├── HGP-SL
    ├── pre_trained
    ├── fig
    │   └── model.png
    ├── __pycache__
    │   ├── layers.cpython-36.pyc
    │   ├── layers.cpython-37.pyc
    │   ├── models.cpython-36.pyc
    │   ├── models.cpython-37.pyc
    │   ├── sparse_softmax.cpython-36.pyc
    │   └── sparse_softmax.cpython-37.pyc
    ├── data
    │   ├── ADNI2
    │   │   └── raw
    │   │   │   └── ADNI2_graph_labels.txt
    │   ├── ADNI
    │   │   └── raw
    │   │   │   ├── ADNI_graph_labels.txt
    │   │   │   └── ADNI_node_labels.txt
    │   └── ABIDE
    │   │   └── raw
    │   │       └── ABIDE_graph_labels.txt
    ├── README.md
    ├── models.py
    ├── sparse_softmax.py
    ├── mainTL.py
    ├── main.py
    └── layers.py
├── README.md
├── ADNI_with_Abide_TL.ipynb
├── Node2vec.ipynb
├── Hierarchical GCN.ipynb
└── Baseline_GCN.ipynb


/HGP-SL/pre_trained:
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/HGP-SL/data/ADNI2/raw/ADNI2_graph_labels.txt:
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106 | 1
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110 | 1
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117 | 2
118 | 


--------------------------------------------------------------------------------
/HGP-SL/data/ADNI/raw/ADNI_graph_labels.txt:
--------------------------------------------------------------------------------
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105 | 1
106 | 1
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118 | 2
119 | 1
120 | 2
121 | 2
122 | 


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/README.md:
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 1 | # Graph Convolutional Neural Networks for Alzheimer’s Classification with transfer learning and HPC methods
 2 | 
 3 | GCNs have been increasingly used for the classification of brain functional networks to aid early prediction of neurodegenerative diseases. It is important to analyze the performance and capabilities of these GCNs for the classification and obtain insights on how and when the GCNs can be used. In this work, we perform detailed analyses of the performance of GCNs for the classification of brain functional networks of Alzheimer's data. We study the impact of the various parameters including the thresholds used for graph generation, graph sizes, data sizes or the number of subjects, and classification accuracy across different visits of the subjects. We have also developed a transfer learning approach to train using one dataset and apply the weights on another dataset to make use of larger data sizes. Finally, we have developed GPU-based acceleration methods to decrease the training time. We find that the accuracy of the models improves when taking into account all visits of the subjects and also improves with increasing graph sizes. The use of transfer learning has also improved classification accuracy. Finally, the use of CUDA streams for asynchronous computations has resulted in a reduction in execution times by up to 60\%.
 4 | 
 5 | ### Requirements
 6 | 
 7 | * python
 8 | * pytorch
 9 | * torch-scatter
10 | * torch-sparse
11 | * torch-cluster
12 | * torch-geometric
13 | * stellargraph
14 | 
15 | Note:
16 | This code repository is heavily built on [pytorch_geometric](https://github.com/rusty1s/pytorch_geometric), which is a Geometric Deep Learning Extension Library for PyTorch. Please refer [here](https://pytorch-geometric.readthedocs.io/en/latest/) for how to install and utilize the library.
17 | 
18 | ### Dataset
19 | 
20 | * ADNI dataset is publicly avaiable at http://adni.loni.usc.edu/
21 | * ABIDE dataset is publicly available at http://preprocessed-connectomes-project.org/abide/
22 | 


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/HGP-SL/README.md:
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 1 | # HGP-SL
 2 | Hierarchical Graph Pooling with Structure Learning ([arXiv](https://arxiv.org/abs/1911.05954)).
 3 | 
 4 | This is a PyTorch implementation of the HGP-SL algorithm, which learns a low-dimensional representation for the entire graph. Specifically, the graph pooling operation utilizes node features and graph structure information to perform down-sampling on graphs. Then, a structure learning layer is stacked on the pooling operation, which aims to learn a refined graph structure that can best preserve the essential topological information.
 5 | 
 6 | 
 7 | ### Data format
 8 | 
 9 | This folder contains the following comma separated text files (replace DS by the name of the dataset):
10 | 
11 | **n = total number of nodes**
12 | 
13 | **m = total number of edges**
14 | 
15 | **N = number of graphs**
16 | 
17 | **(1) DS_A.txt (m lines)** 
18 | 
19 | *sparse (block diagonal) adjacency matrix for all graphs, each line corresponds to (row, col) resp. (node_id, node_id)*
20 | 
21 | **(2) DS_graph_indicator.txt (n lines)**
22 | 
23 | *column vector of graph identifiers for all nodes of all graphs, the value in the i-th line is the graph_id of the node with node_id i*
24 | 
25 | **(3) DS_graph_labels.txt (N lines)** 
26 | 
27 | *class labels for all graphs in the dataset, the value in the i-th line is the class label of the graph with graph_id i*
28 | 
29 | **(4) DS_node_labels.txt (n lines)**
30 | 
31 | *column vector of node labels, the value in the i-th line corresponds to the node with node_id i*
32 | 
33 | There are OPTIONAL files if the respective information is available:
34 | 
35 | **(5) DS_edge_labels.txt (m lines; same size as DS_A_sparse.txt)**
36 | 
37 | *labels for the edges in DS_A_sparse.txt* 
38 | 
39 | **(6) DS_edge_attributes.txt (m lines; same size as DS_A.txt)**
40 | 
41 | *attributes for the edges in DS_A.txt* 
42 | 
43 | **(7) DS_node_attributes.txt (n lines)** 
44 | 
45 | *matrix of node attributes, the comma seperated values in the i-th line is the attribute vector of the node with node_id i*
46 | 
47 | **(8) DS_graph_attributes.txt (N lines)** 
48 | 
49 | *regression values for all graphs in the dataset, the value in the i-th line is the attribute of the graph with graph_id i*
50 | 


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/HGP-SL/models.py:
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 1 | import torch
 2 | import torch.nn.functional as F
 3 | from torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmp
 4 | from torch_geometric.nn import GCNConv
 5 | 
 6 | from layers import GCN, HGPSLPool
 7 | 
 8 | 
 9 | class Model(torch.nn.Module):
10 |     def __init__(self, args):
11 |         super(Model, self).__init__()
12 |         self.args = args
13 |         self.num_features = args.num_features
14 |         self.nhid = args.nhid
15 |         self.num_classes = args.num_classes
16 |         self.pooling_ratio = args.pooling_ratio
17 |         self.dropout_ratio = args.dropout_ratio
18 |         self.sample = args.sample_neighbor
19 |         self.sparse = args.sparse_attention
20 |         self.sl = args.structure_learning
21 |         self.lamb = args.lamb
22 | 
23 |         self.conv1 = GCNConv(self.num_features, self.nhid)
24 |         self.conv2 = GCN(self.nhid, self.nhid)
25 |         self.conv3 = GCN(self.nhid, self.nhid)
26 | 
27 |         self.pool1 = HGPSLPool(self.nhid, self.pooling_ratio, self.sample, self.sparse, self.sl, self.lamb)
28 |         self.pool2 = HGPSLPool(self.nhid, self.pooling_ratio, self.sample, self.sparse, self.sl, self.lamb)
29 | 
30 |         self.lin1 = torch.nn.Linear(self.nhid * 2, self.nhid)
31 |         self.lin2 = torch.nn.Linear(self.nhid, self.nhid // 2)
32 |         self.lin3 = torch.nn.Linear(self.nhid // 2, self.num_classes)
33 | 
34 |     def forward(self, data):
35 |         x, edge_index, batch = data.x, data.edge_index, data.batch
36 |         edge_attr = None
37 | 
38 |         x = F.relu(self.conv1(x, edge_index, edge_attr))
39 |         x, edge_index, edge_attr, batch = self.pool1(x, edge_index, edge_attr, batch)
40 |         x1 = torch.cat([gmp(x, batch), gap(x, batch)], dim=1)
41 | 
42 |         x = F.relu(self.conv2(x, edge_index, edge_attr))
43 |         x, edge_index, edge_attr, batch = self.pool2(x, edge_index, edge_attr, batch)
44 |         x2 = torch.cat([gmp(x, batch), gap(x, batch)], dim=1)
45 | 
46 |         x = F.relu(self.conv3(x, edge_index, edge_attr))
47 |         x3 = torch.cat([gmp(x, batch), gap(x, batch)], dim=1)
48 | 
49 |         x = F.relu(x1) + F.relu(x2) + F.relu(x3)
50 | 
51 |         x = F.relu(self.lin1(x))
52 |         x = F.dropout(x, p=self.dropout_ratio, training=self.training)
53 |         x = F.relu(self.lin2(x))
54 |         x = F.dropout(x, p=self.dropout_ratio, training=self.training)
55 |         x = F.log_softmax(self.lin3(x), dim=-1)
56 | 
57 |         return x
58 | 


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/HGP-SL/data/ABIDE/raw/ABIDE_graph_labels.txt:
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  1 | 1
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106 | 1
107 | 1
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110 | 2
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114 | 1
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116 | 1
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153 | 1
154 | 1
155 | 2
156 | 1
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158 | 1
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162 | 1
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180 | 1
181 | 1
182 | 2
183 | 1
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185 | 2
186 | 2
187 | 1
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189 | 2
190 | 2
191 | 1
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194 | 2
195 | 2
196 | 1
197 | 2
198 | 2
199 | 1
200 | 1
201 | 1
202 | 1
203 | 2
204 | 1
205 | 1
206 | 2
207 | 2
208 | 1
209 | 2
210 | 1
211 | 2
212 | 2
213 | 2
214 | 2
215 | 2
216 | 2
217 | 1
218 | 1
219 | 2
220 | 1
221 | 1
222 | 1
223 | 1
224 | 1
225 | 1
226 | 1
227 | 2
228 | 1
229 | 2
230 | 2
231 | 1
232 | 2
233 | 1
234 | 1
235 | 1
236 | 2
237 | 2
238 | 1
239 | 2
240 | 1
241 | 1
242 | 1
243 | 1
244 | 2
245 | 1
246 | 2
247 | 1
248 | 2
249 | 1
250 | 1
251 | 1
252 | 1
253 | 2
254 | 1
255 | 1
256 | 1
257 | 1
258 | 2
259 | 2
260 | 2
261 | 2
262 | 1
263 | 1
264 | 1
265 | 2
266 | 2
267 | 1
268 | 2
269 | 1
270 | 2
271 | 1
272 | 1
273 | 1
274 | 1
275 | 1
276 | 1
277 | 1
278 | 1
279 | 1
280 | 2
281 | 2
282 | 1
283 | 1
284 | 1
285 | 2
286 | 1
287 | 1
288 | 1
289 | 1
290 | 2
291 | 1
292 | 2
293 | 1
294 | 1
295 | 2
296 | 2
297 | 2
298 | 1
299 | 2
300 | 1
301 | 1
302 | 1
303 | 1
304 | 1
305 | 1
306 | 2
307 | 1
308 | 2
309 | 1
310 | 2
311 | 2
312 | 2
313 | 2
314 | 1
315 | 2
316 | 1
317 | 1
318 | 2
319 | 1
320 | 1
321 | 2
322 | 1
323 | 2
324 | 2
325 | 2
326 | 2
327 | 2
328 | 2
329 | 1
330 | 2
331 | 1
332 | 2
333 | 2
334 | 1
335 | 2
336 | 1
337 | 1
338 | 2
339 | 2
340 | 2
341 | 2
342 | 1
343 | 1
344 | 1
345 | 2
346 | 1
347 | 1
348 | 2
349 | 1
350 | 2
351 | 1
352 | 2
353 | 2
354 | 1
355 | 1
356 | 1
357 | 1
358 | 2
359 | 2
360 | 2
361 | 1
362 | 2
363 | 1
364 | 2
365 | 2
366 | 1
367 | 1
368 | 2
369 | 2
370 | 1
371 | 2
372 | 1
373 | 1
374 | 1
375 | 2
376 | 2
377 | 2
378 | 1
379 | 1
380 | 1
381 | 2
382 | 1
383 | 2
384 | 1
385 | 1
386 | 2
387 | 1
388 | 2
389 | 1
390 | 2
391 | 1
392 | 2
393 | 2
394 | 2
395 | 1
396 | 1
397 | 2
398 | 2
399 | 2
400 | 1
401 | 1
402 | 1
403 | 2
404 | 2
405 | 2
406 | 2
407 | 1
408 | 1
409 | 1
410 | 2
411 | 1
412 | 2
413 | 1
414 | 1
415 | 2
416 | 1
417 | 2
418 | 1
419 | 1
420 | 1
421 | 2
422 | 2
423 | 2
424 | 2
425 | 2
426 | 2
427 | 2
428 | 2
429 | 1
430 | 1
431 | 2
432 | 2
433 | 2
434 | 2
435 | 2
436 | 1
437 | 1
438 | 1
439 | 1
440 | 1
441 | 2
442 | 2
443 | 1
444 | 1
445 | 2
446 | 1
447 | 2
448 | 1
449 | 1
450 | 1
451 | 1
452 | 2
453 | 2
454 | 1
455 | 1
456 | 2
457 | 1
458 | 1
459 | 2
460 | 1
461 | 1
462 | 2
463 | 2
464 | 1
465 | 2
466 | 2
467 | 2
468 | 1
469 | 1
470 | 2
471 | 2
472 | 2
473 | 1
474 | 2
475 | 2
476 | 1
477 | 2
478 | 1
479 | 2
480 | 2
481 | 1
482 | 2
483 | 1
484 | 1
485 | 2
486 | 1
487 | 1
488 | 1
489 | 2
490 | 1
491 | 1
492 | 1
493 | 2
494 | 1
495 | 1
496 | 1
497 | 2
498 | 2
499 | 1
500 | 2
501 | 2
502 | 2
503 | 2
504 | 1
505 | 1
506 | 2
507 | 1
508 | 2
509 | 1
510 | 2
511 | 1
512 | 1
513 | 1
514 | 2
515 | 1
516 | 2
517 | 1
518 | 2
519 | 2
520 | 2
521 | 1
522 | 1
523 | 2
524 | 1
525 | 2
526 | 1
527 | 2
528 | 2
529 | 1
530 | 1
531 | 1
532 | 1
533 | 1
534 | 2
535 | 2
536 | 1
537 | 2
538 | 1
539 | 1
540 | 2
541 | 1
542 | 1
543 | 1
544 | 2
545 | 2
546 | 1
547 | 1
548 | 1
549 | 2
550 | 2
551 | 1
552 | 1
553 | 1
554 | 1
555 | 1
556 | 2
557 | 2
558 | 2
559 | 2
560 | 1
561 | 2
562 | 1
563 | 1
564 | 1
565 | 2
566 | 1
567 | 2
568 | 1
569 | 1
570 | 2
571 | 2
572 | 2
573 | 1
574 | 2
575 | 2
576 | 1
577 | 1
578 | 2
579 | 1
580 | 2
581 | 1
582 | 1
583 | 2
584 | 2
585 | 2
586 | 1
587 | 1
588 | 2
589 | 2
590 | 2
591 | 2
592 | 1
593 | 1
594 | 2
595 | 2
596 | 1
597 | 2
598 | 2
599 | 1
600 | 1
601 | 2
602 | 1
603 | 2
604 | 2
605 | 1
606 | 


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/HGP-SL/sparse_softmax.py:
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  1 | """
  2 | An original implementation of sparsemax (Martins & Astudillo, 2016) is available at
  3 | https://github.com/OpenNMT/OpenNMT-py/blob/master/onmt/modules/sparse_activations.py.
  4 | See `From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification, ICML 2016`
  5 | for detailed description.
  6 | 
  7 | We make some modifications to make it work at scatter operation scenarios, e.g., calculate softmax according to batch
  8 | indicators.
  9 | 
 10 | Usage:
 11 | >> x = torch.tensor([ 1.7301,  0.6792, -1.0565,  1.6614, -0.3196, -0.7790, -0.3877, -0.4943,
 12 |          0.1831, -0.0061])
 13 | >> batch = torch.tensor([0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
 14 | >> sparse_attention = Sparsemax()
 15 | >> res = sparse_attention(x, batch)
 16 | >> print(res)
 17 | tensor([0.5343, 0.0000, 0.0000, 0.4657, 0.0612, 0.0000, 0.0000, 0.0000, 0.5640,
 18 |         0.3748])
 19 | 
 20 | """
 21 | import torch
 22 | import torch.nn as nn
 23 | from torch.autograd import Function
 24 | from torch_scatter import scatter_add, scatter_max
 25 | 
 26 | 
 27 | def scatter_sort(x, batch, fill_value=-1e16):
 28 |     num_nodes = scatter_add(batch.new_ones(x.size(0)), batch, dim=0)
 29 |     batch_size, max_num_nodes = num_nodes.size(0), num_nodes.max().item()
 30 | 
 31 |     cum_num_nodes = torch.cat([num_nodes.new_zeros(1), num_nodes.cumsum(dim=0)[:-1]], dim=0)
 32 | 
 33 |     index = torch.arange(batch.size(0), dtype=torch.long, device=x.device)
 34 |     index = (index - cum_num_nodes[batch]) + (batch * max_num_nodes)
 35 | 
 36 |     dense_x = x.new_full((batch_size * max_num_nodes,), fill_value)
 37 |     dense_x[index] = x
 38 |     dense_x = dense_x.view(batch_size, max_num_nodes)
 39 | 
 40 |     sorted_x, _ = dense_x.sort(dim=-1, descending=True)
 41 |     cumsum_sorted_x = sorted_x.cumsum(dim=-1)
 42 |     cumsum_sorted_x = cumsum_sorted_x.view(-1)
 43 | 
 44 |     sorted_x = sorted_x.view(-1)
 45 |     filled_index = sorted_x != fill_value
 46 | 
 47 |     sorted_x = sorted_x[filled_index]
 48 |     cumsum_sorted_x = cumsum_sorted_x[filled_index]
 49 | 
 50 |     return sorted_x, cumsum_sorted_x
 51 | 
 52 | 
 53 | def _make_ix_like(batch):
 54 |     num_nodes = scatter_add(batch.new_ones(batch.size(0)), batch, dim=0)
 55 |     idx = [torch.arange(1, i + 1, dtype=torch.long, device=batch.device) for i in num_nodes]
 56 |     idx = torch.cat(idx, dim=0)
 57 | 
 58 |     return idx
 59 | 
 60 | 
 61 | def _threshold_and_support(x, batch):
 62 |     """Sparsemax building block: compute the threshold
 63 |     Args:
 64 |         x: input tensor to apply the sparsemax
 65 |         batch: group indicators
 66 |     Returns:
 67 |         the threshold value
 68 |     """
 69 |     num_nodes = scatter_add(batch.new_ones(x.size(0)), batch, dim=0)
 70 |     cum_num_nodes = torch.cat([num_nodes.new_zeros(1), num_nodes.cumsum(dim=0)[:-1]], dim=0)
 71 | 
 72 |     sorted_input, input_cumsum = scatter_sort(x, batch)
 73 |     input_cumsum = input_cumsum - 1.0
 74 |     rhos = _make_ix_like(batch).to(x.dtype)
 75 |     support = rhos * sorted_input > input_cumsum
 76 | 
 77 |     support_size = scatter_add(support.to(batch.dtype), batch)
 78 |     # mask invalid index, for example, if batch is not start from 0 or not continuous, it may result in negative index
 79 |     idx = support_size + cum_num_nodes - 1
 80 |     mask = idx < 0
 81 |     idx[mask] = 0
 82 |     tau = input_cumsum.gather(0, idx)
 83 |     tau /= support_size.to(x.dtype)
 84 | 
 85 |     return tau, support_size
 86 | 
 87 | 
 88 | class SparsemaxFunction(Function):
 89 | 
 90 |     @staticmethod
 91 |     def forward(ctx, x, batch):
 92 |         """sparsemax: normalizing sparse transform
 93 |         Parameters:
 94 |             ctx: context object
 95 |             x (Tensor): shape (N, )
 96 |             batch: group indicator
 97 |         Returns:
 98 |             output (Tensor): same shape as input
 99 |         """
100 |         max_val, _ = scatter_max(x, batch)
101 |         x -= max_val[batch]
102 |         tau, supp_size = _threshold_and_support(x, batch)
103 |         output = torch.clamp(x - tau[batch], min=0)
104 |         ctx.save_for_backward(supp_size, output, batch)
105 | 
106 |         return output
107 | 
108 |     @staticmethod
109 |     def backward(ctx, grad_output):
110 |         supp_size, output, batch = ctx.saved_tensors
111 |         grad_input = grad_output.clone()
112 |         grad_input[output == 0] = 0
113 | 
114 |         v_hat = scatter_add(grad_input, batch) / supp_size.to(output.dtype)
115 |         grad_input = torch.where(output != 0, grad_input - v_hat[batch], grad_input)
116 | 
117 |         return grad_input, None
118 | 
119 | 
120 | sparsemax = SparsemaxFunction.apply
121 | 
122 | 
123 | class Sparsemax(nn.Module):
124 | 
125 |     def __init__(self):
126 |         super(Sparsemax, self).__init__()
127 | 
128 |     def forward(self, x, batch):
129 |         return sparsemax(x, batch)
130 | 
131 | 
132 | if __name__ == '__main__':
133 |     sparse_attention = Sparsemax()
134 |     input_x = torch.tensor([1.7301, 0.6792, -1.0565, 1.6614, -0.3196, -0.7790, -0.3877, -0.4943, 0.1831, -0.0061])
135 |     input_batch = torch.cat([torch.zeros(4, dtype=torch.long), torch.ones(6, dtype=torch.long)], dim=0)
136 |     res = sparse_attention(input_x, input_batch)
137 |     print(res)
138 | 


--------------------------------------------------------------------------------
/HGP-SL/mainTL.py:
--------------------------------------------------------------------------------
  1 | import argparse
  2 | import glob
  3 | import os
  4 | import time
  5 | 
  6 | import torch
  7 | import torch.nn.functional as F
  8 | from models import Model
  9 | from torch.utils.data import random_split
 10 | from torch_geometric.data import DataLoader
 11 | from torch_geometric.datasets import TUDataset
 12 | 
 13 | parser = argparse.ArgumentParser()
 14 | 
 15 | parser.add_argument('--seed', type=int, default=777, help='random seed')
 16 | parser.add_argument('--batch_size', type=int, default=512, help='batch size')
 17 | parser.add_argument('--lr', type=float, default=0.001, help='learning rate')
 18 | parser.add_argument('--weight_decay', type=float, default=0.001, help='weight decay')
 19 | parser.add_argument('--nhid', type=int, default=128, help='hidden size')
 20 | parser.add_argument('--sample_neighbor', type=bool, default=True, help='whether sample neighbors')
 21 | parser.add_argument('--sparse_attention', type=bool, default=True, help='whether use sparse attention')
 22 | parser.add_argument('--structure_learning', type=bool, default=True, help='whether perform structure learning')
 23 | parser.add_argument('--pooling_ratio', type=float, default=0.5, help='pooling ratio')
 24 | parser.add_argument('--dropout_ratio', type=float, default=0.4, help='dropout ratio')
 25 | parser.add_argument('--lamb', type=float, default=1.0, help='trade-off parameter')
 26 | parser.add_argument('--dataset', type=str, default='PROTEINS', help='DD/PROTEINS/NCI1/NCI109/Mutagenicity/ENZYMES')
 27 | parser.add_argument('--device', type=str, default='cuda:0', help='specify cuda devices')
 28 | parser.add_argument('--epochs', type=int, default=1000, help='maximum number of epochs')
 29 | parser.add_argument('--patience', type=int, default=100, help='patience for early stopping')
 30 | 
 31 | args = parser.parse_args()
 32 | torch.manual_seed(args.seed)
 33 | if torch.cuda.is_available():
 34 |     print("CUDA")
 35 |     torch.cuda.manual_seed(args.seed)
 36 | 
 37 | 
 38 | dataset = TUDataset("/home/anoopkumar/Brain/HGP-SL/data", name=args.dataset, use_node_attr=False)
 39 | 
 40 | 
 41 | args.num_classes = dataset.num_classes
 42 | args.num_features = dataset.num_features
 43 | 
 44 | print(args)
 45 | 
 46 | print("len of the dataset ######## ",len(dataset))
 47 | 
 48 | num_training = int(len(dataset) * 0.7)
 49 | num_val = int(len(dataset) * 0.1)
 50 | num_test = len(dataset) - (num_training + num_val)
 51 | training_set, validation_set, test_set = random_split(dataset, [num_training, num_val, num_test])
 52 | 
 53 | train_loader = DataLoader(training_set, batch_size=args.batch_size, shuffle=True)
 54 | val_loader = DataLoader(validation_set, batch_size=args.batch_size, shuffle=False)
 55 | test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False)
 56 | 
 57 | model = Model(args).to(args.device)
 58 | 
 59 | model.load_state_dict(torch.load("pre_trained"))
 60 | print("model loaded ################")
 61 | optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
 62 | 
 63 | 
 64 | def train():
 65 |     min_loss = 1e10
 66 |     patience_cnt = 0
 67 |     val_loss_values = []
 68 |     best_epoch = 0
 69 | 
 70 |     t = time.time()
 71 |     model.train()
 72 |     for epoch in range(args.epochs):
 73 |         loss_train = 0.0
 74 |         correct = 0
 75 |         for i, data in enumerate(train_loader):
 76 |             optimizer.zero_grad()
 77 |             data = data.to(args.device)
 78 |             out = model(data)
 79 |             loss = F.nll_loss(out, data.y)
 80 |             loss.backward()
 81 |             optimizer.step()
 82 |             loss_train += loss.item()
 83 |             pred = out.max(dim=1)[1]
 84 |             correct += pred.eq(data.y).sum().item()
 85 |         acc_train = correct / len(train_loader.dataset)
 86 |         acc_val, loss_val = compute_test(val_loader)
 87 |         print('Epoch: {:04d}'.format(epoch + 1), 'loss_train: {:.6f}'.format(loss_train),
 88 |               'acc_train: {:.6f}'.format(acc_train), 'loss_val: {:.6f}'.format(loss_val),
 89 |               'acc_val: {:.6f}'.format(acc_val), 'time: {:.6f}s'.format(time.time() - t))
 90 | 
 91 |         val_loss_values.append(loss_val)
 92 | #         torch.save(model.state_dict(), '{}.pth'.format(epoch))
 93 |         torch.save(model.state_dict(), "pre_trained")
 94 | 
 95 |         if val_loss_values[-1] < min_loss:
 96 |             min_loss = val_loss_values[-1]
 97 |             best_epoch = epoch
 98 |             patience_cnt = 0
 99 |         else:
100 |             patience_cnt += 1
101 | 
102 |         if patience_cnt == args.patience:
103 |             break
104 | 
105 |         files = glob.glob('*.pth')
106 |         for f in files:
107 |             epoch_nb = int(f.split('.')[0])
108 |             if epoch_nb < best_epoch:
109 |                 os.remove(f)
110 | 
111 |     files = glob.glob('*.pth')
112 |     for f in files:
113 |         epoch_nb = int(f.split('.')[0])
114 |         if epoch_nb > best_epoch:
115 |             os.remove(f)
116 |     print('Optimization Finished! Total time elapsed: {:.6f}'.format(time.time() - t))
117 | 
118 |     return best_epoch
119 | 
120 | 
121 | def compute_test(loader, which="val"):
122 |     model.eval()
123 |     correct = 0.0
124 |     loss_test = 0.0
125 |     for data in loader:
126 |         data = data.to(args.device)
127 |         out = model(data)
128 |         pred = out.max(dim=1)[1]
129 |         correct += pred.eq(data.y).sum().item()
130 |         loss_test += F.nll_loss(out, data.y).item()
131 |     return correct / len(loader.dataset), loss_test
132 | 
133 | 
134 | if __name__ == '__main__':
135 |     # Model training
136 |     print("here inside main")
137 |     best_model = train()
138 |     print("here inside main")
139 |     print("done")
140 | #     Restore best model for test set
141 |     model.load_state_dict(torch.load('{}.pth'.format(best_model)))
142 |     test_acc, test_loss = compute_test(test_loader,"test")
143 |     print('Test set results, loss = {:.6f}, accuracy = {:.6f}'.format(test_loss, test_acc))
144 | 
145 | 


--------------------------------------------------------------------------------
/HGP-SL/main.py:
--------------------------------------------------------------------------------
  1 | import argparse
  2 | import glob
  3 | import os
  4 | import time
  5 | 
  6 | import torch
  7 | import torch.nn.functional as F
  8 | from models import Model
  9 | from torch.utils.data import random_split
 10 | from torch_geometric.data import DataLoader
 11 | from torch_geometric.datasets import TUDataset
 12 | 
 13 | parser = argparse.ArgumentParser()
 14 | 
 15 | parser.add_argument('--seed', type=int, default=777, help='random seed')
 16 | parser.add_argument('--batch_size', type=int, default=512, help='batch size')
 17 | parser.add_argument('--lr', type=float, default=0.001, help='learning rate')
 18 | parser.add_argument('--weight_decay', type=float, default=0.001, help='weight decay')
 19 | parser.add_argument('--nhid', type=int, default=128, help='hidden size')
 20 | parser.add_argument('--sample_neighbor', type=bool, default=True, help='whether sample neighbors')
 21 | parser.add_argument('--sparse_attention', type=bool, default=False, help='whether use sparse attention')
 22 | parser.add_argument('--structure_learning', type=bool, default=False, help='whether perform structure learning')
 23 | parser.add_argument('--pooling_ratio', type=float, default=0.5, help='pooling ratio')
 24 | parser.add_argument('--dropout_ratio', type=float, default=0.4, help='dropout ratio')
 25 | parser.add_argument('--lamb', type=float, default=1.0, help='trade-off parameter')
 26 | parser.add_argument('--dataset', type=str, default='PROTEINS', help='DD/PROTEINS/NCI1/NCI109/Mutagenicity/ENZYMES')
 27 | parser.add_argument('--device', type=str, default='cuda:0', help='specify cuda devices')
 28 | parser.add_argument('--epochs', type=int, default=1000, help='maximum number of epochs')
 29 | parser.add_argument('--patience', type=int, default=100, help='patience for early stopping')
 30 | 
 31 | args = parser.parse_args()
 32 | torch.manual_seed(args.seed)
 33 | if torch.cuda.is_available():
 34 |     print("CUDA")
 35 |     torch.cuda.manual_seed(args.seed)
 36 | 
 37 | 
 38 | dataset = TUDataset("/home/anoopkumar/Brain/HGP-SL/data", name=args.dataset, use_node_attr=False, use_edge_attr=True)
 39 | 
 40 | 
 41 | args.num_classes = dataset.num_classes
 42 | args.num_features = dataset.num_features
 43 | 
 44 | print(args)
 45 | 
 46 | print("len of the dataset ######## ",len(dataset))
 47 | 
 48 | num_training = int(len(dataset) * 0.80)
 49 | num_val = int(len(dataset) * 0.1)
 50 | num_test = len(dataset) - (num_training + num_val)
 51 | training_set, validation_set, test_set = random_split(dataset, [num_training, num_val, num_test])
 52 | print(" train len", len(training_set))
 53 | print("val len", len(validation_set))
 54 | print("test len", len(test_set))
 55 | print("random split")
 56 | 
 57 | train_loader = DataLoader(training_set, batch_size=args.batch_size, shuffle=True)
 58 | val_loader = DataLoader(validation_set, batch_size=args.batch_size, shuffle=True)
 59 | test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=True)
 60 | 
 61 | model = Model(args).to(args.device)
 62 | 
 63 | # model.load_state_dict(torch.load("pre_trained"))
 64 | # print("model loaded ################")
 65 | optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
 66 | 
 67 | 
 68 | def train():
 69 |     min_loss = 1e10
 70 |     patience_cnt = 0
 71 |     val_loss_values = []
 72 |     best_epoch = 0
 73 | 
 74 |     t = time.time()
 75 |     model.train()
 76 |     for epoch in range(args.epochs):
 77 |         loss_train = 0.0
 78 |         correct = 0
 79 |         for i, data in enumerate(train_loader):
 80 |             optimizer.zero_grad()
 81 |             data = data.to(args.device)
 82 |             out = model(data)
 83 |             loss = F.nll_loss(out, data.y)
 84 |             loss.backward()
 85 |             optimizer.step()
 86 |             loss_train += loss.item()
 87 |             pred = out.max(dim=1)[1]
 88 |             correct += pred.eq(data.y).sum().item()
 89 |         acc_train = correct / len(train_loader.dataset)
 90 |         acc_val, loss_val = compute_test(val_loader)
 91 |         print('Epoch: {:04d}'.format(epoch + 1), 'loss_train: {:.6f}'.format(loss_train),
 92 |               'acc_train: {:.6f}'.format(acc_train), 'loss_val: {:.6f}'.format(loss_val),
 93 |               'acc_val: {:.6f}'.format(acc_val), 'time: {:.6f}s'.format(time.time() - t))
 94 | 
 95 |         val_loss_values.append(loss_val)
 96 |         torch.save(model.state_dict(), '{}.pth'.format(epoch))
 97 |     #         torch.save(model.state_dict(), "pre_trained")
 98 | 
 99 |         if val_loss_values[-1] < min_loss:
100 |             min_loss = val_loss_values[-1]
101 |             best_epoch = epoch
102 |             patience_cnt = 0
103 |         else:
104 |             patience_cnt += 1
105 | 
106 |         if patience_cnt == args.patience:
107 |             break
108 | 
109 |         files = glob.glob('*.pth')
110 |         for f in files:
111 |             epoch_nb = int(f.split('.')[0])
112 |             if epoch_nb < best_epoch:
113 |                 os.remove(f)
114 | 
115 |     files = glob.glob('*.pth')
116 |     for f in files:
117 |         epoch_nb = int(f.split('.')[0])
118 |         if epoch_nb > best_epoch:
119 |             os.remove(f)
120 |     print('Optimization Finished! Total time elapsed: {:.6f}'.format(time.time() - t))
121 | 
122 |     return best_epoch
123 | 
124 | 
125 | def compute_test(loader, which="val"):
126 |     model.eval()
127 |     correct = 0.0
128 |     loss_test = 0.0
129 |     testResult = []
130 |     asli = []
131 |     for data in loader:
132 |         data = data.to(args.device)
133 |         out = model(data)
134 |         pred = out.max(dim=1)[1]
135 |         testResult.append(pred)
136 |         asli.append(data.y)
137 |         correct += pred.eq(data.y).sum().item()
138 |         loss_test += F.nll_loss(out, data.y).item()
139 | #     print("test results", testResult)
140 | #     print("asli results", asli)
141 |     return correct / len(loader.dataset), loss_test
142 | 
143 | 
144 | if __name__ == '__main__':
145 |     best_model = train()
146 | #     Restore best model for test set
147 |     model.load_state_dict(torch.load('{}.pth'.format(best_model)))
148 |     print("ALL DONE")
149 |     test_acc, test_loss = compute_test(test_loader,"test")
150 |     print('Test set results, loss = {:.6f}, accuracy = {:.6f}'.format(test_loss, test_acc))
151 | 
152 | 


--------------------------------------------------------------------------------
/ADNI_with_Abide_TL.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": null,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "# !pwd\n",
 10 |     "import os\n",
 11 |     "import pandas as pd\n",
 12 |     "import numpy as np"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": null,
 18 |    "metadata": {},
 19 |    "outputs": [],
 20 |    "source": [
 21 |     "all_sub_adjMat = np.load(\"corr_mat_abide_605.npy\")\n",
 22 |     "sub_names = np.load(\"filename_abide_605.npy\", allow_pickle= True)\n",
 23 |     "print(len(all_sub_adjMat), len(sub_names))"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "code",
 28 |    "execution_count": null,
 29 |    "metadata": {},
 30 |    "outputs": [],
 31 |    "source": [
 32 |     "with open('Phenotypic_V1_0b_preprocessed1.csv') as f:\n",
 33 |     "    content = f.readlines()\n",
 34 |     "content = [x.strip() for x in content]\n",
 35 |     "content.pop(-1)\n",
 36 |     "print(len(content))"
 37 |    ]
 38 |   },
 39 |   {
 40 |    "cell_type": "code",
 41 |    "execution_count": null,
 42 |    "metadata": {},
 43 |    "outputs": [],
 44 |    "source": [
 45 |     "df = pd.read_csv('Phenotypic_V1_0b_preprocessed1.csv') # can also index sheet by name or fetch all sheets\n",
 46 |     "label = df['DX_GROUP'].tolist()\n",
 47 |     "subject = df['FILE_ID'].tolist()\n",
 48 |     "print(len(label),label[:5], subject[:5])"
 49 |    ]
 50 |   },
 51 |   {
 52 |    "cell_type": "code",
 53 |    "execution_count": null,
 54 |    "metadata": {},
 55 |    "outputs": [],
 56 |    "source": [
 57 |     "subnames = [str(i)[:-14] for i in sub_names]\n",
 58 |     "print(len(subnames), subnames[:5])"
 59 |    ]
 60 |   },
 61 |   {
 62 |    "cell_type": "code",
 63 |    "execution_count": null,
 64 |    "metadata": {},
 65 |    "outputs": [],
 66 |    "source": [
 67 |     "adni90_feature = []\n",
 68 |     "adni90_labels = []\n",
 69 |     "adni90_adjmat = []\n",
 70 |     "# edge_fea = []\n",
 71 |     "\n",
 72 |     "for x,i in enumerate(subnames):\n",
 73 |     "    ind = [n for n, l in enumerate(subject) if l.startswith(i)]\n",
 74 |     "#     ind = subject.index(i)\n",
 75 |     "    adni90_labels.append(label[ind[0]])\n",
 76 |     "    adni90_feature.append(all_sub_adjMat[x])\n",
 77 |     "\n",
 78 |     "\n",
 79 |     "for i in adni90_feature:\n",
 80 |     "    temp = np.zeros((90, 90))\n",
 81 |     "    for x in range(90):\n",
 82 |     "        for y in range(90):\n",
 83 |     "            if abs(i[x][y]) >= 0.15:\n",
 84 |     "                temp[x][y] = 1\n",
 85 |     "#                 edge_fea.append(i[x][y])\n",
 86 |     "#             else:\n",
 87 |     "#                 edge_fea.append(-999)\n",
 88 |     "    adni90_adjmat.append(list(temp))\n",
 89 |     "\n",
 90 |     "            \n",
 91 |     "    \n",
 92 |     "print(len(adni90_labels), len(adni90_adjmat), len(adni90_feature))\n",
 93 |     "print(np.array(adni90_adjmat[0]).shape)"
 94 |    ]
 95 |   },
 96 |   {
 97 |    "cell_type": "code",
 98 |    "execution_count": null,
 99 |    "metadata": {},
100 |    "outputs": [],
101 |    "source": [
102 |     "avg = []\n",
103 |     "for k in range(605):\n",
104 |     "    count = 0\n",
105 |     "    for j in adni90_adjmat[k]:\n",
106 |     "#         print(len(j))\n",
107 |     "        for i in j:\n",
108 |     "            if i == 1:\n",
109 |     "                count += 1\n",
110 |     "\n",
111 |     "    avg.append(count)\n",
112 |     "print(sum(avg)/len(avg))\n"
113 |    ]
114 |   },
115 |   {
116 |    "cell_type": "code",
117 |    "execution_count": null,
118 |    "metadata": {},
119 |    "outputs": [],
120 |    "source": [
121 |     "# # choose the classification type\n",
122 |     "\n",
123 |     "lab1 = []\n",
124 |     "exp1 = []\n",
125 |     "fea1 = []\n",
126 |     "\n",
127 |     "stop  = 0\n",
128 |     "\n",
129 |     "for c,i in enumerate(adni90_labels):\n",
130 |     "    if i == 1:\n",
131 |     "        lab1.append(1)\n",
132 |     "        exp1.append(adni90_adjmat[c])\n",
133 |     "        \n",
134 |     "    else:\n",
135 |     "        lab1.append(2)\n",
136 |     "        exp1.append(adni90_adjmat[c])\n",
137 |     "        \n",
138 |     "print(len(exp1), len(fea1), len(lab1))"
139 |    ]
140 |   },
141 |   {
142 |    "cell_type": "code",
143 |    "execution_count": null,
144 |    "metadata": {},
145 |    "outputs": [],
146 |    "source": [
147 |     "# for ADNI_A.txt\n",
148 |     "\n",
149 |     "edgeFea = []\n",
150 |     "count = -1\n",
151 |     "adni_a = []\n",
152 |     "for i, x in enumerate(exp1):\n",
153 |     "    temp = []\n",
154 |     "    for a in range(len(x)):\n",
155 |     "        for b in range(len(x[0])):\n",
156 |     "            count += 1\n",
157 |     "            if a < b and x[a][b] == 1:\n",
158 |     "                temp.append((a+1+i*90,b+1+i*90))\n",
159 |     "#                 edgeFea.append(edge_fea[count])\n",
160 |     "\n",
161 |     "    adni_a.extend(temp)\n",
162 |     "\n",
163 |     "print(len(adni_a))\n",
164 |     "print(adni_a[-10:])\n",
165 |     "\n",
166 |     "\n",
167 |     "with open(\"/home/anoopkumar/Brain/HGP-SL/data/ABIDE/raw/ABIDE_A.txt\",\"w\") as f:\n",
168 |     "    for item in adni_a:\n",
169 |     "        f.write(\"%s, %s\\n\" % (item[0],item[1]))\n",
170 |     "\n",
171 |     "# print(len(edgeFea))"
172 |    ]
173 |   },
174 |   {
175 |    "cell_type": "code",
176 |    "execution_count": null,
177 |    "metadata": {},
178 |    "outputs": [],
179 |    "source": [
180 |     "print(len(exp1))"
181 |    ]
182 |   },
183 |   {
184 |    "cell_type": "code",
185 |    "execution_count": null,
186 |    "metadata": {},
187 |    "outputs": [],
188 |    "source": [
189 |     "# ADNI_graph_indicator.txt\n",
190 |     "\n",
191 |     "node_graph_indicator = []\n",
192 |     "for i in range(1,len(exp1)+1):\n",
193 |     "    node_graph_indicator.extend([i for j in range(90)])\n",
194 |     "\n",
195 |     "with open(\"/home/anoopkumar/Brain/HGP-SL/data/ABIDE/raw/ABIDE_graph_indicator.txt\",\"w\") as f:\n",
196 |     "    for item in node_graph_indicator:\n",
197 |     "        f.write(\"%s\\n\" % item) "
198 |    ]
199 |   },
200 |   {
201 |    "cell_type": "code",
202 |    "execution_count": null,
203 |    "metadata": {},
204 |    "outputs": [],
205 |    "source": [
206 |     "# ADNI_graph_labels.txt\n",
207 |     "\n",
208 |     "with open(\"/home/anoopkumar/Brain/HGP-SL/data/ABIDE/raw/ABIDE_graph_labels.txt\",\"w\") as f:\n",
209 |     "    for item in lab1:\n",
210 |     "        f.write(\"%s\\n\" % item) "
211 |    ]
212 |   },
213 |   {
214 |    "cell_type": "code",
215 |    "execution_count": null,
216 |    "metadata": {},
217 |    "outputs": [],
218 |    "source": [
219 |     "# ADNI node labels.txt\n",
220 |     "\n",
221 |     "\n",
222 |     "\n",
223 |     "node_labels = []\n",
224 |     "\n",
225 |     "for i in range(len(exp1)):\n",
226 |     "    for j in range(90):\n",
227 |     "        node_labels.append(j)\n",
228 |     "print(len(node_labels), len(node_graph_indicator))                   \n",
229 |     "with open(\"/home/anoopkumar/Brain/HGP-SL/data/ABIDE/raw/ABIDE_node_labels.txt\",\"w\") as f:\n",
230 |     "    for item in node_labels:\n",
231 |     "        f.write(\"%s\\n\" % item) "
232 |    ]
233 |   },
234 |   {
235 |    "cell_type": "code",
236 |    "execution_count": null,
237 |    "metadata": {},
238 |    "outputs": [],
239 |    "source": [
240 |     "import torch\n",
241 |     "import numpy as np\n",
242 |     "import pandas as pd\n",
243 |     "import os\n",
244 |     "import re\n",
245 |     "import sklearn"
246 |    ]
247 |   },
248 |   {
249 |    "cell_type": "code",
250 |    "execution_count": null,
251 |    "metadata": {},
252 |    "outputs": [],
253 |    "source": [
254 |     "print(torch.__version__, torch.cuda.is_available())"
255 |    ]
256 |   },
257 |   {
258 |    "cell_type": "code",
259 |    "execution_count": null,
260 |    "metadata": {},
261 |    "outputs": [],
262 |    "source": [
263 |     "!which pip\n",
264 |     "\n",
265 |     "os.chdir(\"/home/anoopkumar/Brain/HGP-SL\")\n",
266 |     "!pwd"
267 |    ]
268 |   },
269 |   {
270 |    "cell_type": "code",
271 |    "execution_count": null,
272 |    "metadata": {},
273 |    "outputs": [],
274 |    "source": [
275 |     "!rm -rf /home/anoopkumar/Brain/HGP-SL/data/ABIDE/processed\n",
276 |     "!python main.py --epochs 50 --dataset ABIDE --batch_size 64 --lr 0.0001 --dropout_ratio 0.8 --nhid 128\n",
277 |     "# --sparse_attention False --structure_learning False"
278 |    ]
279 |   },
280 |   {
281 |    "cell_type": "code",
282 |    "execution_count": null,
283 |    "metadata": {},
284 |    "outputs": [],
285 |    "source": [
286 |     "############### load learned weights and use it to train adni #######################"
287 |    ]
288 |   },
289 |   {
290 |    "cell_type": "code",
291 |    "execution_count": null,
292 |    "metadata": {},
293 |    "outputs": [],
294 |    "source": [
295 |     "!rm -rf /home/anoopkumar/Brain/HGP-SL/data/ADNI_612/processed\n",
296 |     "!python mainTL.py --epochs 20 --dataset ADNI_612 --batch_size 8 --lr 0.0001 --dropout_ratio 0.5 --nhid 256 --sparse_attention False --structure_learning False"
297 |    ]
298 |   }
299 |  ],
300 |  "metadata": {
301 |   "kernelspec": {
302 |    "display_name": "Python 3",
303 |    "language": "python",
304 |    "name": "python3"
305 |   },
306 |   "language_info": {
307 |    "codemirror_mode": {
308 |     "name": "ipython",
309 |     "version": 3
310 |    },
311 |    "file_extension": ".py",
312 |    "mimetype": "text/x-python",
313 |    "name": "python",
314 |    "nbconvert_exporter": "python",
315 |    "pygments_lexer": "ipython3",
316 |    "version": "3.7.6"
317 |   }
318 |  },
319 |  "nbformat": 4,
320 |  "nbformat_minor": 2
321 | }
322 | 


--------------------------------------------------------------------------------
/Node2vec.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": null,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd\n",
 10 |     "import pickle\n",
 11 |     "import numpy as np\n",
 12 |     "from stellargraph import StellarGraph\n",
 13 |     "import os\n",
 14 |     "import networkx as nx\n",
 15 |     "import numpy as np\n",
 16 |     "import pandas as pd\n",
 17 |     "\n",
 18 |     "from stellargraph.data import BiasedRandomWalk\n",
 19 |     "from stellargraph import StellarGraph"
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "code",
 24 |    "execution_count": null,
 25 |    "metadata": {},
 26 |    "outputs": [],
 27 |    "source": [
 28 |     "os.chdir('/home/anoopkumar/')\n",
 29 |     "all_sub_adj_mat = []\n",
 30 |     "all_sub_label = []\n",
 31 |     "\n",
 32 |     "with open ('all_subject_adj_mat.pkl','rb') as f1, open('all_subject_label.pkl','rb') as f2:\n",
 33 |     "    all_sub_adj_mat = pickle.load(f1)\n",
 34 |     "    all_sub_label = pickle.load(f2)"
 35 |    ]
 36 |   },
 37 |   {
 38 |    "cell_type": "code",
 39 |    "execution_count": null,
 40 |    "metadata": {},
 41 |    "outputs": [],
 42 |    "source": [
 43 |     "newall = []\n",
 44 |     "for sub in all_sub_adj_mat:\n",
 45 |     "    newsub = []\n",
 46 |     "    for row in sub:\n",
 47 |     "        row = np.append(row,1)\n",
 48 |     "        newsub.append(row)\n",
 49 |     "    newsub = np.vstack([newsub,[1 for i in range(91)]])\n",
 50 |     "    newall.append(newsub)\n",
 51 |     "\n"
 52 |    ]
 53 |   },
 54 |   {
 55 |    "cell_type": "code",
 56 |    "execution_count": null,
 57 |    "metadata": {},
 58 |    "outputs": [],
 59 |    "source": [
 60 |     "print(len(all_sub_label))\n",
 61 |     "print(len(all_sub_adj_mat))\n",
 62 |     "print(len(all_sub_adj_mat[0]))\n",
 63 |     "print(len(all_sub_adj_mat[0][0]))\n",
 64 |     "print(len(newall))\n",
 65 |     "print(len(newall[0]))\n",
 66 |     "print(len(newall[0][0]))"
 67 |    ]
 68 |   },
 69 |   {
 70 |    "cell_type": "code",
 71 |    "execution_count": null,
 72 |    "metadata": {},
 73 |    "outputs": [],
 74 |    "source": [
 75 |     "all_sg = [[] for i in range(160)]"
 76 |    ]
 77 |   },
 78 |   {
 79 |    "cell_type": "code",
 80 |    "execution_count": null,
 81 |    "metadata": {},
 82 |    "outputs": [],
 83 |    "source": [
 84 |     "for count, sub in enumerate(newall):\n",
 85 |     "    np.fill_diagonal(sub, np.nan)\n",
 86 |     "    df = pd.DataFrame(sub)\n",
 87 |     "    df = df.stack().reset_index()\n",
 88 |     "    pairs = list(zip(df['level_0'], df['level_1']))\n",
 89 |     "    for i in pairs:\n",
 90 |     "            a, b = i\n",
 91 |     "            if (b,a) not in all_sg[count]:\n",
 92 |     "                all_sg[count].append(i)"
 93 |    ]
 94 |   },
 95 |   {
 96 |    "cell_type": "code",
 97 |    "execution_count": null,
 98 |    "metadata": {},
 99 |    "outputs": [],
100 |    "source": [
101 |     "all_sg_lr = [[] for _ in range(160)]\n",
102 |     "\n",
103 |     "for i,x in enumerate(all_sg):\n",
104 |     "    all_sg_lr[i] = [[],[]]\n",
105 |     "    for pair in x:\n",
106 |     "        a, b = pair\n",
107 |     "        all_sg_lr[i][0].append(a)\n",
108 |     "        all_sg_lr[i][1].append(b)"
109 |    ]
110 |   },
111 |   {
112 |    "cell_type": "code",
113 |    "execution_count": null,
114 |    "metadata": {},
115 |    "outputs": [],
116 |    "source": [
117 |     "graphs = []\n",
118 |     "\n",
119 |     "for one_sglr in all_sg_lr:\n",
120 |     "    dic = {\"source\": one_sglr[0],\"target\":one_sglr[1]}\n",
121 |     "    one = pd.DataFrame(dic)\n",
122 |     "    graphs.append(StellarGraph(edges = one))"
123 |    ]
124 |   },
125 |   {
126 |    "cell_type": "code",
127 |    "execution_count": null,
128 |    "metadata": {},
129 |    "outputs": [],
130 |    "source": [
131 |     "print(graphs[0].info()) \n",
132 |     "G = graphs"
133 |    ]
134 |   },
135 |   {
136 |    "cell_type": "code",
137 |    "execution_count": null,
138 |    "metadata": {},
139 |    "outputs": [],
140 |    "source": [
141 |     "allwalk = []\n",
142 |     "for sub in G:\n",
143 |     "    rw = BiasedRandomWalk(sub)\n",
144 |     "    walks = rw.run(\n",
145 |     "    nodes=list(sub.nodes()),  # root nodes\n",
146 |     "    length=100,  # maximum length of a random walk\n",
147 |     "    n=10,  # number of random walks per root node\n",
148 |     "    p=0.5,  # Defines (unormalised) probability, 1/p, of returning to source node\n",
149 |     "    q=2.0,  # Defines (unormalised) probability, 1/q, for moving away from source node\n",
150 |     "    )\n",
151 |     "    allwalk.append(walks)\n",
152 |     "# print(\"Number of random walks: {}\".format(len(walks)))\n"
153 |    ]
154 |   },
155 |   {
156 |    "cell_type": "code",
157 |    "execution_count": null,
158 |    "metadata": {},
159 |    "outputs": [],
160 |    "source": [
161 |     "print(len(allwalk))"
162 |    ]
163 |   },
164 |   {
165 |    "cell_type": "code",
166 |    "execution_count": null,
167 |    "metadata": {},
168 |    "outputs": [],
169 |    "source": [
170 |     "modelvecs = []\n",
171 |     "from gensim.models import Word2Vec\n",
172 |     "\n",
173 |     "for walks in allwalk:\n",
174 |     "    str_walks = [[str(n) for n in walk] for walk in walks]\n",
175 |     "    model = Word2Vec(str_walks, size=80, window=5, min_count=0, sg=1, workers=2, iter=1)\n",
176 |     "    modelvecs.append(model)"
177 |    ]
178 |   },
179 |   {
180 |    "cell_type": "code",
181 |    "execution_count": null,
182 |    "metadata": {},
183 |    "outputs": [],
184 |    "source": [
185 |     "graphembeds = []\n",
186 |     "\n",
187 |     "for vecs in modelvecs:\n",
188 |     "    node_embeds = (vecs.wv.vectors)\n",
189 |     "    print(len(node_embeds))\n",
190 |     "    break"
191 |    ]
192 |   },
193 |   {
194 |    "cell_type": "code",
195 |    "execution_count": null,
196 |    "metadata": {},
197 |    "outputs": [],
198 |    "source": [
199 |     "supernode = []\n",
200 |     "avgnode = []\n",
201 |     "\n",
202 |     "for vecs in modelvecs:\n",
203 |     "    node_embeds = (vecs.wv.vectors)\n",
204 |     "    supernode.append(node_embeds[-1])\n",
205 |     "    avgnode.append(node_embeds.mean(axis = 0)) "
206 |    ]
207 |   },
208 |   {
209 |    "cell_type": "code",
210 |    "execution_count": null,
211 |    "metadata": {},
212 |    "outputs": [],
213 |    "source": [
214 |     "print(len(supernode),len(avgnode))\n",
215 |     "graph_labels = []\n",
216 |     "rem_ad = []\n",
217 |     "for c,i in enumerate(all_sub_label):\n",
218 |     "    if i == 'CN':\n",
219 |     "        graph_labels.append(1)\n",
220 |     "    elif i == 'MCI' or i == 'EMCI' or i == 'LMCI':\n",
221 |     "        graph_labels.append(-1)\n",
222 |     "    else:\n",
223 |     "        rem_ad.append(c)\n",
224 |     "\n",
225 |     "exp1 = []\n",
226 |     "for i in range(160):\n",
227 |     "    if i not in rem_ad:\n",
228 |     "        exp1.append(supernode[i])\n",
229 |     "        \n",
230 |     "print(len(exp1), len (graph_labels))"
231 |    ]
232 |   },
233 |   {
234 |    "cell_type": "code",
235 |    "execution_count": null,
236 |    "metadata": {},
237 |    "outputs": [],
238 |    "source": [
239 |     "############# supernode check #############################################"
240 |    ]
241 |   },
242 |   {
243 |    "cell_type": "code",
244 |    "execution_count": null,
245 |    "metadata": {},
246 |    "outputs": [],
247 |    "source": [
248 |     "data = exp1\n",
249 |     "label = graph_labels\n",
250 |     "print(len(data),len(label))"
251 |    ]
252 |   },
253 |   {
254 |    "cell_type": "code",
255 |    "execution_count": null,
256 |    "metadata": {},
257 |    "outputs": [],
258 |    "source": [
259 |     "pd.DataFrame(label).value_counts().to_frame()"
260 |    ]
261 |   },
262 |   {
263 |    "cell_type": "code",
264 |    "execution_count": null,
265 |    "metadata": {},
266 |    "outputs": [],
267 |    "source": [
268 |     "from sklearn.model_selection import cross_validate\n",
269 |     "from sklearn.neural_network import MLPClassifier\n",
270 |     "from sklearn.pipeline import make_pipeline\n",
271 |     "from sklearn.preprocessing import StandardScaler\n",
272 |     "from sklearn.ensemble import GradientBoostingClassifier\n",
273 |     "from sklearn.svm import SVC\n",
274 |     "clf1 = SVC(gamma='auto')\n",
275 |     "# clf2 =make_pipeline(StandardScaler(), MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(16, 4), random_state=1))\n",
276 |     "clf3 = GradientBoostingClassifier(n_estimators=200, learning_rate=1.0,max_depth=5, random_state=0)\n",
277 |     "\n",
278 |     "X = np.array(data)\n",
279 |     "y = np.array(label)\n"
280 |    ]
281 |   },
282 |   {
283 |    "cell_type": "code",
284 |    "execution_count": null,
285 |    "metadata": {},
286 |    "outputs": [],
287 |    "source": [
288 |     "cv_results = cross_validate(clf1, X, y, cv=40, scoring = ['accuracy','roc_auc','balanced_accuracy'])\n",
289 |     "# cv_results2 = cross_validate(clf2, X, y, cv=40)\n",
290 |     "cv_results3 = cross_validate(clf3, X, y, cv=40,scoring = ['accuracy','roc_auc','balanced_accuracy'])"
291 |    ]
292 |   },
293 |   {
294 |    "cell_type": "code",
295 |    "execution_count": null,
296 |    "metadata": {},
297 |    "outputs": [],
298 |    "source": [
299 |     "print(np.mean(cv_results['test_roc_auc']))\n",
300 |     "# print((cv_results['test_score']))\n",
301 |     "print(np.mean(cv_results3['test_roc_auc']))\n",
302 |     "print(np.mean(cv_results3['test_balanced_accuracy']))\n"
303 |    ]
304 |   },
305 |   {
306 |    "cell_type": "code",
307 |    "execution_count": null,
308 |    "metadata": {},
309 |    "outputs": [],
310 |    "source": []
311 |   }
312 |  ],
313 |  "metadata": {
314 |   "kernelspec": {
315 |    "display_name": "Python 3",
316 |    "language": "python",
317 |    "name": "python3"
318 |   },
319 |   "language_info": {
320 |    "codemirror_mode": {
321 |     "name": "ipython",
322 |     "version": 3
323 |    },
324 |    "file_extension": ".py",
325 |    "mimetype": "text/x-python",
326 |    "name": "python",
327 |    "nbconvert_exporter": "python",
328 |    "pygments_lexer": "ipython3",
329 |    "version": "3.7.6"
330 |   }
331 |  },
332 |  "nbformat": 4,
333 |  "nbformat_minor": 4
334 | }
335 | 


--------------------------------------------------------------------------------
/HGP-SL/layers.py:
--------------------------------------------------------------------------------
  1 | import torch
  2 | import torch.nn as nn
  3 | import torch.nn.functional as F
  4 | from sparse_softmax import Sparsemax
  5 | from torch.nn import Parameter
  6 | from torch_geometric.data import Data
  7 | from torch_geometric.nn.conv import MessagePassing
  8 | from torch_geometric.nn.pool.topk_pool import topk, filter_adj
  9 | from torch_geometric.utils import softmax, dense_to_sparse, add_remaining_self_loops
 10 | from torch_scatter import scatter_add
 11 | from torch_sparse import spspmm, coalesce
 12 | 
 13 | 
 14 | class TwoHopNeighborhood(object):
 15 |     def __call__(self, data):
 16 |         edge_index, edge_attr = data.edge_index, data.edge_attr
 17 |         n = data.num_nodes
 18 | 
 19 |         value = edge_index.new_ones((edge_index.size(1),), dtype=torch.float)
 20 | 
 21 |         index, value = spspmm(edge_index, value, edge_index, value, n, n, n)
 22 |         value.fill_(0)
 23 | 
 24 |         edge_index = torch.cat([edge_index, index], dim=1)
 25 |         if edge_attr is None:
 26 |             data.edge_index, _ = coalesce(edge_index, None, n, n)
 27 |         else:
 28 |             value = value.view(-1, *[1 for _ in range(edge_attr.dim() - 1)])
 29 |             value = value.expand(-1, *list(edge_attr.size())[1:])
 30 |             edge_attr = torch.cat([edge_attr, value], dim=0)
 31 |             data.edge_index, edge_attr = coalesce(edge_index, edge_attr, n, n)
 32 |             data.edge_attr = edge_attr
 33 | 
 34 |         return data
 35 | 
 36 |     def __repr__(self):
 37 |         return '{}()'.format(self.__class__.__name__)
 38 | 
 39 | 
 40 | class GCN(MessagePassing):
 41 |     def __init__(self, in_channels, out_channels, cached=False, bias=True, **kwargs):
 42 |         super(GCN, self).__init__(aggr='add', **kwargs)
 43 | 
 44 |         self.in_channels = in_channels
 45 |         self.out_channels = out_channels
 46 |         self.cached = cached
 47 |         self.cached_result = None
 48 |         self.cached_num_edges = None
 49 | 
 50 |         self.weight = Parameter(torch.Tensor(in_channels, out_channels))
 51 |         nn.init.xavier_uniform_(self.weight.data)
 52 | 
 53 |         if bias:
 54 |             self.bias = Parameter(torch.Tensor(out_channels))
 55 |             nn.init.zeros_(self.bias.data)
 56 |         else:
 57 |             self.register_parameter('bias', None)
 58 | 
 59 |         self.reset_parameters()
 60 | 
 61 |     def reset_parameters(self):
 62 |         self.cached_result = None
 63 |         self.cached_num_edges = None
 64 | 
 65 |     @staticmethod
 66 |     def norm(edge_index, num_nodes, edge_weight, dtype=None):
 67 |         if edge_weight is None:
 68 |             edge_weight = torch.ones((edge_index.size(1),), dtype=dtype, device=edge_index.device)
 69 | 
 70 |         row, col = edge_index
 71 |         deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
 72 |         deg_inv_sqrt = deg.pow(-0.5)
 73 |         deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
 74 | 
 75 |         return edge_index, deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]
 76 | 
 77 |     def forward(self, x, edge_index, edge_weight=None):
 78 |         x = torch.matmul(x, self.weight)
 79 | 
 80 |         if self.cached and self.cached_result is not None:
 81 |             if edge_index.size(1) != self.cached_num_edges:
 82 |                 raise RuntimeError(
 83 |                     'Cached {} number of edges, but found {}'.format(self.cached_num_edges, edge_index.size(1)))
 84 | 
 85 |         if not self.cached or self.cached_result is None:
 86 |             self.cached_num_edges = edge_index.size(1)
 87 |             edge_index, norm = self.norm(edge_index, x.size(0), edge_weight, x.dtype)
 88 |             self.cached_result = edge_index, norm
 89 | 
 90 |         edge_index, norm = self.cached_result
 91 | 
 92 |         return self.propagate(edge_index, x=x, norm=norm)
 93 | 
 94 |     def message(self, x_j, norm):
 95 |         return norm.view(-1, 1) * x_j
 96 | 
 97 |     def update(self, aggr_out):
 98 |         if self.bias is not None:
 99 |             aggr_out = aggr_out + self.bias
100 |         return aggr_out
101 | 
102 |     def __repr__(self):
103 |         return '{}({}, {})'.format(self.__class__.__name__, self.in_channels, self.out_channels)
104 | 
105 | 
106 | class NodeInformationScore(MessagePassing):
107 |     def __init__(self, improved=False, cached=False, **kwargs):
108 |         super(NodeInformationScore, self).__init__(aggr='add', **kwargs)
109 | 
110 |         self.improved = improved
111 |         self.cached = cached
112 |         self.cached_result = None
113 |         self.cached_num_edges = None
114 | 
115 |     @staticmethod
116 |     def norm(edge_index, num_nodes, edge_weight, dtype=None):
117 |         if edge_weight is None:
118 |             edge_weight = torch.ones((edge_index.size(1),), dtype=dtype, device=edge_index.device)
119 | 
120 |         row, col = edge_index
121 |         deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
122 |         deg_inv_sqrt = deg.pow(-0.5)
123 |         deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
124 | 
125 |         edge_index, edge_weight = add_remaining_self_loops(edge_index, edge_weight, 0, num_nodes)
126 | 
127 |         row, col = edge_index
128 |         expand_deg = torch.zeros((edge_weight.size(0),), dtype=dtype, device=edge_index.device)
129 |         expand_deg[-num_nodes:] = torch.ones((num_nodes,), dtype=dtype, device=edge_index.device)
130 | 
131 |         return edge_index, expand_deg - deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]
132 | 
133 |     def forward(self, x, edge_index, edge_weight):
134 |         if self.cached and self.cached_result is not None:
135 |             if edge_index.size(1) != self.cached_num_edges:
136 |                 raise RuntimeError(
137 |                     'Cached {} number of edges, but found {}'.format(self.cached_num_edges, edge_index.size(1)))
138 | 
139 |         if not self.cached or self.cached_result is None:
140 |             self.cached_num_edges = edge_index.size(1)
141 |             edge_index, norm = self.norm(edge_index, x.size(0), edge_weight, x.dtype)
142 |             self.cached_result = edge_index, norm
143 | 
144 |         edge_index, norm = self.cached_result
145 | 
146 |         return self.propagate(edge_index, x=x, norm=norm)
147 | 
148 |     def message(self, x_j, norm):
149 |         return norm.view(-1, 1) * x_j
150 | 
151 |     def update(self, aggr_out):
152 |         return aggr_out
153 | 
154 | 
155 | class HGPSLPool(torch.nn.Module):
156 |     def __init__(self, in_channels, ratio=0.8, sample=False, sparse=False, sl=True, lamb=1.0, negative_slop=0.2):
157 |         super(HGPSLPool, self).__init__()
158 |         self.in_channels = in_channels
159 |         self.ratio = ratio
160 |         self.sample = sample
161 |         self.sparse = sparse
162 |         self.sl = sl
163 |         self.negative_slop = negative_slop
164 |         self.lamb = lamb
165 | 
166 |         self.att = Parameter(torch.Tensor(1, self.in_channels * 2))
167 |         nn.init.xavier_uniform_(self.att.data)
168 |         self.sparse_attention = Sparsemax()
169 |         self.neighbor_augment = TwoHopNeighborhood()
170 |         self.calc_information_score = NodeInformationScore()
171 | 
172 |     def forward(self, x, edge_index, edge_attr, batch=None):
173 |         if batch is None:
174 |             batch = edge_index.new_zeros(x.size(0))
175 | 
176 |         x_information_score = self.calc_information_score(x, edge_index, edge_attr)
177 |         score = torch.sum(torch.abs(x_information_score), dim=1)
178 | 
179 |         # Graph Pooling
180 |         original_x = x
181 |         perm = topk(score, self.ratio, batch)
182 |         x = x[perm]
183 |         batch = batch[perm]
184 |         induced_edge_index, induced_edge_attr = filter_adj(edge_index, edge_attr, perm, num_nodes=score.size(0))
185 | 
186 |         # Discard structure learning layer, directly return
187 |         if self.sl is False:
188 |             return x, induced_edge_index, induced_edge_attr, batch
189 | 
190 |         # Structure Learning
191 |         if self.sample:
192 |             # A fast mode for large graphs.
193 |             # In large graphs, learning the possible edge weights between each pair of nodes is time consuming.
194 |             # To accelerate this process, we sample it's K-Hop neighbors for each node and then learn the
195 |             # edge weights between them.
196 |             k_hop = 3
197 |             if edge_attr is None:
198 |                 edge_attr = torch.ones((edge_index.size(1),), dtype=torch.float, device=edge_index.device)
199 | 
200 |             hop_data = Data(x=original_x, edge_index=edge_index, edge_attr=edge_attr)
201 |             for _ in range(k_hop - 1):
202 |                 hop_data = self.neighbor_augment(hop_data)
203 |             hop_edge_index = hop_data.edge_index
204 |             hop_edge_attr = hop_data.edge_attr
205 |             new_edge_index, new_edge_attr = filter_adj(hop_edge_index, hop_edge_attr, perm, num_nodes=score.size(0))
206 | 
207 |             new_edge_index, new_edge_attr = add_remaining_self_loops(new_edge_index, new_edge_attr, 0, x.size(0))
208 |             row, col = new_edge_index
209 |             weights = (torch.cat([x[row], x[col]], dim=1) * self.att).sum(dim=-1)
210 |             weights = F.leaky_relu(weights, self.negative_slop) + new_edge_attr * self.lamb
211 |             adj = torch.zeros((x.size(0), x.size(0)), dtype=torch.float, device=x.device)
212 |             adj[row, col] = weights
213 |             new_edge_index, weights = dense_to_sparse(adj)
214 |             row, col = new_edge_index
215 |             if self.sparse:
216 |                 new_edge_attr = self.sparse_attention(weights, row)
217 |             else:
218 |                 new_edge_attr = softmax(weights, row, x.size(0))
219 |             # filter out zero weight edges
220 |             adj[row, col] = new_edge_attr
221 |             new_edge_index, new_edge_attr = dense_to_sparse(adj)
222 |             # release gpu memory
223 |             del adj
224 |             torch.cuda.empty_cache()
225 |         else:
226 |             # Learning the possible edge weights between each pair of nodes in the pooled subgraph, relative slower.
227 |             if edge_attr is None:
228 |                 induced_edge_attr = torch.ones((induced_edge_index.size(1),), dtype=x.dtype,
229 |                                                device=induced_edge_index.device)
230 |             num_nodes = scatter_add(batch.new_ones(x.size(0)), batch, dim=0)
231 |             shift_cum_num_nodes = torch.cat([num_nodes.new_zeros(1), num_nodes.cumsum(dim=0)[:-1]], dim=0)
232 |             cum_num_nodes = num_nodes.cumsum(dim=0)
233 |             adj = torch.zeros((x.size(0), x.size(0)), dtype=torch.float, device=x.device)
234 |             # Construct batch fully connected graph in block diagonal matirx format
235 |             for idx_i, idx_j in zip(shift_cum_num_nodes, cum_num_nodes):
236 |                 adj[idx_i:idx_j, idx_i:idx_j] = 1.0
237 |             new_edge_index, _ = dense_to_sparse(adj)
238 |             row, col = new_edge_index
239 | 
240 |             weights = (torch.cat([x[row], x[col]], dim=1) * self.att).sum(dim=-1)
241 |             weights = F.leaky_relu(weights, self.negative_slop)
242 |             adj[row, col] = weights
243 |             induced_row, induced_col = induced_edge_index
244 | 
245 |             adj[induced_row, induced_col] += induced_edge_attr * self.lamb
246 |             weights = adj[row, col]
247 |             if self.sparse:
248 |                 new_edge_attr = self.sparse_attention(weights, row)
249 |             else:
250 |                 new_edge_attr = softmax(weights, row, x.size(0))
251 |             # filter out zero weight edges
252 |             adj[row, col] = new_edge_attr
253 |             new_edge_index, new_edge_attr = dense_to_sparse(adj)
254 |             # release gpu memory
255 |             del adj
256 |             torch.cuda.empty_cache()
257 | 
258 |         return x, new_edge_index, new_edge_attr, batch
259 | 


--------------------------------------------------------------------------------
/Hierarchical GCN.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": null,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "# !pwd\n",
 10 |     "import os\n",
 11 |     "import pandas as pd\n",
 12 |     "import numpy as np"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": null,
 18 |    "metadata": {
 19 |     "scrolled": true
 20 |    },
 21 |    "outputs": [],
 22 |    "source": [
 23 |     "all_sub_adjMat = np.load(\"adni_612_corrmat.npy\")\n",
 24 |     "sub_names = np.load(\"adni_612_filenames.npy\", allow_pickle= True)\n",
 25 |     "print(len(all_sub_adjMat), len(sub_names))"
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "code",
 30 |    "execution_count": null,
 31 |    "metadata": {
 32 |     "scrolled": false
 33 |    },
 34 |    "outputs": [],
 35 |    "source": [
 36 |     "len(all_sub_adjMat), np.shape(all_sub_adjMat), type(all_sub_adjMat)"
 37 |    ]
 38 |   },
 39 |   {
 40 |    "cell_type": "code",
 41 |    "execution_count": null,
 42 |    "metadata": {},
 43 |    "outputs": [],
 44 |    "source": [
 45 |     "all_sub_adjMat = np.delete(all_sub_adjMat, (130,460, 540), axis=0)\n",
 46 |     "sub_names = np.delete(sub_names, (130,460, 540))\n",
 47 |     "print(len(all_sub_adjMat), len(sub_names))"
 48 |    ]
 49 |   },
 50 |   {
 51 |    "cell_type": "code",
 52 |    "execution_count": null,
 53 |    "metadata": {},
 54 |    "outputs": [],
 55 |    "source": [
 56 |     "with open('ADNI_largerdataset_labels.csv') as f:\n",
 57 |     "    content = f.readlines()\n",
 58 |     "content = [x.strip() for x in content]\n",
 59 |     "content.pop(-1)\n",
 60 |     "print(content[0])\n",
 61 |     "print(len(content))"
 62 |    ]
 63 |   },
 64 |   {
 65 |    "cell_type": "code",
 66 |    "execution_count": null,
 67 |    "metadata": {},
 68 |    "outputs": [],
 69 |    "source": [
 70 |     "with open('imageid_visitid.txt', 'r') as g:\n",
 71 |     "    lines = g.readlines()\n",
 72 |     "lines = [x.strip() for x in lines]\n",
 73 |     "image_ids = [x[0:7] for x in lines]\n",
 74 |     "visit_ids =  [x[9:] for x in lines]\n",
 75 |     "print(lines[0], image_ids[0], visit_ids[0])"
 76 |    ]
 77 |   },
 78 |   {
 79 |    "cell_type": "code",
 80 |    "execution_count": null,
 81 |    "metadata": {
 82 |     "scrolled": true
 83 |    },
 84 |    "outputs": [],
 85 |    "source": [
 86 |     "df = pd.read_csv('ADNI_largerdataset_labels.csv') # can also index sheet by name or fetch all sheets\n",
 87 |     "label = df['Group'].tolist()\n",
 88 |     "subject = df['Image Data ID'].tolist()\n",
 89 |     "print(len(label),label[:5], subject[:5])"
 90 |    ]
 91 |   },
 92 |   {
 93 |    "cell_type": "code",
 94 |    "execution_count": null,
 95 |    "metadata": {},
 96 |    "outputs": [],
 97 |    "source": [
 98 |     "visitnames = [str(i)[11:] for i in sub_names]\n",
 99 |     "visitnames = [str(i)[0:-21] for i in visitnames]\n",
100 |     "print(visitnames[0])\n",
101 |     "\n",
102 |     "subnames = []\n",
103 |     "for i in range(len(visitnames)):\n",
104 |     "    index = visit_ids.index(visitnames[i])\n",
105 |     "    subnames.append(image_ids[index])\n",
106 |     "    "
107 |    ]
108 |   },
109 |   {
110 |    "cell_type": "code",
111 |    "execution_count": null,
112 |    "metadata": {
113 |     "scrolled": true
114 |    },
115 |    "outputs": [],
116 |    "source": [
117 |     "print(len(subnames), subnames[:5])"
118 |    ]
119 |   },
120 |   {
121 |    "cell_type": "code",
122 |    "execution_count": null,
123 |    "metadata": {},
124 |    "outputs": [],
125 |    "source": [
126 |     "subnames.index('visitid')"
127 |    ]
128 |   },
129 |   {
130 |    "cell_type": "code",
131 |    "execution_count": null,
132 |    "metadata": {},
133 |    "outputs": [],
134 |    "source": [
135 |     "print(len(subnames), subnames[:5])\n",
136 |     "for x,i in enumerate(subnames):\n",
137 |     "    print(x,i)\n",
138 |     "    break\n",
139 |     "    \n",
140 |     "# print(len(subject), subject[])"
141 |    ]
142 |   },
143 |   {
144 |    "cell_type": "code",
145 |    "execution_count": null,
146 |    "metadata": {},
147 |    "outputs": [],
148 |    "source": [
149 |     "adni90_feature = []\n",
150 |     "adni90_labels = []\n",
151 |     "adni90_adjmat = []\n",
152 |     "edge_fea = []\n",
153 |     "\n",
154 |     "for x,i in enumerate(subnames):\n",
155 |     "    if i =='visitid':\n",
156 |     "        continue\n",
157 |     "    ind = subject.index(i)\n",
158 |     "    adni90_labels.append(label[ind])\n",
159 |     "    adni90_feature.append(all_sub_adjMat[x])\n",
160 |     "\n",
161 |     "for i in adni90_feature:\n",
162 |     "    temp = np.zeros((90,90))\n",
163 |     "    for x in range(90):\n",
164 |     "        for y in range(90):\n",
165 |     "            if abs(i[x][y]) > 0.15:\n",
166 |     "                temp[x][y] = 1\n",
167 |     "                edge_fea.append(i[x][y])\n",
168 |     "            else:\n",
169 |     "                edge_fea.append(-999)\n",
170 |     "    adni90_adjmat.append(list(temp))\n",
171 |     "            \n",
172 |     "    \n",
173 |     "print(len(adni90_labels), len(adni90_adjmat), len(adni90_feature))\n",
174 |     "print(np.array(adni90_adjmat[0]).shape)"
175 |    ]
176 |   },
177 |   {
178 |    "cell_type": "code",
179 |    "execution_count": null,
180 |    "metadata": {},
181 |    "outputs": [],
182 |    "source": [
183 |     "# # choose the classification type\n",
184 |     "\n",
185 |     "lab1 = []\n",
186 |     "exp1 = []\n",
187 |     "fea1 = []\n",
188 |     "\n",
189 |     "c1,c2 = 0, 0\n",
190 |     "\n",
191 |     "for c,i in enumerate(adni90_labels):\n",
192 |     "    if i == 'EMCI' or i == 'LMCI' or i == 'MCI':\n",
193 |     "#     if i == 'CN':\n",
194 |     "        c1 += 1\n",
195 |     "#         if c1 == 150:\n",
196 |     "#             c1 -= 1\n",
197 |     "#             continue\n",
198 |     "        lab1.append(1)\n",
199 |     "        exp1.append(adni90_adjmat[c])\n",
200 |     "        fea1.append(adni90_feature[c])\n",
201 |     "        \n",
202 |     "#     elif i == 'AD':\n",
203 |     "    elif i == 'CN':\n",
204 |     "        c2 += 1\n",
205 |     "        lab1.append(2)\n",
206 |     "        exp1.append(adni90_adjmat[c])\n",
207 |     "        fea1.append(adni90_feature[c])\n",
208 |     "    \n",
209 |     "\n",
210 |     "print(len(exp1), len(fea1), len(lab1))\n",
211 |     "print(c1, c2) "
212 |    ]
213 |   },
214 |   {
215 |    "cell_type": "code",
216 |    "execution_count": null,
217 |    "metadata": {
218 |     "scrolled": true
219 |    },
220 |    "outputs": [],
221 |    "source": [
222 |     "# print(len(edge_fea), len(adni_a))\n",
223 |     "print(lab1)"
224 |    ]
225 |   },
226 |   {
227 |    "cell_type": "code",
228 |    "execution_count": null,
229 |    "metadata": {
230 |     "scrolled": true
231 |    },
232 |    "outputs": [],
233 |    "source": [
234 |     "!pwd\n",
235 |     "os.chdir(\"/home/anoopkumar/Brain/HGP-SL/data\")\n",
236 |     "!pwd"
237 |    ]
238 |   },
239 |   {
240 |    "cell_type": "code",
241 |    "execution_count": null,
242 |    "metadata": {},
243 |    "outputs": [],
244 |    "source": [
245 |     "# for ADNI_A.txt\n",
246 |     "\n",
247 |     "edgeFea = []\n",
248 |     "count = -1\n",
249 |     "adni_a = []\n",
250 |     "for i, x in enumerate(exp1):\n",
251 |     "    temp = []\n",
252 |     "    for a in range(len(x)):\n",
253 |     "        for b in range(len(x[0])):\n",
254 |     "            count += 1\n",
255 |     "            if a < b and x[a][b] == 1:\n",
256 |     "                temp.append((a+1+i*90,b+1+i*90))\n",
257 |     "                edgeFea.append(edge_fea[count])\n",
258 |     "\n",
259 |     "    adni_a.extend(temp)\n",
260 |     "\n",
261 |     "print(len(adni_a))\n",
262 |     "print(adni_a[-10:])\n",
263 |     "\n",
264 |     "\n",
265 |     "with open(\"ADNI_612/raw/ADNI_612_A.txt\",\"w\") as f:\n",
266 |     "    for item in adni_a:\n",
267 |     "        f.write(\"%s, %s\\n\" % (item[0],item[1]))\n",
268 |     "\n",
269 |     "print(len(edgeFea))"
270 |    ]
271 |   },
272 |   {
273 |    "cell_type": "code",
274 |    "execution_count": null,
275 |    "metadata": {},
276 |    "outputs": [],
277 |    "source": [
278 |     "# ADNI_graph_indicator.txt\n",
279 |     "\n",
280 |     "node_graph_indicator = []\n",
281 |     "for i in range(1,len(exp1)+1):\n",
282 |     "    node_graph_indicator.extend([i for j in range(90)])\n",
283 |     "\n",
284 |     "with open(\"ADNI_612/raw/ADNI_612_graph_indicator.txt\",\"w\") as f:\n",
285 |     "    for item in node_graph_indicator:\n",
286 |     "        f.write(\"%s\\n\" % item) "
287 |    ]
288 |   },
289 |   {
290 |    "cell_type": "code",
291 |    "execution_count": null,
292 |    "metadata": {},
293 |    "outputs": [],
294 |    "source": [
295 |     "# ADNI_graph_labels.txt\n",
296 |     "\n",
297 |     "with open(\"ADNI_612/raw/ADNI_612_graph_labels.txt\",\"w\") as f:\n",
298 |     "    for item in lab1:\n",
299 |     "        f.write(\"%s\\n\" % item) "
300 |    ]
301 |   },
302 |   {
303 |    "cell_type": "code",
304 |    "execution_count": null,
305 |    "metadata": {},
306 |    "outputs": [],
307 |    "source": [
308 |     "# ADNI node labels.txt\n",
309 |     "\n",
310 |     "\n",
311 |     "\n",
312 |     "node_labels = []\n",
313 |     "\n",
314 |     "for i in range(len(exp1)):\n",
315 |     "    for j in range(90):\n",
316 |     "        node_labels.extend([j])\n",
317 |     "print(len(node_labels), len(node_graph_indicator))                   \n",
318 |     "with open(\"ADNI_612/raw/ADNI_612_node_labels.txt\",\"w\") as f:\n",
319 |     "    for item in node_labels:\n",
320 |     "        f.write(\"%s\\n\" % item) "
321 |    ]
322 |   },
323 |   {
324 |    "cell_type": "code",
325 |    "execution_count": null,
326 |    "metadata": {},
327 |    "outputs": [],
328 |    "source": [
329 |     "# ADNI edge attr\n",
330 |     "\n",
331 |     "\n",
332 |     "with open(\"ADNI_612/raw/ADNI_612_edge_attributes.txt\",\"w\") as f:\n",
333 |     "    for item in edgeFea:\n",
334 |     "        f.write(\"%s\\n\" % item) "
335 |    ]
336 |   },
337 |   {
338 |    "cell_type": "code",
339 |    "execution_count": null,
340 |    "metadata": {},
341 |    "outputs": [],
342 |    "source": [
343 |     "import torch\n",
344 |     "import numpy as np\n",
345 |     "import pandas as pd\n",
346 |     "import os\n",
347 |     "import re\n",
348 |     "import sklearn"
349 |    ]
350 |   },
351 |   {
352 |    "cell_type": "code",
353 |    "execution_count": null,
354 |    "metadata": {},
355 |    "outputs": [],
356 |    "source": [
357 |     "print(torch.__version__, torch.cuda.is_available())"
358 |    ]
359 |   },
360 |   {
361 |    "cell_type": "code",
362 |    "execution_count": null,
363 |    "metadata": {},
364 |    "outputs": [],
365 |    "source": [
366 |     "!which pip\n",
367 |     "\n",
368 |     "os.chdir(\"/home/anoopkumar/Brain/HGP-SL\")\n",
369 |     "!pwd"
370 |    ]
371 |   },
372 |   {
373 |    "cell_type": "code",
374 |    "execution_count": null,
375 |    "metadata": {
376 |     "scrolled": false
377 |    },
378 |    "outputs": [],
379 |    "source": [
380 |     "# !python main_cudastreams2.py --epochs 10 --dataset ADNI_612 --batch_size 4 --lr 0.01 --dropout_ratio 0.5 --nhid 512 \n",
381 |     "# # --sparse_attention False --structure_learning False\n",
382 |     "\n",
383 |     "!rm -rf /home/anoopkumar/Brain/HGP-SL/data/ADNI_612/processed\n",
384 |     "!python main.py --epochs 20 --dataset ADNI_612 --batch_size 8 --lr 0.0001 --dropout_ratio 0.5 --nhid 256 --sparse_attention False --structure_learning False"
385 |    ]
386 |   },
387 |   {
388 |    "cell_type": "code",
389 |    "execution_count": null,
390 |    "metadata": {},
391 |    "outputs": [],
392 |    "source": [
393 |     "#plot the times taken\n",
394 |     "import matplotlib.pyplot as plt"
395 |    ]
396 |   },
397 |   {
398 |    "cell_type": "code",
399 |    "execution_count": null,
400 |    "metadata": {},
401 |    "outputs": [],
402 |    "source": [
403 |     "fig = plt.figure()\n",
404 |     "ax = fig.add_axes([0,0,1,1])\n",
405 |     "ax.bar(times_labels[0:3],times_values[0:3])\n",
406 |     "ax.set_ylabel('sec')\n",
407 |     "ax.set_title('train and test')\n",
408 |     "plt.show()"
409 |    ]
410 |   },
411 |   {
412 |    "cell_type": "code",
413 |    "execution_count": null,
414 |    "metadata": {
415 |     "scrolled": true
416 |    },
417 |    "outputs": [],
418 |    "source": [
419 |     "fig = plt.figure()\n",
420 |     "ax = fig.add_axes([0,0,1,1])\n",
421 |     "ax.bar(times_labels[3:],times_values[3:])\n",
422 |     "ax.set_ylabel('sec')\n",
423 |     "ax.set_title('train time split up')\n",
424 |     "plt.show()"
425 |    ]
426 |   }
427 |  ],
428 |  "metadata": {
429 |   "kernelspec": {
430 |    "display_name": "Python 3",
431 |    "language": "python",
432 |    "name": "python3"
433 |   },
434 |   "language_info": {
435 |    "codemirror_mode": {
436 |     "name": "ipython",
437 |     "version": 3
438 |    },
439 |    "file_extension": ".py",
440 |    "mimetype": "text/x-python",
441 |    "name": "python",
442 |    "nbconvert_exporter": "python",
443 |    "pygments_lexer": "ipython3",
444 |    "version": "3.7.6"
445 |   }
446 |  },
447 |  "nbformat": 4,
448 |  "nbformat_minor": 2
449 | }
450 | 


--------------------------------------------------------------------------------
/Baseline_GCN.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": null,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "!pip3 install stellargraph\n",
 10 |     "!pip3 install pandas\n",
 11 |     "!pip3 install sklearn"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": null,
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "import pickle\n",
 21 |     "import numpy as np\n",
 22 |     "import pandas as pd\n",
 23 |     "import sklearn\n",
 24 |     "import tensorflow as tf\n",
 25 |     "from copy import deepcopy\n",
 26 |     "from stellargraph import StellarGraph\n",
 27 |     "import time"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": null,
 33 |    "metadata": {},
 34 |    "outputs": [],
 35 |    "source": [
 36 |     "!pwd"
 37 |    ]
 38 |   },
 39 |   {
 40 |    "cell_type": "code",
 41 |    "execution_count": null,
 42 |    "metadata": {},
 43 |    "outputs": [],
 44 |    "source": [
 45 |     "all_sub_adjMat = np.load(\"vibha_preprop_adni/correlation_matrices.npy\")\n",
 46 |     "sub_names = np.load(\"vibha_preprop_adni/filenamelist.npy\", allow_pickle= True)\n",
 47 |     "print(len(all_sub_adjMat), len(sub_names))"
 48 |    ]
 49 |   },
 50 |   {
 51 |    "cell_type": "code",
 52 |    "execution_count": null,
 53 |    "metadata": {},
 54 |    "outputs": [],
 55 |    "source": [
 56 |     "subnames = [str(i)[5:15] for i in sub_names]\n",
 57 |     "print(len(subnames), subnames[:5])"
 58 |    ]
 59 |   },
 60 |   {
 61 |    "cell_type": "code",
 62 |    "execution_count": null,
 63 |    "metadata": {},
 64 |    "outputs": [],
 65 |    "source": [
 66 |     "with open('/home/anoopkumar/adni/anoop/bck/filelist.txt') as f:\n",
 67 |     "    content = f.readlines()\n",
 68 |     "content = [x.strip() for x in content]\n",
 69 |     "content.pop(-1)\n",
 70 |     "print(len(content))"
 71 |    ]
 72 |   },
 73 |   {
 74 |    "cell_type": "code",
 75 |    "execution_count": null,
 76 |    "metadata": {},
 77 |    "outputs": [],
 78 |    "source": [
 79 |     "df = pd.read_csv('/home/anoopkumar/adni/anoop/bck/xlabel.csv') # can also index sheet by name or fetch all sheets\n",
 80 |     "label = df['Group'].tolist()\n",
 81 |     "subject = df['Subject'].tolist()\n",
 82 |     "print(len(label),label[:5], subject[:5])"
 83 |    ]
 84 |   },
 85 |   {
 86 |    "cell_type": "code",
 87 |    "execution_count": null,
 88 |    "metadata": {},
 89 |    "outputs": [],
 90 |    "source": [
 91 |     "adni90_feature = []\n",
 92 |     "adni90_labels = []\n",
 93 |     "adni90_adjmat = []\n",
 94 |     "\n",
 95 |     "for x,i in enumerate(subnames):\n",
 96 |     "    ind = subject.index(i)\n",
 97 |     "    adni90_labels.append(label[ind])\n",
 98 |     "    adni90_feature.append(all_sub_adjMat[x])\n",
 99 |     "\n",
100 |     "for i in adni90_feature:\n",
101 |     "    temp = np.zeros((90,90))\n",
102 |     "    for x in range(90):\n",
103 |     "        for y in range(90):\n",
104 |     "            if i[x][y] > 0.15:\n",
105 |     "                temp[x][y] = 1\n",
106 |     "    adni90_adjmat.append(list(temp))\n",
107 |     "            \n",
108 |     "    \n",
109 |     "print(len(adni90_labels), len(adni90_adjmat), len(adni90_feature))"
110 |    ]
111 |   },
112 |   {
113 |    "cell_type": "code",
114 |    "execution_count": null,
115 |    "metadata": {},
116 |    "outputs": [],
117 |    "source": [
118 |     "############################# gcn code ###############################################################"
119 |    ]
120 |   },
121 |   {
122 |    "cell_type": "code",
123 |    "execution_count": null,
124 |    "metadata": {},
125 |    "outputs": [],
126 |    "source": [
127 |     "all_sg = [[] for i in range(152)]\n",
128 |     "\n",
129 |     "for count, sub in enumerate(adni90_adjmat):\n",
130 |     "    np.fill_diagonal(np.array(sub), np.nan)\n",
131 |     "    df = pd.DataFrame(sub)\n",
132 |     "    df = df.stack().reset_index()\n",
133 |     "    df.columns = ['a','b','c']\n",
134 |     "    isedge = df['c'] == 1\n",
135 |     "    dfn = df[isedge]\n",
136 |     "    pairs = list(zip(dfn['a'], dfn['b']))\n",
137 |     "    for i in pairs:\n",
138 |     "            a, b = i\n",
139 |     "            if (b,a) not in all_sg[count]:\n",
140 |     "                all_sg[count].append(i)"
141 |    ]
142 |   },
143 |   {
144 |    "cell_type": "code",
145 |    "execution_count": null,
146 |    "metadata": {},
147 |    "outputs": [],
148 |    "source": [
149 |     "print(len(all_sg))\n",
150 |     "print(len(all_sg[0]))"
151 |    ]
152 |   },
153 |   {
154 |    "cell_type": "code",
155 |    "execution_count": null,
156 |    "metadata": {},
157 |    "outputs": [],
158 |    "source": [
159 |     "all_sg_lr = [[] for _ in range(152)]\n",
160 |     "\n",
161 |     "for i,x in enumerate(all_sg):\n",
162 |     "    all_sg_lr[i] = [[],[]]\n",
163 |     "    for pair in x:\n",
164 |     "        a, b = pair\n",
165 |     "        all_sg_lr[i][0].append(a)\n",
166 |     "        all_sg_lr[i][1].append(b)\n",
167 |     "        "
168 |    ]
169 |   },
170 |   {
171 |    "cell_type": "code",
172 |    "execution_count": null,
173 |    "metadata": {},
174 |    "outputs": [],
175 |    "source": [
176 |     "# for featue\n",
177 |     "\n",
178 |     "all_sub_fea = []\n",
179 |     "for i in adni90_feature:\n",
180 |     "    fead = pd.DataFrame(i)\n",
181 |     "    all_sub_fea.append(fead)"
182 |    ]
183 |   },
184 |   {
185 |    "cell_type": "code",
186 |    "execution_count": null,
187 |    "metadata": {},
188 |    "outputs": [],
189 |    "source": [
190 |     "graphs = []\n",
191 |     "\n",
192 |     "for i,one_sglr in enumerate(all_sg_lr):\n",
193 |     "    dic = {\"source\": one_sglr[0],\"target\":one_sglr[1]}\n",
194 |     "    one = pd.DataFrame(dic)\n",
195 |     "    graphs.append(StellarGraph(all_sub_fea[i], edge = one))"
196 |    ]
197 |   },
198 |   {
199 |    "cell_type": "code",
200 |    "execution_count": null,
201 |    "metadata": {},
202 |    "outputs": [],
203 |    "source": [
204 |     "print(graphs[1].info())"
205 |    ]
206 |   },
207 |   {
208 |    "cell_type": "code",
209 |    "execution_count": null,
210 |    "metadata": {},
211 |    "outputs": [],
212 |    "source": [
213 |     "summary = pd.DataFrame(\n",
214 |     "    [(g.number_of_nodes(), g.number_of_edges()) for g in graphs],\n",
215 |     "    columns=[\"nodes\", \"edges\"],\n",
216 |     ")\n",
217 |     "summary.describe().round(1)"
218 |    ]
219 |   },
220 |   {
221 |    "cell_type": "code",
222 |    "execution_count": null,
223 |    "metadata": {},
224 |    "outputs": [],
225 |    "source": [
226 |     "\n",
227 |     "################### EMCI Vs LMCI  #############################\n",
228 |     "graph_labels = []\n",
229 |     "rem_ad = []\n",
230 |     "exp1 = []\n",
231 |     "for c,i in enumerate(adni90_labels):\n",
232 |     "    if i == 'EMCI':\n",
233 |     "        graph_labels.append(1)\n",
234 |     "        exp1.append(graphs[c])\n",
235 |     "    elif i == 'LMCI':\n",
236 |     "        graph_labels.append(-1)\n",
237 |     "        exp1.append(graphs[c])\n",
238 |     "    else:\n",
239 |     "        rem_ad.append(c)\n",
240 |     "   \n",
241 |     "\n",
242 |     "################## AD Vs NC ################################\n",
243 |     "# graph_labels = []\n",
244 |     "# rem_ad = []\n",
245 |     "# exp1 = []\n",
246 |     "# for c,i in enumerate(all_sub_label):\n",
247 |     "#     if i == 'AD':\n",
248 |     "#         graph_labels.append(1)\n",
249 |     "#         exp1.append(graphs[c])\n",
250 |     "#     elif i == 'CN':\n",
251 |     "#         graph_labels.append(-1)\n",
252 |     "#         exp1.append(graphs[c])\n",
253 |     "#     else:\n",
254 |     "#         rem_ad.append(c)\n",
255 |     "\n",
256 |     "\n",
257 |     "################  AD Vs MCI ###############################\n",
258 |     "# graph_labels = []\n",
259 |     "# rem_ad = []\n",
260 |     "# exp1 = []\n",
261 |     "# for c,i in enumerate(all_sub_label):\n",
262 |     "#     if i == 'EMCI' or i == 'LMCI' or i == 'MCI':\n",
263 |     "#         graph_labels.append(1)\n",
264 |     "#         exp1.append(graphs[c])\n",
265 |     "#     elif i == 'AD':\n",
266 |     "#         graph_labels.append(-1)\n",
267 |     "#         exp1.append(graphs[c])\n",
268 |     "#     else:\n",
269 |     "#         rem_ad.append(c)\n",
270 |     "   \n",
271 |     "   \n",
272 |     "\n",
273 |     "\n",
274 |     "########### MCI Vs NC ##########################\n",
275 |     "# graph_labels = []\n",
276 |     "# rem_ad = []\n",
277 |     "# exp1 = []\n",
278 |     "# for c,i in enumerate(all_sub_label):\n",
279 |     "#     if i == 'EMCI' or i == 'LMCI' or i == 'MCI':\n",
280 |     "#         graph_labels.append(1)\n",
281 |     "#         exp1.append(graphs[c])\n",
282 |     "#     elif i == 'CN':\n",
283 |     "#         graph_labels.append(-1)\n",
284 |     "#         exp1.append(graphs[c])\n",
285 |     "#     else:\n",
286 |     "#         rem_ad.append(c)\n",
287 |     "    "
288 |    ]
289 |   },
290 |   {
291 |    "cell_type": "code",
292 |    "execution_count": null,
293 |    "metadata": {},
294 |    "outputs": [],
295 |    "source": [
296 |     "print(len(exp1), len (graph_labels))"
297 |    ]
298 |   },
299 |   {
300 |    "cell_type": "code",
301 |    "execution_count": null,
302 |    "metadata": {},
303 |    "outputs": [],
304 |    "source": [
305 |     "pd.Series(graph_labels).value_counts().to_frame()"
306 |    ]
307 |   },
308 |   {
309 |    "cell_type": "code",
310 |    "execution_count": null,
311 |    "metadata": {},
312 |    "outputs": [],
313 |    "source": [
314 |     "graph_labels = pd.get_dummies(graph_labels, drop_first=True)\n",
315 |     "# print(graph_labels)"
316 |    ]
317 |   },
318 |   {
319 |    "cell_type": "code",
320 |    "execution_count": null,
321 |    "metadata": {},
322 |    "outputs": [],
323 |    "source": [
324 |     "\n",
325 |     "##############################################################################################"
326 |    ]
327 |   },
328 |   {
329 |    "cell_type": "code",
330 |    "execution_count": null,
331 |    "metadata": {},
332 |    "outputs": [],
333 |    "source": [
334 |     "    import pandas as pd\n",
335 |     "    import numpy as np\n",
336 |     "\n",
337 |     "    import stellargraph as sg\n",
338 |     "    from stellargraph.mapper import PaddedGraphGenerator\n",
339 |     "    from stellargraph.layer import GCNSupervisedGraphClassification\n",
340 |     "    from stellargraph import StellarGraph\n",
341 |     "\n",
342 |     "    from stellargraph import datasets\n",
343 |     "    import sklearn\n",
344 |     "    from sklearn import model_selection\n",
345 |     "    from sklearn.metrics import balanced_accuracy_score\n",
346 |     "    from IPython.display import display, HTML\n",
347 |     "\n",
348 |     "    from tensorflow.keras import Model\n",
349 |     "    from tensorflow.keras.optimizers import Adam\n",
350 |     "    from tensorflow.keras.layers import Dense\n",
351 |     "    from tensorflow.keras.losses import binary_crossentropy\n",
352 |     "    from tensorflow.keras.callbacks import EarlyStopping\n",
353 |     "    import tensorflow.keras.metrics\n",
354 |     "    import tensorflow as tf\n",
355 |     "    import matplotlib.pyplot as plt"
356 |    ]
357 |   },
358 |   {
359 |    "cell_type": "code",
360 |    "execution_count": null,
361 |    "metadata": {},
362 |    "outputs": [],
363 |    "source": [
364 |     "generator = PaddedGraphGenerator(graphs=exp1)"
365 |    ]
366 |   },
367 |   {
368 |    "cell_type": "code",
369 |    "execution_count": null,
370 |    "metadata": {},
371 |    "outputs": [],
372 |    "source": [
373 |     "def create_graph_classification_model(generator):\n",
374 |     "    gc_model = GCNSupervisedGraphClassification(\n",
375 |     "        layer_sizes=[128, 128, 128,128],\n",
376 |     "        activations=[\"relu\", \"relu\",\"relu\", \"relu\"],\n",
377 |     "        generator=generator,\n",
378 |     "        dropout=0.5,\n",
379 |     "    )\n",
380 |     "    x_inp, x_out = gc_model.in_out_tensors()\n",
381 |     "    predictions = Dense(units=256, activation=\"relu\")(x_out)\n",
382 |     "    predictions = Dense(units=128, activation=\"relu\")(predictions)\n",
383 |     "    \n",
384 |     "    predictions = Dense(units=64, activation=\"relu\")(predictions)\n",
385 |     "    \n",
386 |     "    predictions = Dense(units=32, activation=\"relu\")(predictions)\n",
387 |     "    \n",
388 |     "    predictions = Dense(units=16, activation=\"relu\")(predictions)\n",
389 |     "    predictions = Dense(units=1, activation=\"sigmoid\")(predictions)\n",
390 |     "\n",
391 |     "    # Let's create the Keras model and prepare it for training\n",
392 |     "    model = Model(inputs=x_inp, outputs=predictions)\n",
393 |     "#     model.compile(optimizer=Adam(0.005), loss=binary_crossentropy, metrics=[\"acc\"]) \n",
394 |     "    model.compile(optimizer=Adam(0.005), loss=binary_crossentropy, metrics=[\"acc\",tf.keras.metrics.AUC()])\n",
395 |     "\n",
396 |     "    return model"
397 |    ]
398 |   },
399 |   {
400 |    "cell_type": "code",
401 |    "execution_count": null,
402 |    "metadata": {},
403 |    "outputs": [],
404 |    "source": [
405 |     "epochs = 200  # maximum number of training epochs\n",
406 |     "folds = 32 # the number of folds for k-fold cross validation\n",
407 |     "n_repeats = 5  # the number of repeats for repeated k-fold cross validation"
408 |    ]
409 |   },
410 |   {
411 |    "cell_type": "code",
412 |    "execution_count": null,
413 |    "metadata": {},
414 |    "outputs": [],
415 |    "source": [
416 |     "es = EarlyStopping(\n",
417 |     "    monitor=\"val_loss\", min_delta=0, patience=25, restore_best_weights=True\n",
418 |     ")"
419 |    ]
420 |   },
421 |   {
422 |    "cell_type": "code",
423 |    "execution_count": null,
424 |    "metadata": {},
425 |    "outputs": [],
426 |    "source": [
427 |     "def train_fold(model, train_gen, test_gen, es, epochs):\n",
428 |     "    history = model.fit(\n",
429 |     "        train_gen, epochs=epochs, validation_data=test_gen, verbose=0, callbacks=[es],\n",
430 |     "    )\n",
431 |     "    # calculate performance on the test data and return along with history\n",
432 |     "    \n",
433 |     "    test_metrics = model.evaluate(test_gen, verbose=0)\n",
434 |     "#     print(model.metrics_names)\n",
435 |     "    test_acc = test_metrics[model.metrics_names.index(\"acc\")]\n",
436 |     "#     test_bacc = test_metrics[3]\n",
437 |     "    test_auc = test_metrics[2]\n",
438 |     "    \n",
439 |     "\n",
440 |     "    return history, test_acc, test_auc"
441 |    ]
442 |   },
443 |   {
444 |    "cell_type": "code",
445 |    "execution_count": null,
446 |    "metadata": {},
447 |    "outputs": [],
448 |    "source": [
449 |     "def get_generators(train_index, test_index, graph_labels, batch_size):\n",
450 |     "    train_gen = generator.flow(\n",
451 |     "        train_index, targets=graph_labels.iloc[train_index].values, batch_size=batch_size\n",
452 |     "    )\n",
453 |     "    test_gen = generator.flow(\n",
454 |     "        test_index, targets=graph_labels.iloc[test_index].values, batch_size=batch_size\n",
455 |     "    )\n",
456 |     "\n",
457 |     "    return train_gen, test_gen"
458 |    ]
459 |   },
460 |   {
461 |    "cell_type": "code",
462 |    "execution_count": null,
463 |    "metadata": {},
464 |    "outputs": [],
465 |    "source": [
466 |     "test_accs = []\n",
467 |     "# test_raccs = []\n",
468 |     "# test_baccs = []\n",
469 |     "test_aucs = []\n",
470 |     "start = time.time()\n",
471 |     "\n",
472 |     "\n",
473 |     "stratified_folds = model_selection.RepeatedStratifiedKFold(\n",
474 |     "    n_splits=folds, n_repeats=n_repeats\n",
475 |     ").split(graph_labels, graph_labels)\n",
476 |     "\n",
477 |     "for i, (train_index, test_index) in enumerate(stratified_folds):\n",
478 |     "    print(f\"Training and evaluating on fold {i+1} out of {folds * n_repeats}...\")\n",
479 |     "    train_gen, test_gen = get_generators(\n",
480 |     "        train_index, test_index, graph_labels, batch_size=30\n",
481 |     "    )\n",
482 |     "\n",
483 |     "    model = create_graph_classification_model(generator)\n",
484 |     "    history, acc, auc = train_fold(model, train_gen, test_gen, es, epochs)\n",
485 |     "    \n",
486 |     "#     history, acc = train_fold(model, train_gen, test_gen, es, epochs)\n",
487 |     "\n",
488 |     "    test_accs.append(acc)\n",
489 |     "#     test_raccs.append(racc)\n",
490 |     "    test_aucs.append(auc)\n",
491 |     "\n",
492 |     "end = time.time()\n",
493 |     "print(end - start)"
494 |    ]
495 |   },
496 |   {
497 |    "cell_type": "code",
498 |    "execution_count": null,
499 |    "metadata": {},
500 |    "outputs": [],
501 |    "source": [
502 |     "print(\n",
503 |     "    f\"Accuracy over all folds mean: {np.mean(test_accs)*100:.3}% and std: {np.std(test_accs)*100:.2}% and auc: {np.mean(test_aucs)}\"\n",
504 |     ")"
505 |    ]
506 |   },
507 |   {
508 |    "cell_type": "code",
509 |    "execution_count": null,
510 |    "metadata": {},
511 |    "outputs": [],
512 |    "source": [
513 |     "plt.figure(figsize=(8, 6))\n",
514 |     "plt.hist(test_accs)\n",
515 |     "plt.xlabel(\"Accuracy\")\n",
516 |     "plt.ylabel(\"Count\")"
517 |    ]
518 |   }
519 |  ],
520 |  "metadata": {
521 |   "kernelspec": {
522 |    "display_name": "Python 3",
523 |    "language": "python",
524 |    "name": "python3"
525 |   },
526 |   "language_info": {
527 |    "codemirror_mode": {
528 |     "name": "ipython",
529 |     "version": 3
530 |    },
531 |    "file_extension": ".py",
532 |    "mimetype": "text/x-python",
533 |    "name": "python",
534 |    "nbconvert_exporter": "python",
535 |    "pygments_lexer": "ipython3",
536 |    "version": "3.7.6"
537 |   }
538 |  },
539 |  "nbformat": 4,
540 |  "nbformat_minor": 4
541 | }
542 | 


--------------------------------------------------------------------------------
/HGP-SL/data/ADNI/raw/ADNI_node_labels.txt:
--------------------------------------------------------------------------------
    1 | 0
    2 | 1
    3 | 2
    4 | 3
    5 | 4
    6 | 5
    7 | 6
    8 | 7
    9 | 8
   10 | 9
   11 | 10
   12 | 11
   13 | 12
   14 | 13
   15 | 14
   16 | 15
   17 | 16
   18 | 17
   19 | 18
   20 | 19
   21 | 20
   22 | 21
   23 | 22
   24 | 23
   25 | 24
   26 | 25
   27 | 26
   28 | 27
   29 | 28
   30 | 29
   31 | 30
   32 | 31
   33 | 32
   34 | 33
   35 | 34
   36 | 35
   37 | 36
   38 | 37
   39 | 38
   40 | 39
   41 | 40
   42 | 41
   43 | 42
   44 | 43
   45 | 44
   46 | 45
   47 | 46
   48 | 47
   49 | 48
   50 | 49
   51 | 50
   52 | 51
   53 | 52
   54 | 53
   55 | 54
   56 | 55
   57 | 56
   58 | 57
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