-
Notifications
You must be signed in to change notification settings - Fork 3
/
train_mgvae.py
424 lines (335 loc) · 16 KB
/
train_mgvae.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import Adam
from sklearn.metrics import roc_auc_score, average_precision_score
import scipy.sparse as sp
import numpy as np
import os
import time
from input_data import load_data
from preprocessing import *
import argparse
# KMeans Partition Tree
from tree import KMeans_Partitions_Tree, Spectral_Partitions_Tree, Multiresolution_Graph_Targets
def _parse_args():
parser = argparse.ArgumentParser(description = 'Citation Graphs')
parser.add_argument('--dir', '-dir', type = str, default = '.', help = 'Directory')
parser.add_argument('--name', '-n', type = str, default = 'NAME', help = 'Name')
parser.add_argument('--dataset', '-d', type = str, default = 'cora', help = 'cora / citeseer')
parser.add_argument('--num_epoch', '-ne', type = int, default = 256, help = 'Number of epochs')
parser.add_argument('--learning_rate', '-l', type = float, default = 0.01, help = 'Initial learning rate')
parser.add_argument('--kl_loss', '-k', type = int, default = 0, help = 'Use KL divergence loss or not')
parser.add_argument('--seed', '-s', type = int, default = 123456789, help = 'Random seed')
parser.add_argument('--pca', '-pca', type = int, default = 0, help = 'Use PCA for clustering or not')
parser.add_argument('--n_clusters', '-n_clusters', type = int, default = 4, help = 'Number of clusters')
parser.add_argument('--n_levels', '-n_levels', type = int, default = 2, help = 'Number of levels')
parser.add_argument('--partition', '-partition', type = str, default = 'kmeans', help = 'Clustering method: kmeans/spectral')
parser.add_argument('--Lambda', '-Lambda', type = int, default = 0.01, help = 'Weight for the multiresolution loss')
parser.add_argument('--device', '-device', type = str, default = 'cpu', help = 'cuda/cpu')
args = parser.parse_args()
return args
args = _parse_args()
log_name = args.dir + "/" + args.name + ".log"
model_name = args.dir + "/" + args.name + ".model"
LOG = open(log_name, "w")
# Fix CPU torch random seed
torch.manual_seed(args.seed)
# Fix GPU torch random seed
torch.cuda.manual_seed(args.seed)
# Fix the Numpy random seed
np.random.seed(args.seed)
# Train on CPU (hide GPU) due to memory constraints
# os.environ['CUDA_VISIBLE_DEVICES'] = ""
device = args.device
print(device)
adj, features = load_data(args.dataset)
# Store original adjacency matrix (without diagonal entries) for later
adj_orig = adj
adj_orig = adj_orig - sp.dia_matrix((adj_orig.diagonal()[np.newaxis, :], [0]), shape=adj_orig.shape)
adj_orig.eliminate_zeros()
adj_train, train_edges, val_edges, val_edges_false, test_edges, test_edges_false = mask_test_edges(adj)
adj = adj_train
# Some preprocessing
adj_norm = preprocess_graph(adj)
num_nodes = adj.shape[0]
features = sparse_to_tuple(features.tocoo())
num_features = features[2][1]
features_nonzero = features[1].shape[0]
# Create Model
pos_weight = float(adj.shape[0] * adj.shape[0] - adj.sum()) / adj.sum()
norm = adj.shape[0] * adj.shape[0] / float((adj.shape[0] * adj.shape[0] - adj.sum()) * 2)
adj_label = adj_train + sp.eye(adj_train.shape[0])
adj_label = sparse_to_tuple(adj_label)
adj_norm = torch.sparse.FloatTensor(torch.LongTensor(adj_norm[0].T),
torch.FloatTensor(adj_norm[1]),
torch.Size(adj_norm[2]))
adj_label = torch.sparse.FloatTensor(torch.LongTensor(adj_label[0].T),
torch.FloatTensor(adj_label[1]),
torch.Size(adj_label[2]))
features = torch.sparse.FloatTensor(torch.LongTensor(features[0].T),
torch.FloatTensor(features[1]),
torch.Size(features[2]))
weight_mask = adj_label.to_dense().view(-1) == 1
weight_tensor = torch.ones(weight_mask.size(0))
weight_tensor[weight_mask] = pos_weight
weight_tensor = weight_tensor.to(device = device)
# Principle Component Analysis
X = features.to_dense().detach().numpy()
if args.pca == 1:
mean = np.mean(X, axis = 0)
X = X - mean
cov = np.matmul(X.transpose(), X)
u, s, vh = np.linalg.svd(cov, full_matrices = True)
basis = u[:, :10]
X = np.matmul(X, basis)
# Partitions Tree
if args.partition == 'kmeans':
tree = KMeans_Partitions_Tree(features = X, n_clusters = args.n_clusters, n_levels = args.n_levels)
else:
if args.partition == 'spectral':
tree = Spectral_Partitions_Tree(adj = adj_norm.to_dense().detach().numpy(), n_clusters = args.n_clusters, n_levels = args.n_levels)
else:
print('ERROR: Could not find the partition method')
# Construct multiresolution targets
targets = Multiresolution_Graph_Targets(adj = adj_norm.to_dense().detach().numpy(), tree = tree, device = device)
input_adj = torch.FloatTensor(adj_norm.to_dense()).to(device = device)
features = features.to_dense().to(device = device)
nVertices = input_adj.size(0)
nFeatures = features.size(1)
input_dim = nFeatures
hidden_dim = 32
z_dim = 16
# Multiresolution Graph Variational Autoencoder
class MGVAE(nn.Module):
def __init__(self, adj, tree, targets, input_dim, hidden_dim, z_dim, device = 'cuda'):
super(MGVAE, self).__init__()
self.adj = adj
self.tree = tree
self.n_levels = tree.n_levels
self.targets = targets
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.z_dim = z_dim
self.device = device
self.base_encoder = GraphEncoder(self.input_dim, self.hidden_dim, self.z_dim, device = device).to(device = device)
self.local_encoder = nn.ModuleList()
self.local_pooler = nn.ModuleList()
for l in range(self.n_levels + 1):
self.local_encoder.append(GraphEncoder(self.z_dim, self.hidden_dim, self.z_dim, device = device).to(device = device))
self.local_pooler.append(GraphPooler(self.z_dim, self.hidden_dim, self.z_dim, device = device).to(device = device))
def forward(self, X):
outputs = []
# Base encoder
base_latent, base_mean, base_logstd = self.base_encoder(self.adj, X)
# Base dot-product decoder
base_predict = dot_product_decode(base_latent)
outputs.append([base_latent, base_mean, base_logstd, base_predict, self.adj])
l = self.n_levels
while l >= 0:
if l == self.n_levels:
# Local targets
count = 0
for i in self.tree.levels[l]:
# Local encoder
local_adj = self.targets.local_targets[l][count]
X = filter(self.tree.vertices[i], base_latent)
latent, mean, logstd = self.local_encoder[l](local_adj, X)
# Local dot-product decoder
predict = dot_product_decode(latent)
outputs.append([latent, mean, logstd, predict, local_adj])
count += 1
# Equivariant Pooling & Global target
count = 0
next_latent = []
next_mean = []
next_logstd = []
for i in self.tree.levels[l]:
local_adj = self.targets.local_targets[l][count]
X = filter(self.tree.vertices[i], base_latent)
latent, mean, logstd = self.local_pooler[l](local_adj, X)
next_latent.append(latent)
next_mean.append(mean)
next_logstd.append(logstd)
count += 1
else:
# Local targets
count = 0
for i in self.tree.levels[l]:
children_idx = []
child_idx = 0
for child in self.tree.children_nodes[i]:
children_idx.append(child_idx)
child_idx += 1
# Local encoder
local_adj = self.targets.local_targets[l][count]
X = filter(children_idx, prev_latent)
latent, mean, logstd = self.local_encoder[l](local_adj, X)
# Local dot-product decoder
predict = dot_product_decode(latent)
outputs.append([latent, mean, logstd, predict, local_adj])
count += 1
# Equivariant Pooling & Global target
count = 0
next_latent = []
next_mean = []
next_logstd = []
for i in self.tree.levels[l]:
local_adj = self.targets.local_targets[l][count]
X = filter(children_idx, prev_latent)
latent, mean, logstd = self.local_pooler[l](local_adj, X)
next_latent.append(latent)
next_mean.append(mean)
next_logstd.append(logstd)
count += 1
next_latent = torch.cat(next_latent, dim = 0)
next_mean = torch.cat(next_mean, dim = 0)
next_logstd = torch.cat(next_logstd, dim = 0)
# Global dot-product decoder
global_adj = self.targets.global_target[l]
predict = dot_product_decode(next_latent)
outputs.append([next_latent, next_mean, next_logstd, predict, global_adj])
prev_latent = next_latent
prev_mean = next_mean
prev_logstd = next_logstd
l -= 1
return outputs
class GraphEncoder(nn.Module):
def __init__(self, input_dim, hidden_dim, z_dim, device = 'cuda', **kwargs):
super(GraphEncoder, self).__init__(**kwargs)
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.z_dim = z_dim
self.device = device
self.base_net = GraphConvSparse(self.input_dim, self.hidden_dim)
self.mean_net = GraphConvSparse(self.hidden_dim, self.z_dim, activation=lambda x:x)
self.logstd_net = GraphConvSparse(self.hidden_dim, self.z_dim, activation=lambda x:x)
def forward(self, adj, X):
hidden = self.base_net(adj, X)
mean = self.mean_net(adj, hidden)
logstd = self.logstd_net(adj, hidden)
gaussian_noise = torch.randn(X.size(0), self.z_dim).to(device = self.device)
latent = gaussian_noise * torch.exp(logstd) + mean
return latent, mean, logstd
class GraphPooler(nn.Module):
def __init__(self, input_dim, hidden_dim, z_dim, device = 'cuda', **kwargs):
super(GraphPooler, self).__init__(**kwargs)
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.z_dim = z_dim
self.device = device
self.base_net = GraphConvSparse(self.input_dim, self.hidden_dim, activation = F.tanh)
self.mean_net = GraphConvSparse(self.hidden_dim, self.z_dim, activation = F.tanh)
self.logstd_net = GraphConvSparse(self.hidden_dim, self.z_dim, activation = F.tanh)
def forward(self, adj, X):
hidden = self.base_net(adj, X)
mean = self.mean_net(adj, hidden)
logstd = self.logstd_net(adj, hidden)
pooled_mean = torch.mean(mean, dim = 0).unsqueeze(dim = 0)
pooled_logstd = torch.mean(logstd, dim = 0).unsqueeze(dim = 0)
gaussian_noise = torch.randn(1, self.z_dim).to(device = self.device)
latent = gaussian_noise * torch.exp(pooled_logstd) + pooled_mean
return latent, pooled_mean, pooled_logstd
class GraphConvSparse(nn.Module):
def __init__(self, input_dim, output_dim, activation = F.relu, **kwargs):
super(GraphConvSparse, self).__init__(**kwargs)
self.weight = glorot_init(input_dim, output_dim)
self.activation = activation
def forward(self, adj, inputs):
x = inputs
x = torch.matmul(x, self.weight)
x = torch.matmul(adj, x)
outputs = self.activation(x)
return outputs
def filter(idx, matrix):
b = np.full(matrix.size(0), False)
b[idx] = True
return matrix[torch.ByteTensor(b)]
def dot_product_decode(Z):
A_pred = torch.sigmoid(torch.matmul(Z,Z.t()))
return A_pred
def glorot_init(input_dim, output_dim):
init_range = np.sqrt(6.0/(input_dim + output_dim))
initial = torch.rand(input_dim, output_dim)*2*init_range - init_range
return nn.Parameter(initial)
# init model and optimizer
model = MGVAE(input_adj, tree, targets, input_dim, hidden_dim, z_dim, device = device).to(device=device)
optimizer = Adam(model.parameters(), lr=args.learning_rate)
def get_scores(edges_pos, edges_neg, adj_rec):
def sigmoid(x):
return 1 / (1 + np.exp(-x))
# Predict on test set of edges
preds = []
pos = []
for e in edges_pos:
# print(e)
# print(adj_rec[e[0], e[1]])
preds.append(sigmoid(adj_rec[e[0], e[1]].item()))
pos.append(adj_orig[e[0], e[1]])
preds_neg = []
neg = []
for e in edges_neg:
preds_neg.append(sigmoid(adj_rec[e[0], e[1]].data))
neg.append(adj_orig[e[0], e[1]])
preds_all = np.hstack([preds, preds_neg])
labels_all = np.hstack([np.ones(len(preds)), np.zeros(len(preds_neg))])
roc_score = roc_auc_score(labels_all, preds_all)
ap_score = average_precision_score(labels_all, preds_all)
return roc_score, ap_score
def get_acc(adj_rec, adj_label):
labels_all = adj_label.to_dense().view(-1).long()
preds_all = (adj_rec > 0.5).view(-1).long()
accuracy = (preds_all == labels_all).sum().float() / labels_all.size(0)
return accuracy
def multiresolution_loss(outputs, norm, adj_label, weight_tensor, device, args):
for i in range(len(outputs)):
latent, mean, logstd, predict, target = outputs[i]
if i == 0:
A_pred = predict
loss = norm * F.binary_cross_entropy(A_pred.view(-1), adj_label.to_dense().view(-1).to(device = device), weight = weight_tensor)
else:
loss += norm * args.Lambda * F.mse_loss(predict.view(-1), target.view(-1).to(device = device))
if args.kl_loss == 1:
kl_divergence = 0.5 / predict.size(0) * (1 + 2 * logstd - mean ** 2 - torch.exp(logstd)).sum(1).mean()
loss -= kl_divergence
return loss, A_pred
# train model
best_val_roc = 0.0
for epoch in range(args.num_epoch):
t = time.time()
outputs = model(features)
optimizer.zero_grad()
loss, A_pred = multiresolution_loss(outputs, norm, adj_label, weight_tensor, device, args)
loss.backward()
optimizer.step()
train_acc = get_acc(A_pred, adj_label.to(device = device))
val_roc, val_ap = get_scores(val_edges, val_edges_false, A_pred.cpu())
print("Epoch:", '%04d' % (epoch + 1), "train_loss=", "{:.5f}".format(loss.item()),
"train_acc=", "{:.5f}".format(train_acc), "val_roc=", "{:.5f}".format(val_roc),
"val_ap=", "{:.5f}".format(val_ap),
"time=", "{:.5f}".format(time.time() - t))
LOG.write("Epoch = " + str(epoch + 1) + ". Train loss = " + str(loss.item()) + ". Train accuracy = " + str(train_acc) + ". Val ROC = " + str(val_roc)
+ ". Val AP = " + str(val_ap) + ". Time = " + str(time.time() - t) + "\n")
if val_roc > best_val_roc:
best_val_roc = val_roc
print("Best validation updated!")
LOG.write("Best validation updated!\n")
torch.save(model.state_dict(), model_name)
print("Save the best model to " + model_name)
LOG.write("Save the best model to " + model_name + "\n")
test_roc, test_ap = get_scores(test_edges, test_edges_false, A_pred.cpu())
print("test_roc=", "{:.5f}".format(test_roc), "test_ap=", "{:.5f}".format(test_ap))
# Load the best validated model
'''
model.eval()
with torch.no_grad():
model.load_state_dict(torch.load(model_name))
outputs = model(features)
loss, A_pred = multiresolution_loss(outputs, norm, adj_label, weight_tensor, device, args)
test_roc, test_ap = get_scores(test_edges, test_edges_false, A_pred.cpu())
'''
print("End of training!", "test_roc=", "{:.5f}".format(test_roc),
"test_ap=", "{:.5f}".format(test_ap))
LOG.write("Test ROC = " + str(test_roc) + "\n")
LOG.write("Test AP = " + str(test_ap) + "\n")
LOG.close()