-
Notifications
You must be signed in to change notification settings - Fork 6
/
eval_utils.py
778 lines (644 loc) · 24.9 KB
/
eval_utils.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
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
import dgl
import dgl.function as fn
import dgl.sparse as dglsp
import networkx as nx
import numpy as np
import os
import secrets
import subprocess as sp
import torch
from functools import partial
from pprint import pprint
from scipy import stats
from string import ascii_uppercase, digits
from model import BaseEvaluator, MLPTrainer, SGCTrainer, GCNTrainer,\
APPNPTrainer, GAETrainer, CNEvaluator
def get_triangle_count(nx_g):
triangle_count = sum(nx.triangles(nx.to_undirected(nx_g)).values()) / 3
return triangle_count
def linkx_homophily(graph, y):
r"""Homophily measure from `Large Scale Learning on Non-Homophilous Graphs:
New Benchmarks and Strong Simple Methods
<https://arxiv.org/abs/2110.14446>`__
Mathematically it is defined as follows:
.. math::
\frac{1}{C-1} \sum_{k=1}^{C} \max \left(0, \frac{\sum_{v\in C_k}|\{u\in
\mathcal{N}(v): y_v = y_u \}|}{\sum_{v\in C_k}|\mathcal{N}(v)|} -
\frac{|\mathcal{C}_k|}{|\mathcal{V}|} \right),
where :math:`C` is the number of node classes, :math:`C_k` is the set of
nodes that belong to class k, :math:`\mathcal{N}(v)` are the predecessors
of node :math:`v`, :math:`y_v` is the class of node :math:`v`, and
:math:`\mathcal{V}` is the set of nodes.
Parameters
----------
graph : DGLGraph
The graph.
y : torch.Tensor
The node labels, which is a tensor of shape (|V|).
Returns
-------
float
The homophily value.
"""
with graph.local_scope():
# Compute |{u\in N(v): y_v = y_u}| for each node v.
src, dst = graph.edges()
# Compute y_v = y_u for all edges.
graph.edata["same_class"] = (y[src] == y[dst]).float()
graph.update_all(
fn.copy_e("same_class", "m"), fn.sum("m", "same_class_deg")
)
deg = graph.in_degrees().float()
num_nodes = graph.num_nodes()
num_classes = y.max(dim=0).values.item() + 1
value = torch.tensor(0.0).to(graph.device)
for k in range(num_classes):
# Get the nodes that belong to class k.
class_mask = y == k
same_class_deg_k = graph.ndata["same_class_deg"][class_mask].sum()
deg_k = deg[class_mask].sum()
num_nodes_k = class_mask.sum()
value += max(0, same_class_deg_k / deg_k - num_nodes_k / num_nodes)
return value.item() / (num_classes - 1)
def edge_list_reindexed(G):
idx = 0
id2idx = dict()
for u in G.nodes():
id2idx[str(u)] = idx
idx += 1
edges = []
for (u, v) in G.edges():
edges.append((id2idx[str(u)], id2idx[str(v)]))
return edges
COUNT_START_STR = 'orbit counts:'
def orca(graph):
graph = graph.to_undirected()
tmp_fname = f'orca/tmp_{"".join(secrets.choice(ascii_uppercase + digits) for i in range(8))}.txt'
tmp_fname = os.path.join(os.path.dirname(os.path.realpath(__file__)), tmp_fname)
with open(tmp_fname, 'w') as f:
f.write(str(graph.number_of_nodes()) + ' ' + str(graph.number_of_edges()) + '\n')
for (u, v) in edge_list_reindexed(graph):
f.write(str(u) + ' ' + str(v) + '\n')
output = sp.check_output(
[str(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'orca/orca')), 'node', '4', tmp_fname, 'std'])
output = output.decode('utf8').strip()
idx = output.find(COUNT_START_STR) + len(COUNT_START_STR) + 2
output = output[idx:]
node_orbit_counts = np.array([
list(map(int, node_cnts.strip().split(' ')))
for node_cnts in output.strip('\n').split('\n')
])
try:
os.remove(tmp_fname)
except OSError:
pass
return node_orbit_counts
def get_orbit_dist(nx_g):
# (|V|, Q), where Q is the number of unique orbits
orbit_counts = orca(nx_g)
orbit_counts = np.sum(orbit_counts, axis=0) / nx_g.number_of_nodes()
orbit_counts = torch.from_numpy(orbit_counts)
orbit_dist = orbit_counts / max(orbit_counts.sum(), 1)
return orbit_dist
def get_adj(dgl_g):
# Get symmetrically normalized adjacency matrix.
A = dgl_g.adj()
N = dgl_g.num_nodes()
I = dglsp.identity((N, N), device=dgl_g.device)
A_hat = A + I
D_hat = dglsp.diag(A_hat.sum(1)) ** -0.5
A_norm = D_hat @ A_hat @ D_hat
return A_norm
def get_edge_split(A_dense):
# Exclude self-loops.
A_dense_upper = torch.triu(A_dense, diagonal=1)
real_edges = A_dense_upper.nonzero()
real_indices = torch.randperm(real_edges.size(0))
real_edges = real_edges[real_indices]
num_real = len(real_edges)
num_train = int(num_real * 0.8)
num_val = int(num_real * 0.1)
num_test = num_real - num_train - num_val
real_train, real_val, real_test = torch.split(
real_edges, [num_train, num_val, num_test])
neg_edges = torch.triu((A_dense == 0).float(), diagonal=1).nonzero()
neg_indices = torch.randperm(neg_edges.size(0))
neg_val = neg_edges[neg_indices[:num_val]]
neg_test = neg_edges[neg_indices[num_val:num_val+num_test]]
return real_train, real_val, real_test, neg_val, neg_test
def prepare_for_GAE(A):
A_dense = A.to_dense()
real_train, real_val, real_test, neg_val, neg_test = get_edge_split(A_dense)
num_nodes = A_dense.size(0)
train_mask = torch.zeros(num_nodes, num_nodes)
val_mask = torch.zeros(num_nodes, num_nodes)
test_mask = torch.zeros(num_nodes, num_nodes)
edge_train = real_train
edge_val = torch.cat([real_val, neg_val], dim=0)
edge_test = torch.cat([real_test, neg_test], dim=0)
row_train, col_train = edge_train.T
train_mask[row_train, col_train] = 1.
row_val, col_val = edge_val.T
val_mask[row_val, col_val] = 1.
row_test, col_test = edge_test.T
test_mask[row_test, col_test] = 1.
train_mask = train_mask.bool()
val_mask = val_mask.bool()
test_mask = test_mask.bool()
real_row_train, real_col_train = real_train.T
train_g = dgl.graph((real_row_train, real_col_train), num_nodes=num_nodes)
train_g = dgl.to_bidirected(train_g)
A_train = get_adj(train_g)
return A_train, train_mask, val_mask, test_mask
def emd(p, q):
return (
torch.cumsum(p, dim=0) - torch.cumsum(q, dim=0)
).abs().sum().item()
def get_pairwise_emd(real_dists, sample_dists):
emd_list = []
for p in real_dists:
for q in sample_dists:
emd_list.append(emd(p, q))
return float(np.mean(emd_list))
def get_deg_emd(real_degs, sample_degs):
"""Compute the earth mover distance (EMD) between
two degree distributions.
Parameters
----------
real_degs : list of torch.Tensor of shape (|V1|)
Node degrees of the real graphs.
sample_degs : list of torch.Tensor of shape (|V2|)
Node degrees of the sampled graphs.
Returns
-------
emd
The EMD value.
"""
max_deg = max(
max([deg.max().item() for deg in real_degs]),
max([deg.max().item() for deg in sample_degs])
)
def get_degree_dist(deg):
num_nodes = deg.size(0)
freq = torch.zeros(num_nodes, max_deg + 1)
freq[torch.arange(num_nodes), deg] = 1.
freq = freq.sum(dim=0)
return freq / (freq.sum() + 1e-6)
real_dists = []
for deg in real_degs:
real_dists.append(get_degree_dist(deg))
sample_dists = []
for deg in sample_degs:
sample_dists.append(get_degree_dist(deg))
return get_pairwise_emd(real_dists, sample_dists)
def get_cluster_emd(real_vals, sample_vals, bins=100):
"""Compute the earth mover distance (EMD) between
two clustering coefficient distributions.
Parameters
----------
real_vals : list of list of length (|V1|)
Node clustering coefficients of the real graphs.
sample_vals : list of list of length (|V2|)
Node clustering coefficients of the sampled graphs.
bins : int
Number of equal-width bins in the given range.
Returns
-------
emd
The EMD value.
"""
def get_cluster_dist(vals):
hist, _ = np.histogram(
vals, bins=bins, range=(0.0, 1.0), density=False)
hist = torch.from_numpy(hist)
return hist / (hist.sum() + 1e-6)
real_dists = []
for vals in real_vals:
real_dists.append(get_cluster_dist(vals))
sample_dists = []
for vals in sample_vals:
sample_dists.append(get_cluster_dist(vals))
return get_pairwise_emd(real_dists, sample_dists)
class Evaluator:
def __init__(self,
data_name,
dgl_g_real,
X_one_hot_3d_real,
Y_one_hot_real):
"""
Parameters
----------
data_name : str
Name of the dataset.
dgl_g_real : dgl.DGLGraph
Real graph.
X_one_hot_3d_real : torch.Tensor of shape (F, |V|, 2)
X_one_hot_3d_real[f, :, :] is the one-hot encoding of the f-th node
attribute in the real graph.
Y_one_hot_real : torch.Tensor of shape (|V|, C)
One-hot encoding of the node label in the real graph.
"""
self.data_name = data_name
# If the number of edges in a newly added graph exceeds this limit,
# a subgraph will be used for certain metric computations.
self.edge_limit = min(dgl_g_real.num_edges(), 20000)
# Split datasets without a built-in split.
add_mask = False
if data_name in ["amazon_photo", "amazon_computer"]:
add_mask = True
torch.manual_seed(0)
dgl_g_real, X_real, Y_real, data_dict_real = self.preprocess_g(
dgl_g_real,
X_one_hot_3d_real,
Y_one_hot_real,
add_mask)
self.data_dict_real = data_dict_real
self.data_dict_sample_list = []
num_classes = len(Y_real.unique())
os.makedirs(f"{data_name}_cpts", exist_ok=True)
self.mlp_evaluator = BaseEvaluator(MLPTrainer,
f"{data_name}_cpts/mlp.pth",
num_classes,
train_mask=dgl_g_real.ndata["train_mask"],
val_mask=dgl_g_real.ndata["val_mask"],
test_mask=dgl_g_real.ndata["test_mask"],
X=X_real,
Y=Y_real)
A_real = get_adj(dgl_g_real)
self.sgc_one_layer_evaluator = BaseEvaluator(
partial(SGCTrainer, num_gnn_layers=1),
f"{data_name}_cpts/sgc_one_layer.pth",
num_classes,
train_mask=dgl_g_real.ndata["train_mask"],
val_mask=dgl_g_real.ndata["val_mask"],
test_mask=dgl_g_real.ndata["test_mask"],
A=A_real,
X=X_real,
Y=Y_real)
self.sgc_two_layer_evaluator = BaseEvaluator(
partial(SGCTrainer, num_gnn_layers=2),
f"{data_name}_cpts/sgc_two_layer.pth",
num_classes,
train_mask=dgl_g_real.ndata["train_mask"],
val_mask=dgl_g_real.ndata["val_mask"],
test_mask=dgl_g_real.ndata["test_mask"],
A=A_real,
X=X_real,
Y=Y_real)
self.gcn_evaluator = BaseEvaluator(
partial(GCNTrainer, num_gnn_layers=2),
f"{data_name}_cpts/gcn.pth",
num_classes,
train_mask=dgl_g_real.ndata["train_mask"],
val_mask=dgl_g_real.ndata["val_mask"],
test_mask=dgl_g_real.ndata["test_mask"],
A=A_real,
X=X_real,
Y=Y_real)
self.appnp_one_layer_evaluator = BaseEvaluator(
partial(APPNPTrainer, num_gnn_layers=1),
f"{data_name}_cpts/appnp_one_layer.pth",
num_classes,
train_mask=dgl_g_real.ndata["train_mask"],
val_mask=dgl_g_real.ndata["val_mask"],
test_mask=dgl_g_real.ndata["test_mask"],
A=A_real,
X=X_real,
Y=Y_real)
self.appnp_two_layer_evaluator = BaseEvaluator(
partial(APPNPTrainer, num_gnn_layers=2),
f"{data_name}_cpts/appnp_two_layer.pth",
num_classes,
train_mask=dgl_g_real.ndata["train_mask"],
val_mask=dgl_g_real.ndata["val_mask"],
test_mask=dgl_g_real.ndata["test_mask"],
A=A_real,
X=X_real,
Y=Y_real)
# Generate train/val/test mask for link prediction.
# Fix the raw graph split for reproducibility.
torch.manual_seed(0)
A_real_train, train_mask, val_mask, test_mask = prepare_for_GAE(A_real)
self.gae_one_layer_evaluator = BaseEvaluator(
partial(GAETrainer, num_gnn_layers=1),
f"{data_name}_cpts/gae_one_layer.pth",
num_classes,
train_mask=train_mask,
val_mask=val_mask,
test_mask=test_mask,
A_train=A_real_train,
A_full=A_real,
X=X_real,
Y=Y_real)
self.gae_two_layer_evaluator = BaseEvaluator(
partial(GAETrainer, num_gnn_layers=2),
f"{data_name}_cpts/gae_two_layer.pth",
num_classes,
train_mask=train_mask,
val_mask=val_mask,
test_mask=test_mask,
A_train=A_real_train,
A_full=A_real,
X=X_real,
Y=Y_real)
self.cn_evaluator = CNEvaluator(
f"{data_name}_cpts/cn.pth",
A_train=A_real_train,
A_full=A_real,
val_mask=val_mask,
test_mask=test_mask
)
def add_mask_cora(self, dgl_g, Y_one_hot):
num_nodes = dgl_g.num_nodes()
train_mask = torch.zeros(num_nodes)
val_mask = torch.zeros(num_nodes)
test_mask = torch.zeros(num_nodes)
# Based on the raw graph
num_val_nodes = {
0: 61,
1: 36,
2: 78,
3: 158,
4: 81,
5: 57,
6: 29
}
num_test_nodes = {
0: 130,
1: 91,
2: 144,
3: 319,
4: 149,
5: 103,
6: 64
}
num_classes = Y_one_hot.size(-1)
for y in range(num_classes):
nodes_y = (Y_one_hot[:, y] == 1.).nonzero().squeeze(-1)
nid_y = torch.randperm(len(nodes_y))
nodes_y = nodes_y[nid_y]
train_mask[nodes_y[:20]] = 1.
start = 20
end = start + num_val_nodes[y]
val_mask[nodes_y[start: end]] = 1.
start = end
end = start + num_test_nodes[y]
test_mask[nodes_y[start: end]] = 1.
dgl_g.ndata["train_mask"] = train_mask.bool()
dgl_g.ndata["val_mask"] = val_mask.bool()
dgl_g.ndata["test_mask"] = test_mask.bool()
return dgl_g
def add_mask_benchmark(self, dgl_g, Y_one_hot):
num_nodes = dgl_g.num_nodes()
train_mask = torch.zeros(num_nodes)
val_mask = torch.zeros(num_nodes)
test_mask = torch.zeros(num_nodes)
num_classes = Y_one_hot.size(-1)
for y in range(num_classes):
nodes_y = (Y_one_hot[:, y] == 1.).nonzero().squeeze(-1)
nid_y = torch.randperm(len(nodes_y))
nodes_y = nodes_y[nid_y]
# Based on the raw paper.
train_mask[nodes_y[:20]] = 1.
val_mask[nodes_y[20: 50]] = 1.
test_mask[nodes_y[50:]] = 1.
dgl_g.ndata["train_mask"] = train_mask.bool()
dgl_g.ndata["val_mask"] = val_mask.bool()
dgl_g.ndata["test_mask"] = test_mask.bool()
return dgl_g
def add_mask(self, dgl_g, Y_one_hot):
if self.data_name == "cora":
return self.add_mask_cora(dgl_g, Y_one_hot)
elif self.data_name in ["amazon_photo", "amazon_computer"]:
return self.add_mask_benchmark(dgl_g, Y_one_hot)
else:
raise ValueError(f'Unexpected data name: {self.data_name}')
def sample_subg(self, dgl_g):
# Sample edge-induced subgraph for costly computation.
A = dgl_g.adj().to_dense()
A_upper = torch.triu(A, diagonal=1)
# (|E|, 2)
edges = A_upper.nonzero()
indices = torch.randperm(edges.size(0))[:self.edge_limit // 2]
src, dst = edges[indices].T
sub_g = dgl.graph((src, dst), num_nodes=dgl_g.num_nodes())
sub_g = dgl.to_bidirected(sub_g)
return sub_g
def k_order_g(self, dgl_g, k):
# Get DGLGraph of A^k.
A = dgl_g.adj().to_dense()
A_new = A
for _ in range(k-1):
A_new = A_new @ A
src, dst = A_new.nonzero().T
new_g = dgl.graph((src, dst), num_nodes=dgl_g.num_nodes())
return new_g
def preprocess_g(self,
dgl_g,
X_one_hot_3d,
Y_one_hot,
add_mask):
"""
Parameters
----------
dgl_g : dgl.DGLGraph
Graph.
X_one_hot_3d : torch.Tensor of shape (F, |V|, 2)
X_one_hot_3d[f, :, :] is the one-hot encoding of the f-th node
attribute in the graph.
Y_one_hot : torch.Tensor of shape (|V|, C)
One-hot encoding of the node label in the graph.
add_mask : bool
Whether to add a mask to the graph for node classification
data split.
Returns
-------
dgl_g : dgl.DGLGraph
Graph, potentially with node mask added.
X : torch.Tensor of shape (|V|, F)
Node attributes.
Y : torch.Tensor of shape (|V|)
Categorical node label.
data_dict : dict
Dictionary of graph statistics.
"""
if add_mask:
dgl_g = self.add_mask(dgl_g, Y_one_hot)
F = X_one_hot_3d.size(0)
# (|V|, F)
X = torch.zeros(X_one_hot_3d.size(1), F)
for f in range(F):
X[:, f] = X_one_hot_3d[f].argmax(dim=1)
if dgl_g.num_edges() > self.edge_limit:
dgl_subg = self.sample_subg(dgl_g)
else:
dgl_subg = dgl_g
nx_g = nx.DiGraph(dgl_subg.cpu().to_networkx())
triangle_count = get_triangle_count(nx_g)
Y = Y_one_hot.argmax(dim=-1)
linkx_A = linkx_homophily(dgl_g, Y)
dgl_g_pow_2 = self.k_order_g(dgl_g, 2)
linkx_A_pow_2 = linkx_homophily(dgl_g_pow_2, Y)
degs = dgl_g.in_degrees()
cluster_coefs = list(nx.clustering(nx_g).values())
orbit_dist = get_orbit_dist(nx_g)
data_dict = {
"triangle_count": triangle_count,
"linkx_A": linkx_A,
"linkx_A_pow_2": linkx_A_pow_2,
"degs": degs,
"cluster_coefs": cluster_coefs,
"orbit_dist": orbit_dist,
}
return dgl_g, X, Y, data_dict
def add_sample(self,
dgl_g,
X_one_hot_3d,
Y_one_hot):
"""Add a generated sample for evaluation.
Parameters
----------
dgl_g : dgl.DGLGraph
Generated graph.
X_one_hot_3d : torch.Tensor of shape (F, |V|, 2)
X_one_hot_3d[f, :, :] is the one-hot encoding of the f-th node
attribute in the generated graph.
Y_one_hot : torch.Tensor of shape (|V|, C)
One-hot encoding of the node label in the generated graph.
"""
dgl_g_sample, X_sample, Y_sample, data_dict_sample = self.preprocess_g(
dgl_g,
X_one_hot_3d,
Y_one_hot,
add_mask=True)
self.data_dict_sample_list.append(data_dict_sample)
self.mlp_evaluator.add_sample(
X=X_sample,
Y=Y_sample,
train_mask=dgl_g_sample.ndata["train_mask"],
val_mask=dgl_g_sample.ndata["val_mask"],
test_mask=dgl_g_sample.ndata["test_mask"])
A_sample = get_adj(dgl_g_sample)
self.sgc_one_layer_evaluator.add_sample(
A=A_sample,
X=X_sample,
Y=Y_sample,
train_mask=dgl_g_sample.ndata["train_mask"],
val_mask=dgl_g_sample.ndata["val_mask"],
test_mask=dgl_g_sample.ndata["test_mask"])
self.sgc_two_layer_evaluator.add_sample(
A=A_sample,
X=X_sample,
Y=Y_sample,
train_mask=dgl_g_sample.ndata["train_mask"],
val_mask=dgl_g_sample.ndata["val_mask"],
test_mask=dgl_g_sample.ndata["test_mask"])
self.gcn_evaluator.add_sample(
A=A_sample,
X=X_sample,
Y=Y_sample,
train_mask=dgl_g_sample.ndata["train_mask"],
val_mask=dgl_g_sample.ndata["val_mask"],
test_mask=dgl_g_sample.ndata["test_mask"])
self.appnp_one_layer_evaluator.add_sample(
A=A_sample,
X=X_sample,
Y=Y_sample,
train_mask=dgl_g_sample.ndata["train_mask"],
val_mask=dgl_g_sample.ndata["val_mask"],
test_mask=dgl_g_sample.ndata["test_mask"])
self.appnp_two_layer_evaluator.add_sample(
A=A_sample,
X=X_sample,
Y=Y_sample,
train_mask=dgl_g_sample.ndata["train_mask"],
val_mask=dgl_g_sample.ndata["val_mask"],
test_mask=dgl_g_sample.ndata["test_mask"])
# Generate train/val/test mask.
A_sample_train, train_mask, val_mask, test_mask = prepare_for_GAE(A_sample)
self.gae_one_layer_evaluator.add_sample(
A_train=A_sample_train,
A_full=A_sample,
X=X_sample,
Y=Y_sample,
train_mask=train_mask,
val_mask=val_mask,
test_mask=test_mask)
self.gae_two_layer_evaluator.add_sample(
A_train=A_sample_train,
A_full=A_sample,
X=X_sample,
Y=Y_sample,
train_mask=train_mask,
val_mask=val_mask,
test_mask=test_mask)
self.cn_evaluator.add_sample(
A_train=A_sample_train,
A_full=A_sample,
val_mask=val_mask,
test_mask=test_mask
)
def summary(self):
report = dict()
for key in ["triangle_count", "linkx_A", "linkx_A_pow_2"]:
avg_stats_sample = np.mean([
data_dict_sample[key] for data_dict_sample in self.data_dict_sample_list
])
report[key] = avg_stats_sample / self.data_dict_real[key]
report["deg_emd"] = get_deg_emd(
[self.data_dict_real["degs"]],
[data_dict_sample["degs"] for data_dict_sample in self.data_dict_sample_list])
# clustering coefficient EMD
report["cluster_emd"] = get_cluster_emd(
[self.data_dict_real["cluster_coefs"]],
[data_dict_sample["cluster_coefs"]
for data_dict_sample in self.data_dict_sample_list]
)
report["orbit_emd"] = get_pairwise_emd(
[self.data_dict_real["orbit_dist"]],
[data_dict_sample["orbit_dist"]
for data_dict_sample in self.data_dict_sample_list]
)
print('\n')
pprint(report)
print('\nMLP discriminator')
self.mlp_evaluator.summary()
print('\nSGC 1-layer discriminator')
self.sgc_one_layer_evaluator.summary()
print('\nSGC 2-layer discriminator')
self.sgc_two_layer_evaluator.summary()
print('\nGCN discriminator')
self.gcn_evaluator.summary()
print('\nAPPNP 1-layer discriminator')
self.appnp_one_layer_evaluator.summary()
print('\nAPPNP 2-layer discriminator')
self.appnp_two_layer_evaluator.summary()
print('\nGAE 1-layer discriminator')
self.gae_one_layer_evaluator.summary()
print('\nGAE 2-layer discriminator')
self.gae_two_layer_evaluator.summary()
print('\nCN discriminator')
self.cn_evaluator.summary()
real_acc_vector = [
self.mlp_evaluator.real_real_acc,
self.sgc_one_layer_evaluator.real_real_acc,
self.sgc_two_layer_evaluator.real_real_acc,
self.gcn_evaluator.real_real_acc,
self.appnp_one_layer_evaluator.real_real_acc,
self.appnp_two_layer_evaluator.real_real_acc
]
pearson_coeff = []
spearman_coeff = []
for i in range(len(self.data_dict_sample_list)):
sample_acc_vector = [
self.mlp_evaluator.sample_sample_acc[i],
self.sgc_one_layer_evaluator.sample_sample_acc[i],
self.sgc_two_layer_evaluator.sample_sample_acc[i],
self.gcn_evaluator.sample_sample_acc[i],
self.appnp_one_layer_evaluator.sample_sample_acc[i],
self.appnp_two_layer_evaluator.sample_sample_acc[i]
]
pearson_coeff.append(stats.pearsonr(real_acc_vector, sample_acc_vector).statistic)
spearman_coeff.append(stats.spearmanr(real_acc_vector, sample_acc_vector).statistic)
print(f'\nPearson correlation coefficient: {np.mean(pearson_coeff)}')
print(f'\nSpearman correlation coefficient: {np.mean(spearman_coeff)}')