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import os |
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# Train on CPU (hide GPU) due to memory constraints |
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os.environ['CUDA_VISIBLE_DEVICES'] = "0" |
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import tensorflow as tf |
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import settings |
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from constructor import get_placeholder, get_model, format_data, get_optimizer, update |
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from metrics import linkpred_metrics |
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from sklearn.neighbors import KernelDensity |
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from dppy.finite_dpps import FiniteDPP |
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from sklearn.decomposition import PCA |
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import numpy as np |
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import scipy.io as scio |
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# Settings |
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flags = tf.app.flags |
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FLAGS = flags.FLAGS |
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class Link_pred_Runner(): |
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def __init__(self, settings): |
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self.data_name = settings['data_name'] |
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self.iteration = settings['iterations'] |
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self.model = settings['model'] |
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def erun(self): |
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model_str = self.model |
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# formatted data |
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feas = format_data(self.data_name) |
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# Define placeholders |
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# 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 |
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placeholders = get_placeholder(feas['adj'], feas['num_features']) |
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# 定义由Dpp和密度估计出来的混合高斯 |
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DPP = FiniteDPP('correlation', **{'K': feas['adj'].toarray()}) |
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# DPP.sample_exact_k_dpp(size=4) |
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pca = PCA(n_components=FLAGS.hidden2) |
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# index = DPP.list_of_samples[0] |
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if self.data_name == 'cora': |
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DPP.sample_exact_k_dpp(size=21) # e 21 ic 6 gpcr 3 |
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index = DPP.list_of_samples[0] |
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pass |
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elif self.data_name == 'citeseer': |
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index = np.array([1782, 741, 3258, 3189, 3112, 2524, 2895, 1780, 1100, 2735, 1318, |
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2944, 1825, 18, 987, 2564, 463, 6, 3173, 701, 1901, 2349, |
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2786, 2412, 646, 2626, 2648, 1793, 432, 538, 1729, 1217, 1397, |
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1932, 2850, 458, 2129, 702, 2934, 2030, 2882, 1393, 308, 1271, |
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1106, 2688, 629, 1145, 3251, 1903, 1004, 1149, 1385, 285, 858, |
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2977, 844, 335, 532, 404, 3174, 528]) |
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elif self.data_name == 'pubmed': |
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index = np.array([842, 3338, 5712, 17511, 10801, 2714, 6970, 13296, 5466, |
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2230]) |
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feature_sample = feas['features_dense'] |
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feature_sample = pca.fit_transform(feature_sample) |
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featuresCompress = np.array([feature_sample[i] for i in index]) |
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# featuresCompress = np.array(feature_sample) |
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kde = KernelDensity(bandwidth=0.7).fit(featuresCompress) |
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# construct model |
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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']) |
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# Optimizer |
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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) |
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# Initialize session |
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# config = tf.ConfigProto() |
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# config.gpu_options.allow_growth = True |
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# sess = tf.Session(config = config) |
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sess = tf.Session() |
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sess.run(tf.global_variables_initializer()) |
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val_roc_score = [] |
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record = [] |
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record_emb = [] |
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# Train model |
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for epoch in range(self.iteration): |
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emb, avg_cost = update(ae_model, opt, sess, feas['adj_norm'], feas['adj_label'], feas['features'], placeholders, feas['adj'], kde, feas['features_dense']) |
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lm_train = linkpred_metrics(feas['val_edges'], feas['val_edges_false']) |
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roc_curr, ap_curr, _, aupr_score = lm_train.get_roc_score(emb, feas) |
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val_roc_score.append(roc_curr) |
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print("Epoch:", '%04d' % (epoch + 1), |
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"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=", |
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"{:.5f}".format(val_roc_score[-1]), "val_ap=", "{:.5f}".format(ap_curr), "val_aupr=", "{:.5f}".format(aupr_score)) |
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if (epoch + 1) % 10 == 0: |
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lm_test = linkpred_metrics(feas['test_edges'], feas['test_edges_false']) |
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roc_score, ap_score, _, aupr_score = lm_test.get_roc_score(emb, feas) |
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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 AUPR score: ' + str(aupr_score)) |
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# print('Test AP score: ' + str(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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rec = np.array(record) |
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index = rec[:, 0].tolist().index(max(rec[:, 0].tolist())) |
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index_pr = rec[:, 1].tolist().index(max(rec[:, 1].tolist())) |
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emb = record_emb[index] |
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ana = record[index] |
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ana_pr = record[index_pr] |
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# scio.savemat('result/{}_link_64_64_new.mat'.format(self.data_name), {'embedded': emb,'labels': feas['true_labels']}) |
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print('The peak [auc] test_roc=%f, aupr=%f, ap = %f' % (ana[0], ana[1], ana[2])) |
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print('The peak [aupr] test_roc=%f, aupr=%f, ap = %f' % (ana_pr[0], ana_pr[1], ana_pr[2]))
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