Repository: xxlya/BrainGNN_Pytorch
Branch: main
Commit: 1e337e7a13af
Files: 24
Total size: 112.3 KB
Directory structure:
gitextract_qan2yim0/
├── .idea/
│ ├── .gitignore
│ ├── GNN_biomarker_MEDIA.iml
│ ├── deployment.xml
│ ├── encodings.xml
│ ├── inspectionProfiles/
│ │ └── Project_Default.xml
│ ├── misc.xml
│ ├── modules.xml
│ └── webServers.xml
├── 01-fetch_data.py
├── 02-process_data.py
├── 03-main.py
├── README.md
├── data/
│ └── subject_ID.txt
├── imports/
│ ├── ABIDEDataset.py
│ ├── __inits__.py
│ ├── gdc.py
│ ├── preprocess_data.py
│ ├── read_abide_stats_parall.py
│ └── utils.py
├── net/
│ ├── braingnn.py
│ ├── braingraphconv.py
│ ├── brainmsgpassing.py
│ └── inits.py
└── requirements.txt
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================================================
FILE: 01-fetch_data.py
================================================
# Copyright (c) 2019 Mwiza Kunda
# Copyright (C) 2017 Sarah Parisot <s.parisot@imperial.ac.uk>, , Sofia Ira Ktena <ira.ktena@imperial.ac.uk>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
'''
This script mainly refers to https://github.com/kundaMwiza/fMRI-site-adaptation/blob/master/fetch_data.py
'''
from nilearn import datasets
import argparse
from imports import preprocess_data as Reader
import os
import shutil
import sys
# Input data variables
code_folder = os.getcwd()
root_folder = '/data/'
data_folder = os.path.join(root_folder, 'ABIDE_pcp/cpac/filt_noglobal/')
if not os.path.exists(data_folder):
os.makedirs(data_folder)
shutil.copyfile(os.path.join(root_folder,'subject_ID.txt'), os.path.join(data_folder, 'subject_IDs.txt'))
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def main():
parser = argparse.ArgumentParser(description='Download ABIDE data and compute functional connectivity matrices')
parser.add_argument('--pipeline', default='cpac', type=str,
help='Pipeline to preprocess ABIDE data. Available options are ccs, cpac, dparsf and niak.'
' default: cpac.')
parser.add_argument('--atlas', default='cc200',
help='Brain parcellation atlas. Options: ho, cc200 and cc400, default: cc200.')
parser.add_argument('--download', default=True, type=str2bool,
help='Dowload data or just compute functional connectivity. default: True')
args = parser.parse_args()
print(args)
params = dict()
pipeline = args.pipeline
atlas = args.atlas
download = args.download
# Files to fetch
files = ['rois_' + atlas]
filemapping = {'func_preproc': 'func_preproc.nii.gz',
files[0]: files[0] + '.1D'}
# Download database files
if download == True:
abide = datasets.fetch_abide_pcp(data_dir=root_folder, pipeline=pipeline,
band_pass_filtering=True, global_signal_regression=False, derivatives=files,
quality_checked=False)
subject_IDs = Reader.get_ids() #changed path to data path
subject_IDs = subject_IDs.tolist()
# Create a folder for each subject
for s, fname in zip(subject_IDs, Reader.fetch_filenames(subject_IDs, files[0], atlas)):
subject_folder = os.path.join(data_folder, s)
if not os.path.exists(subject_folder):
os.mkdir(subject_folder)
# Get the base filename for each subject
base = fname.split(files[0])[0]
# Move each subject file to the subject folder
for fl in files:
if not os.path.exists(os.path.join(subject_folder, base + filemapping[fl])):
shutil.move(base + filemapping[fl], subject_folder)
time_series = Reader.get_timeseries(subject_IDs, atlas)
# Compute and save connectivity matrices
Reader.subject_connectivity(time_series, subject_IDs, atlas, 'correlation')
Reader.subject_connectivity(time_series, subject_IDs, atlas, 'partial correlation')
if __name__ == '__main__':
main()
================================================
FILE: 02-process_data.py
================================================
# Copyright (c) 2019 Mwiza Kunda
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import sys
import argparse
import pandas as pd
import numpy as np
from imports import preprocess_data as Reader
import deepdish as dd
import warnings
import os
warnings.filterwarnings("ignore")
root_folder = '/data/'
data_folder = os.path.join(root_folder, 'ABIDE_pcp/cpac/filt_noglobal/')
# Process boolean command line arguments
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def main():
parser = argparse.ArgumentParser(description='Classification of the ABIDE dataset using a Ridge classifier. '
'MIDA is used to minimize the distribution mismatch between ABIDE sites')
parser.add_argument('--atlas', default='cc200',
help='Atlas for network construction (node definition) options: ho, cc200, cc400, default: cc200.')
parser.add_argument('--seed', default=123, type=int, help='Seed for random initialisation. default: 1234.')
parser.add_argument('--nclass', default=2, type=int, help='Number of classes. default:2')
args = parser.parse_args()
print('Arguments: \n', args)
params = dict()
params['seed'] = args.seed # seed for random initialisation
# Algorithm choice
params['atlas'] = args.atlas # Atlas for network construction
atlas = args.atlas # Atlas for network construction (node definition)
# Get subject IDs and class labels
subject_IDs = Reader.get_ids()
labels = Reader.get_subject_score(subject_IDs, score='DX_GROUP')
# Number of subjects and classes for binary classification
num_classes = args.nclass
num_subjects = len(subject_IDs)
params['n_subjects'] = num_subjects
# Initialise variables for class labels and acquisition sites
# 1 is autism, 2 is control
y_data = np.zeros([num_subjects, num_classes]) # n x 2
y = np.zeros([num_subjects, 1]) # n x 1
# Get class labels for all subjects
for i in range(num_subjects):
y_data[i, int(labels[subject_IDs[i]]) - 1] = 1
y[i] = int(labels[subject_IDs[i]])
# Compute feature vectors (vectorised connectivity networks)
fea_corr = Reader.get_networks(subject_IDs, iter_no='', kind='correlation', atlas_name=atlas) #(1035, 200, 200)
fea_pcorr = Reader.get_networks(subject_IDs, iter_no='', kind='partial correlation', atlas_name=atlas) #(1035, 200, 200)
if not os.path.exists(os.path.join(data_folder,'raw')):
os.makedirs(os.path.join(data_folder,'raw'))
for i, subject in enumerate(subject_IDs):
dd.io.save(os.path.join(data_folder,'raw',subject+'.h5'),{'corr':fea_corr[i],'pcorr':fea_pcorr[i],'label':y[i]%2})
if __name__ == '__main__':
main()
================================================
FILE: 03-main.py
================================================
import os
import numpy as np
import argparse
import time
import copy
import torch
import torch.nn.functional as F
from torch.optim import lr_scheduler
from tensorboardX import SummaryWriter
from imports.ABIDEDataset import ABIDEDataset
from torch_geometric.data import DataLoader
from net.braingnn import Network
from imports.utils import train_val_test_split
from sklearn.metrics import classification_report, confusion_matrix
torch.manual_seed(123)
EPS = 1e-10
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
parser = argparse.ArgumentParser()
parser.add_argument('--epoch', type=int, default=0, help='starting epoch')
parser.add_argument('--n_epochs', type=int, default=100, help='number of epochs of training')
parser.add_argument('--batchSize', type=int, default=100, help='size of the batches')
parser.add_argument('--dataroot', type=str, default='/home/azureuser/projects/BrainGNN/data/ABIDE_pcp/cpac/filt_noglobal', help='root directory of the dataset')
parser.add_argument('--fold', type=int, default=0, help='training which fold')
parser.add_argument('--lr', type = float, default=0.01, help='learning rate')
parser.add_argument('--stepsize', type=int, default=20, help='scheduler step size')
parser.add_argument('--gamma', type=float, default=0.5, help='scheduler shrinking rate')
parser.add_argument('--weightdecay', type=float, default=5e-3, help='regularization')
parser.add_argument('--lamb0', type=float, default=1, help='classification loss weight')
parser.add_argument('--lamb1', type=float, default=0, help='s1 unit regularization')
parser.add_argument('--lamb2', type=float, default=0, help='s2 unit regularization')
parser.add_argument('--lamb3', type=float, default=0.1, help='s1 entropy regularization')
parser.add_argument('--lamb4', type=float, default=0.1, help='s2 entropy regularization')
parser.add_argument('--lamb5', type=float, default=0.1, help='s1 consistence regularization')
parser.add_argument('--layer', type=int, default=2, help='number of GNN layers')
parser.add_argument('--ratio', type=float, default=0.5, help='pooling ratio')
parser.add_argument('--indim', type=int, default=200, help='feature dim')
parser.add_argument('--nroi', type=int, default=200, help='num of ROIs')
parser.add_argument('--nclass', type=int, default=2, help='num of classes')
parser.add_argument('--load_model', type=bool, default=False)
parser.add_argument('--save_model', type=bool, default=True)
parser.add_argument('--optim', type=str, default='Adam', help='optimization method: SGD, Adam')
parser.add_argument('--save_path', type=str, default='./model/', help='path to save model')
opt = parser.parse_args()
if not os.path.exists(opt.save_path):
os.makedirs(opt.save_path)
#################### Parameter Initialization #######################
path = opt.dataroot
name = 'ABIDE'
save_model = opt.save_model
load_model = opt.load_model
opt_method = opt.optim
num_epoch = opt.n_epochs
fold = opt.fold
writer = SummaryWriter(os.path.join('./log',str(fold)))
################## Define Dataloader ##################################
dataset = ABIDEDataset(path,name)
dataset.data.y = dataset.data.y.squeeze()
dataset.data.x[dataset.data.x == float('inf')] = 0
tr_index,val_index,te_index = train_val_test_split(fold=fold)
train_dataset = dataset[tr_index]
val_dataset = dataset[val_index]
test_dataset = dataset[te_index]
train_loader = DataLoader(train_dataset,batch_size=opt.batchSize, shuffle= True)
val_loader = DataLoader(val_dataset, batch_size=opt.batchSize, shuffle=False)
test_loader = DataLoader(test_dataset, batch_size=opt.batchSize, shuffle=False)
############### Define Graph Deep Learning Network ##########################
model = Network(opt.indim,opt.ratio,opt.nclass).to(device)
print(model)
if opt_method == 'Adam':
optimizer = torch.optim.Adam(model.parameters(), lr= opt.lr, weight_decay=opt.weightdecay)
elif opt_method == 'SGD':
optimizer = torch.optim.SGD(model.parameters(), lr =opt.lr, momentum = 0.9, weight_decay=opt.weightdecay, nesterov = True)
scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.stepsize, gamma=opt.gamma)
############################### Define Other Loss Functions ########################################
def topk_loss(s,ratio):
if ratio > 0.5:
ratio = 1-ratio
s = s.sort(dim=1).values
res = -torch.log(s[:,-int(s.size(1)*ratio):]+EPS).mean() -torch.log(1-s[:,:int(s.size(1)*ratio)]+EPS).mean()
return res
def consist_loss(s):
if len(s) == 0:
return 0
s = torch.sigmoid(s)
W = torch.ones(s.shape[0],s.shape[0])
D = torch.eye(s.shape[0])*torch.sum(W,dim=1)
L = D-W
L = L.to(device)
res = torch.trace(torch.transpose(s,0,1) @ L @ s)/(s.shape[0]*s.shape[0])
return res
###################### Network Training Function#####################################
def train(epoch):
print('train...........')
scheduler.step()
for param_group in optimizer.param_groups:
print("LR", param_group['lr'])
model.train()
s1_list = []
s2_list = []
loss_all = 0
step = 0
for data in train_loader:
data = data.to(device)
optimizer.zero_grad()
output, w1, w2, s1, s2 = model(data.x, data.edge_index, data.batch, data.edge_attr, data.pos)
s1_list.append(s1.view(-1).detach().cpu().numpy())
s2_list.append(s2.view(-1).detach().cpu().numpy())
loss_c = F.nll_loss(output, data.y)
loss_p1 = (torch.norm(w1, p=2)-1) ** 2
loss_p2 = (torch.norm(w2, p=2)-1) ** 2
loss_tpk1 = topk_loss(s1,opt.ratio)
loss_tpk2 = topk_loss(s2,opt.ratio)
loss_consist = 0
for c in range(opt.nclass):
loss_consist += consist_loss(s1[data.y == c])
loss = opt.lamb0*loss_c + opt.lamb1 * loss_p1 + opt.lamb2 * loss_p2 \
+ opt.lamb3 * loss_tpk1 + opt.lamb4 *loss_tpk2 + opt.lamb5* loss_consist
writer.add_scalar('train/classification_loss', loss_c, epoch*len(train_loader)+step)
writer.add_scalar('train/unit_loss1', loss_p1, epoch*len(train_loader)+step)
writer.add_scalar('train/unit_loss2', loss_p2, epoch*len(train_loader)+step)
writer.add_scalar('train/TopK_loss1', loss_tpk1, epoch*len(train_loader)+step)
writer.add_scalar('train/TopK_loss2', loss_tpk2, epoch*len(train_loader)+step)
writer.add_scalar('train/GCL_loss', loss_consist, epoch*len(train_loader)+step)
step = step + 1
loss.backward()
loss_all += loss.item() * data.num_graphs
optimizer.step()
s1_arr = np.hstack(s1_list)
s2_arr = np.hstack(s2_list)
return loss_all / len(train_dataset), s1_arr, s2_arr ,w1,w2
###################### Network Testing Function#####################################
def test_acc(loader):
model.eval()
correct = 0
for data in loader:
data = data.to(device)
outputs= model(data.x, data.edge_index, data.batch, data.edge_attr,data.pos)
pred = outputs[0].max(dim=1)[1]
correct += pred.eq(data.y).sum().item()
return correct / len(loader.dataset)
def test_loss(loader,epoch):
print('testing...........')
model.eval()
loss_all = 0
for data in loader:
data = data.to(device)
output, w1, w2, s1, s2= model(data.x, data.edge_index, data.batch, data.edge_attr,data.pos)
loss_c = F.nll_loss(output, data.y)
loss_p1 = (torch.norm(w1, p=2)-1) ** 2
loss_p2 = (torch.norm(w2, p=2)-1) ** 2
loss_tpk1 = topk_loss(s1,opt.ratio)
loss_tpk2 = topk_loss(s2,opt.ratio)
loss_consist = 0
for c in range(opt.nclass):
loss_consist += consist_loss(s1[data.y == c])
loss = opt.lamb0*loss_c + opt.lamb1 * loss_p1 + opt.lamb2 * loss_p2 \
+ opt.lamb3 * loss_tpk1 + opt.lamb4 *loss_tpk2 + opt.lamb5* loss_consist
loss_all += loss.item() * data.num_graphs
return loss_all / len(loader.dataset)
#######################################################################################
############################ Model Training #########################################
#######################################################################################
best_model_wts = copy.deepcopy(model.state_dict())
best_loss = 1e10
for epoch in range(0, num_epoch):
since = time.time()
tr_loss, s1_arr, s2_arr, w1, w2 = train(epoch)
tr_acc = test_acc(train_loader)
val_acc = test_acc(val_loader)
val_loss = test_loss(val_loader,epoch)
time_elapsed = time.time() - since
print('*====**')
print('{:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
print('Epoch: {:03d}, Train Loss: {:.7f}, '
'Train Acc: {:.7f}, Test Loss: {:.7f}, Test Acc: {:.7f}'.format(epoch, tr_loss,
tr_acc, val_loss, val_acc))
writer.add_scalars('Acc',{'train_acc':tr_acc,'val_acc':val_acc}, epoch)
writer.add_scalars('Loss', {'train_loss': tr_loss, 'val_loss': val_loss}, epoch)
writer.add_histogram('Hist/hist_s1', s1_arr, epoch)
writer.add_histogram('Hist/hist_s2', s2_arr, epoch)
if val_loss < best_loss and epoch > 5:
print("saving best model")
best_loss = val_loss
best_model_wts = copy.deepcopy(model.state_dict())
if save_model:
torch.save(best_model_wts, os.path.join(opt.save_path,str(fold)+'.pth'))
#######################################################################################
######################### Testing on testing set ######################################
#######################################################################################
if opt.load_model:
model = Network(opt.indim,opt.ratio,opt.nclass).to(device)
model.load_state_dict(torch.load(os.path.join(opt.save_path,str(fold)+'.pth')))
model.eval()
preds = []
correct = 0
for data in val_loader:
data = data.to(device)
outputs= model(data.x, data.edge_index, data.batch, data.edge_attr,data.pos)
pred = outputs[0].max(1)[1]
preds.append(pred.cpu().detach().numpy())
correct += pred.eq(data.y).sum().item()
preds = np.concatenate(preds,axis=0)
trues = val_dataset.data.y.cpu().detach().numpy()
cm = confusion_matrix(trues,preds)
print("Confusion matrix")
print(classification_report(trues, preds))
else:
model.load_state_dict(best_model_wts)
model.eval()
test_accuracy = test_acc(test_loader)
test_l= test_loss(test_loader,0)
print("===========================")
print("Test Acc: {:.7f}, Test Loss: {:.7f} ".format(test_accuracy, test_l))
print(opt)
================================================
FILE: README.md
================================================
# Graph Neural Network for Brain Network Analysis
A preliminary implementation of BrainGNN. The example presented here is on the public resting-state fMRI ABIDE for the convenience of development. This dataset was different from the ones used in our publication, which are cleaner task-fMRI. Still seeking solutions improve representation learning on the noisy data.
## Usage
### Setup
**pip**
See the `requirements.txt` for environment configuration.
```bash
pip install -r requirements.txt
```
**PYG**
To install pyg library, [please refer to the document](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html)
### Dataset
**ABIDE**
We treat each fMRI as a brain graph. How to download and construct the graphs?
```
python 01-fetch_data.py
python 02-process_data.py
```
### How to run classification?
Training and testing are integrated in file `main.py`. To run
```
python 03-main.py
```
## Citation
If you find the code and dataset useful, please cite our paper.
```latex
@article{li2020braingnn,
title={Braingnn: Interpretable brain graph neural network for fmri analysis},
author={Li, Xiaoxiao and Zhou,Yuan and Dvornek, Nicha and Zhang, Muhan and Gao, Siyuan and Zhuang, Juntang and Scheinost, Dustin and Staib, Lawrence and Ventola, Pamela and Duncan, James},
journal={bioRxiv},
year={2020},
publisher={Cold Spring Harbor Laboratory}
}
```
================================================
FILE: data/subject_ID.txt
================================================
50128
51203
50325
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50573
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50779
51009
50746
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51036
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================================================
FILE: imports/ABIDEDataset.py
================================================
import torch
from torch_geometric.data import InMemoryDataset,Data
from os.path import join, isfile
from os import listdir
import numpy as np
import os.path as osp
from imports.read_abide_stats_parall import read_data
class ABIDEDataset(InMemoryDataset):
def __init__(self, root, name, transform=None, pre_transform=None):
self.root = root
self.name = name
super(ABIDEDataset, self).__init__(root,transform, pre_transform)
self.data, self.slices = torch.load(self.processed_paths[0])
@property
def raw_file_names(self):
data_dir = osp.join(self.root,'raw')
onlyfiles = [f for f in listdir(data_dir) if osp.isfile(osp.join(data_dir, f))]
onlyfiles.sort()
return onlyfiles
@property
def processed_file_names(self):
return 'data.pt'
def download(self):
# Download to `self.raw_dir`.
return
def process(self):
# Read data into huge `Data` list.
self.data, self.slices = read_data(self.raw_dir)
if self.pre_filter is not None:
data_list = [self.get(idx) for idx in range(len(self))]
data_list = [data for data in data_list if self.pre_filter(data)]
self.data, self.slices = self.collate(data_list)
if self.pre_transform is not None:
data_list = [self.get(idx) for idx in range(len(self))]
data_list = [self.pre_transform(data) for data in data_list]
self.data, self.slices = self.collate(data_list)
torch.save((self.data, self.slices), self.processed_paths[0])
def __repr__(self):
return '{}({})'.format(self.name, len(self))
================================================
FILE: imports/__inits__.py
================================================
================================================
FILE: imports/gdc.py
================================================
import torch
import numba
import numpy as np
from scipy.linalg import expm
from torch_geometric.utils import add_self_loops, is_undirected, to_dense_adj
from torch_sparse import coalesce
from torch_scatter import scatter_add
def jit():
def decorator(func):
try:
return numba.jit(cache=True)(func)
except RuntimeError:
return numba.jit(cache=False)(func)
return decorator
class GDC(object):
r"""Processes the graph via Graph Diffusion Convolution (GDC) from the
`"Diffusion Improves Graph Learning" <https://www.kdd.in.tum.de/gdc>`_
paper.
.. note::
The paper offers additional advice on how to choose the
hyperparameters.
For an example of using GCN with GDC, see `examples/gcn.py
<https://github.com/rusty1s/pytorch_geometric/blob/master/examples/
gcn.py>`_.
Args:
self_loop_weight (float, optional): Weight of the added self-loop.
Set to :obj:`None` to add no self-loops. (default: :obj:`1`)
normalization_in (str, optional): Normalization of the transition
matrix on the original (input) graph. Possible values:
:obj:`"sym"`, :obj:`"col"`, and :obj:`"row"`.
See :func:`GDC.transition_matrix` for details.
(default: :obj:`"sym"`)
normalization_out (str, optional): Normalization of the transition
matrix on the transformed GDC (output) graph. Possible values:
:obj:`"sym"`, :obj:`"col"`, :obj:`"row"`, and :obj:`None`.
See :func:`GDC.transition_matrix` for details.
(default: :obj:`"col"`)
diffusion_kwargs (dict, optional): Dictionary containing the parameters
for diffusion.
`method` specifies the diffusion method (:obj:`"ppr"`,
:obj:`"heat"` or :obj:`"coeff"`).
Each diffusion method requires different additional parameters.
See :func:`GDC.diffusion_matrix_exact` or
:func:`GDC.diffusion_matrix_approx` for details.
(default: :obj:`dict(method='ppr', alpha=0.15)`)
sparsification_kwargs (dict, optional): Dictionary containing the
parameters for sparsification.
`method` specifies the sparsification method (:obj:`"threshold"` or
:obj:`"topk"`).
Each sparsification method requires different additional
parameters.
See :func:`GDC.sparsify_dense` for details.
(default: :obj:`dict(method='threshold', avg_degree=64)`)
exact (bool, optional): Whether to exactly calculate the diffusion
matrix.
Note that the exact variants are not scalable.
They densify the adjacency matrix and calculate either its inverse
or its matrix exponential.
However, the approximate variants do not support edge weights and
currently only personalized PageRank and sparsification by
threshold are implemented as fast, approximate versions.
(default: :obj:`True`)
:rtype: :class:`torch_geometric.data.Data`
"""
def __init__(self, self_loop_weight=1, normalization_in='sym',
normalization_out='col',
diffusion_kwargs=dict(method='ppr', alpha=0.15),
sparsification_kwargs=dict(method='threshold',
avg_degree=64), exact=True):
self.self_loop_weight = self_loop_weight
self.normalization_in = normalization_in
self.normalization_out = normalization_out
self.diffusion_kwargs = diffusion_kwargs
self.sparsification_kwargs = sparsification_kwargs
self.exact = exact
if self_loop_weight:
assert exact or self_loop_weight == 1
@torch.no_grad()
def __call__(self, data):
N = data.num_nodes
edge_index = data.edge_index
if data.edge_attr is None:
edge_weight = torch.ones(edge_index.size(1),
device=edge_index.device)
else:
edge_weight = data.edge_attr
assert self.exact
assert edge_weight.dim() == 1
if self.self_loop_weight:
edge_index, edge_weight = add_self_loops(
edge_index, edge_weight, fill_value=self.self_loop_weight,
num_nodes=N)
edge_index, edge_weight = coalesce(edge_index, edge_weight, N, N)
if self.exact:
edge_index, edge_weight = self.transition_matrix(
edge_index, edge_weight, N, self.normalization_in)
diff_mat = self.diffusion_matrix_exact(edge_index, edge_weight, N,
**self.diffusion_kwargs)
edge_index, edge_weight = self.sparsify_dense(
diff_mat, **self.sparsification_kwargs)
else:
edge_index, edge_weight = self.diffusion_matrix_approx(
edge_index, edge_weight, N, self.normalization_in,
**self.diffusion_kwargs)
edge_index, edge_weight = self.sparsify_sparse(
edge_index, edge_weight, N, **self.sparsification_kwargs)
edge_index, edge_weight = coalesce(edge_index, edge_weight, N, N)
edge_index, edge_weight = self.transition_matrix(
edge_index, edge_weight, N, self.normalization_out)
data.edge_index = edge_index
data.edge_attr = edge_weight
return data
def transition_matrix(self, edge_index, edge_weight, num_nodes,
normalization):
r"""Calculate the approximate, sparse diffusion on a given sparse
matrix.
Args:
edge_index (LongTensor): The edge indices.
edge_weight (Tensor): One-dimensional edge weights.
num_nodes (int): Number of nodes.
normalization (str): Normalization scheme:
1. :obj:`"sym"`: Symmetric normalization
:math:`\mathbf{T} = \mathbf{D}^{-1/2} \mathbf{A}
\mathbf{D}^{-1/2}`.
2. :obj:`"col"`: Column-wise normalization
:math:`\mathbf{T} = \mathbf{A} \mathbf{D}^{-1}`.
3. :obj:`"row"`: Row-wise normalization
:math:`\mathbf{T} = \mathbf{D}^{-1} \mathbf{A}`.
4. :obj:`None`: No normalization.
:rtype: (:class:`LongTensor`, :class:`Tensor`)
"""
if normalization == 'sym':
row, col = edge_index
deg = scatter_add(edge_weight, col, dim=0, dim_size=num_nodes)
deg_inv_sqrt = deg.pow(-0.5)
deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
edge_weight = deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]
elif normalization == 'col':
_, col = edge_index
deg = scatter_add(edge_weight, col, dim=0, dim_size=num_nodes)
deg_inv = 1. / deg
deg_inv[deg_inv == float('inf')] = 0
edge_weight = edge_weight * deg_inv[col]
elif normalization == 'row':
row, _ = edge_index
deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
deg_inv = 1. / deg
deg_inv[deg_inv == float('inf')] = 0
edge_weight = edge_weight * deg_inv[row]
elif normalization is None:
pass
else:
raise ValueError(
'Transition matrix normalization {} unknown.'.format(
normalization))
return edge_index, edge_weight
def diffusion_matrix_exact(self, edge_index, edge_weight, num_nodes,
method, **kwargs):
r"""Calculate the (dense) diffusion on a given sparse graph.
Note that these exact variants are not scalable. They densify the
adjacency matrix and calculate either its inverse or its matrix
exponential.
Args:
edge_index (LongTensor): The edge indices.
edge_weight (Tensor): One-dimensional edge weights.
num_nodes (int): Number of nodes.
method (str): Diffusion method:
1. :obj:`"ppr"`: Use personalized PageRank as diffusion.
Additionally expects the parameter:
- **alpha** (*float*) - Return probability in PPR.
Commonly lies in :obj:`[0.05, 0.2]`.
2. :obj:`"heat"`: Use heat kernel diffusion.
Additionally expects the parameter:
- **t** (*float*) - Time of diffusion. Commonly lies in
:obj:`[2, 10]`.
3. :obj:`"coeff"`: Freely choose diffusion coefficients.
Additionally expects the parameter:
- **coeffs** (*List[float]*) - List of coefficients
:obj:`theta_k` for each power of the transition matrix
(starting at :obj:`0`).
:rtype: (:class:`Tensor`)
"""
if method == 'ppr':
# α (I_n + (α - 1) A)^-1
edge_weight = (kwargs['alpha'] - 1) * edge_weight
edge_index, edge_weight = add_self_loops(edge_index, edge_weight,
fill_value=1,
num_nodes=num_nodes)
mat = to_dense_adj(edge_index, edge_attr=edge_weight).squeeze()
diff_matrix = kwargs['alpha'] * torch.inverse(mat)
elif method == 'heat':
# exp(t (A - I_n))
edge_index, edge_weight = add_self_loops(edge_index, edge_weight,
fill_value=-1,
num_nodes=num_nodes)
edge_weight = kwargs['t'] * edge_weight
mat = to_dense_adj(edge_index, edge_attr=edge_weight).squeeze()
undirected = is_undirected(edge_index, edge_weight, num_nodes)
diff_matrix = self.__expm__(mat, undirected)
elif method == 'coeff':
adj_matrix = to_dense_adj(edge_index,
edge_attr=edge_weight).squeeze()
mat = torch.eye(num_nodes, device=edge_index.device)
diff_matrix = kwargs['coeffs'][0] * mat
for coeff in kwargs['coeffs'][1:]:
mat = mat @ adj_matrix
diff_matrix += coeff * mat
else:
raise ValueError('Exact GDC diffusion {} unknown.'.format(method))
return diff_matrix
def diffusion_matrix_approx(self, edge_index, edge_weight, num_nodes,
normalization, method, **kwargs):
r"""Calculate the approximate, sparse diffusion on a given sparse
graph.
Args:
edge_index (LongTensor): The edge indices.
edge_weight (Tensor): One-dimensional edge weights.
num_nodes (int): Number of nodes.
normalization (str): Transition matrix normalization scheme
(:obj:`"sym"`, :obj:`"row"`, or :obj:`"col"`).
See :func:`GDC.transition_matrix` for details.
method (str): Diffusion method:
1. :obj:`"ppr"`: Use personalized PageRank as diffusion.
Additionally expects the parameters:
- **alpha** (*float*) - Return probability in PPR.
Commonly lies in :obj:`[0.05, 0.2]`.
- **eps** (*float*) - Threshold for PPR calculation stopping
criterion (:obj:`edge_weight >= eps * out_degree`).
Recommended default: :obj:`1e-4`.
:rtype: (:class:`LongTensor`, :class:`Tensor`)
"""
if method == 'ppr':
if normalization == 'sym':
# Calculate original degrees.
_, col = edge_index
deg = scatter_add(edge_weight, col, dim=0, dim_size=num_nodes)
edge_index_np = edge_index.cpu().numpy()
# Assumes coalesced edge_index.
_, indptr, out_degree = np.unique(edge_index_np[0],
return_index=True,
return_counts=True)
neighbors, neighbor_weights = GDC.__calc_ppr__(
indptr, edge_index_np[1], out_degree, kwargs['alpha'],
kwargs['eps'])
ppr_normalization = 'col' if normalization == 'col' else 'row'
edge_index, edge_weight = self.__neighbors_to_graph__(
neighbors, neighbor_weights, ppr_normalization,
device=edge_index.device)
edge_index = edge_index.to(torch.long)
if normalization == 'sym':
# We can change the normalization from row-normalized to
# symmetric by multiplying the resulting matrix with D^{1/2}
# from the left and D^{-1/2} from the right.
# Since we use the original degrees for this it will be like
# we had used symmetric normalization from the beginning
# (except for errors due to approximation).
row, col = edge_index
deg_inv = deg.sqrt()
deg_inv_sqrt = deg.pow(-0.5)
deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
edge_weight = deg_inv[row] * edge_weight * deg_inv_sqrt[col]
elif normalization in ['col', 'row']:
pass
else:
raise ValueError(
('Transition matrix normalization {} not implemented for '
'non-exact GDC computation.').format(normalization))
elif method == 'heat':
raise NotImplementedError(
('Currently no fast heat kernel is implemented. You are '
'welcome to create one yourself, e.g., based on '
'"Kloster and Gleich: Heat kernel based community detection '
'(KDD 2014)."'))
else:
raise ValueError(
'Approximate GDC diffusion {} unknown.'.format(method))
return edge_index, edge_weight
def sparsify_dense(self, matrix, method, **kwargs):
r"""Sparsifies the given dense matrix.
Args:
matrix (Tensor): Matrix to sparsify.
num_nodes (int): Number of nodes.
method (str): Method of sparsification. Options:
1. :obj:`"threshold"`: Remove all edges with weights smaller
than :obj:`eps`.
Additionally expects one of these parameters:
- **eps** (*float*) - Threshold to bound edges at.
- **avg_degree** (*int*) - If :obj:`eps` is not given,
it can optionally be calculated by calculating the
:obj:`eps` required to achieve a given :obj:`avg_degree`.
2. :obj:`"topk"`: Keep edges with top :obj:`k` edge weights per
node (column).
Additionally expects the following parameters:
- **k** (*int*) - Specifies the number of edges to keep.
- **dim** (*int*) - The axis along which to take the top
:obj:`k`.
:rtype: (:class:`LongTensor`, :class:`Tensor`)
"""
assert matrix.shape[0] == matrix.shape[1]
N = matrix.shape[1]
if method == 'threshold':
if 'eps' not in kwargs.keys():
kwargs['eps'] = self.__calculate_eps__(matrix, N,
kwargs['avg_degree'])
edge_index = torch.nonzero(matrix >= kwargs['eps']).t()
edge_index_flat = edge_index[0] * N + edge_index[1]
edge_weight = matrix.flatten()[edge_index_flat]
elif method == 'topk':
assert kwargs['dim'] in [0, 1]
sort_idx = torch.argsort(matrix, dim=kwargs['dim'],
descending=True)
if kwargs['dim'] == 0:
top_idx = sort_idx[:kwargs['k']]
edge_weight = torch.gather(matrix, dim=kwargs['dim'],
index=top_idx).flatten()
row_idx = torch.arange(0, N, device=matrix.device).repeat(
kwargs['k'])
edge_index = torch.stack([top_idx.flatten(), row_idx], dim=0)
else:
top_idx = sort_idx[:, :kwargs['k']]
edge_weight = torch.gather(matrix, dim=kwargs['dim'],
index=top_idx).flatten()
col_idx = torch.arange(
0, N, device=matrix.device).repeat_interleave(kwargs['k'])
edge_index = torch.stack([col_idx, top_idx.flatten()], dim=0)
else:
raise ValueError('GDC sparsification {} unknown.'.format(method))
return edge_index, edge_weight
def sparsify_sparse(self, edge_index, edge_weight, num_nodes, method,
**kwargs):
r"""Sparsifies a given sparse graph further.
Args:
edge_index (LongTensor): The edge indices.
edge_weight (Tensor): One-dimensional edge weights.
num_nodes (int): Number of nodes.
method (str): Method of sparsification:
1. :obj:`"threshold"`: Remove all edges with weights smaller
than :obj:`eps`.
Additionally expects one of these parameters:
- **eps** (*float*) - Threshold to bound edges at.
- **avg_degree** (*int*) - If :obj:`eps` is not given,
it can optionally be calculated by calculating the
:obj:`eps` required to achieve a given :obj:`avg_degree`.
:rtype: (:class:`LongTensor`, :class:`Tensor`)
"""
if method == 'threshold':
if 'eps' not in kwargs.keys():
kwargs['eps'] = self.__calculate_eps__(edge_weight, num_nodes,
kwargs['avg_degree'])
remaining_edge_idx = torch.nonzero(
edge_weight >= kwargs['eps']).flatten()
edge_index = edge_index[:, remaining_edge_idx]
edge_weight = edge_weight[remaining_edge_idx]
elif method == 'topk':
raise NotImplementedError(
'Sparse topk sparsification not implemented.')
else:
raise ValueError('GDC sparsification {} unknown.'.format(method))
return edge_index, edge_weight
def __expm__(self, matrix, symmetric):
r"""Calculates matrix exponential.
Args:
matrix (Tensor): Matrix to take exponential of.
symmetric (bool): Specifies whether the matrix is symmetric.
:rtype: (:class:`Tensor`)
"""
if symmetric:
e, V = torch.symeig(matrix, eigenvectors=True)
diff_mat = V @ torch.diag(e.exp()) @ V.t()
else:
diff_mat_np = expm(matrix.cpu().numpy())
diff_mat = torch.Tensor(diff_mat_np).to(matrix.device)
return diff_mat
def __calculate_eps__(self, matrix, num_nodes, avg_degree):
r"""Calculates threshold necessary to achieve a given average degree.
Args:
matrix (Tensor): Adjacency matrix or edge weights.
num_nodes (int): Number of nodes.
avg_degree (int): Target average degree.
:rtype: (:class:`float`)
"""
sorted_edges = torch.sort(matrix.flatten(), descending=True).values
if avg_degree * num_nodes > len(sorted_edges):
return -np.inf
return sorted_edges[avg_degree * num_nodes - 1]
def __neighbors_to_graph__(self, neighbors, neighbor_weights,
normalization='row', device='cpu'):
r"""Combine a list of neighbors and neighbor weights to create a sparse
graph.
Args:
neighbors (List[List[int]]): List of neighbors for each node.
neighbor_weights (List[List[float]]): List of weights for the
neighbors of each node.
normalization (str): Normalization of resulting matrix
(options: :obj:`"row"`, :obj:`"col"`). (default: :obj:`"row"`)
device (torch.device): Device to create output tensors on.
(default: :obj:`"cpu"`)
:rtype: (:class:`LongTensor`, :class:`Tensor`)
"""
edge_weight = torch.Tensor(np.concatenate(neighbor_weights)).to(device)
i = np.repeat(np.arange(len(neighbors)),
np.fromiter(map(len, neighbors), dtype=np.int))
j = np.concatenate(neighbors)
if normalization == 'col':
edge_index = torch.Tensor(np.vstack([j, i])).to(device)
N = len(neighbors)
edge_index, edge_weight = coalesce(edge_index, edge_weight, N, N)
elif normalization == 'row':
edge_index = torch.Tensor(np.vstack([i, j])).to(device)
else:
raise ValueError(
f"PPR matrix normalization {normalization} unknown.")
return edge_index, edge_weight
@staticmethod
@jit()
def __calc_ppr__(indptr, indices, out_degree, alpha, eps):
r"""Calculate the personalized PageRank vector for all nodes
using a variant of the Andersen algorithm
(see Andersen et al. :Local Graph Partitioning using PageRank Vectors.)
Args:
indptr (np.ndarray): Index pointer for the sparse matrix
(CSR-format).
indices (np.ndarray): Indices of the sparse matrix entries
(CSR-format).
out_degree (np.ndarray): Out-degree of each node.
alpha (float): Alpha of the PageRank to calculate.
eps (float): Threshold for PPR calculation stopping criterion
(:obj:`edge_weight >= eps * out_degree`).
:rtype: (:class:`List[List[int]]`, :class:`List[List[float]]`)
"""
alpha_eps = alpha * eps
js = []
vals = []
for inode in range(len(out_degree)):
p = {inode: 0.0}
r = {}
r[inode] = alpha
q = [inode]
while len(q) > 0:
unode = q.pop()
res = r[unode] if unode in r else 0
if unode in p:
p[unode] += res
else:
p[unode] = res
r[unode] = 0
for vnode in indices[indptr[unode]:indptr[unode + 1]]:
_val = (1 - alpha) * res / out_degree[unode]
if vnode in r:
r[vnode] += _val
else:
r[vnode] = _val
res_vnode = r[vnode] if vnode in r else 0
if res_vnode >= alpha_eps * out_degree[vnode]:
if vnode not in q:
q.append(vnode)
js.append(list(p.keys()))
vals.append(list(p.values()))
return js, vals
def __repr__(self):
return '{}()'.format(self.__class__.__name__)
================================================
FILE: imports/preprocess_data.py
================================================
# Copyright (c) 2019 Mwiza Kunda
# Copyright (C) 2017 Sarah Parisot <s.parisot@imperial.ac.uk>, Sofia Ira Ktena <ira.ktena@imperial.ac.uk>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implcd ied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import os
import warnings
import glob
import csv
import re
import numpy as np
import scipy.io as sio
import sys
from nilearn import connectome
import pandas as pd
from scipy.spatial import distance
from scipy import signal
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
warnings.filterwarnings("ignore")
# Input data variables
root_folder = '/home/azureuser/projects/BrainGNN/data/'
data_folder = os.path.join(root_folder, 'ABIDE_pcp/cpac/filt_noglobal')
phenotype = os.path.join(root_folder, 'ABIDE_pcp/Phenotypic_V1_0b_preprocessed1.csv')
def fetch_filenames(subject_IDs, file_type, atlas):
"""
subject_list : list of short subject IDs in string format
file_type : must be one of the available file types
filemapping : resulting file name format
returns:
filenames : list of filetypes (same length as subject_list)
"""
filemapping = {'func_preproc': '_func_preproc.nii.gz',
'rois_' + atlas: '_rois_' + atlas + '.1D'}
# The list to be filled
filenames = []
# Fill list with requested file paths
for i in range(len(subject_IDs)):
os.chdir(data_folder)
try:
try:
os.chdir(data_folder)
filenames.append(glob.glob('*' + subject_IDs[i] + filemapping[file_type])[0])
except:
os.chdir(data_folder + '/' + subject_IDs[i])
filenames.append(glob.glob('*' + subject_IDs[i] + filemapping[file_type])[0])
except IndexError:
filenames.append('N/A')
return filenames
# Get timeseries arrays for list of subjects
def get_timeseries(subject_list, atlas_name, silence=False):
"""
subject_list : list of short subject IDs in string format
atlas_name : the atlas based on which the timeseries are generated e.g. aal, cc200
returns:
time_series : list of timeseries arrays, each of shape (timepoints x regions)
"""
timeseries = []
for i in range(len(subject_list)):
subject_folder = os.path.join(data_folder, subject_list[i])
ro_file = [f for f in os.listdir(subject_folder) if f.endswith('_rois_' + atlas_name + '.1D')]
fl = os.path.join(subject_folder, ro_file[0])
if silence != True:
print("Reading timeseries file %s" % fl)
timeseries.append(np.loadtxt(fl, skiprows=0))
return timeseries
# compute connectivity matrices
def subject_connectivity(timeseries, subjects, atlas_name, kind, iter_no='', seed=1234,
n_subjects='', save=True, save_path=data_folder):
"""
timeseries : timeseries table for subject (timepoints x regions)
subjects : subject IDs
atlas_name : name of the parcellation atlas used
kind : the kind of connectivity to be used, e.g. lasso, partial correlation, correlation
iter_no : tangent connectivity iteration number for cross validation evaluation
save : save the connectivity matrix to a file
save_path : specify path to save the matrix if different from subject folder
returns:
connectivity : connectivity matrix (regions x regions)
"""
if kind in ['TPE', 'TE', 'correlation','partial correlation']:
if kind not in ['TPE', 'TE']:
conn_measure = connectome.ConnectivityMeasure(kind=kind)
connectivity = conn_measure.fit_transform(timeseries)
else:
if kind == 'TPE':
conn_measure = connectome.ConnectivityMeasure(kind='correlation')
conn_mat = conn_measure.fit_transform(timeseries)
conn_measure = connectome.ConnectivityMeasure(kind='tangent')
connectivity_fit = conn_measure.fit(conn_mat)
connectivity = connectivity_fit.transform(conn_mat)
else:
conn_measure = connectome.ConnectivityMeasure(kind='tangent')
connectivity_fit = conn_measure.fit(timeseries)
connectivity = connectivity_fit.transform(timeseries)
if save:
if kind not in ['TPE', 'TE']:
for i, subj_id in enumerate(subjects):
subject_file = os.path.join(save_path, subj_id,
subj_id + '_' + atlas_name + '_' + kind.replace(' ', '_') + '.mat')
sio.savemat(subject_file, {'connectivity': connectivity[i]})
return connectivity
else:
for i, subj_id in enumerate(subjects):
subject_file = os.path.join(save_path, subj_id,
subj_id + '_' + atlas_name + '_' + kind.replace(' ', '_') + '_' + str(
iter_no) + '_' + str(seed) + '_' + validation_ext + str(
n_subjects) + '.mat')
sio.savemat(subject_file, {'connectivity': connectivity[i]})
return connectivity_fit
# Get the list of subject IDs
def get_ids(num_subjects=None):
"""
return:
subject_IDs : list of all subject IDs
"""
subject_IDs = np.genfromtxt(os.path.join(data_folder, 'subject_IDs.txt'), dtype=str)
if num_subjects is not None:
subject_IDs = subject_IDs[:num_subjects]
return subject_IDs
# Get phenotype values for a list of subjects
def get_subject_score(subject_list, score):
scores_dict = {}
with open(phenotype) as csv_file:
reader = csv.DictReader(csv_file)
for row in reader:
if row['SUB_ID'] in subject_list:
if score == 'HANDEDNESS_CATEGORY':
if (row[score].strip() == '-9999') or (row[score].strip() == ''):
scores_dict[row['SUB_ID']] = 'R'
elif row[score] == 'Mixed':
scores_dict[row['SUB_ID']] = 'Ambi'
elif row[score] == 'L->R':
scores_dict[row['SUB_ID']] = 'Ambi'
else:
scores_dict[row['SUB_ID']] = row[score]
elif (score == 'FIQ' or score == 'PIQ' or score == 'VIQ'):
if (row[score].strip() == '-9999') or (row[score].strip() == ''):
scores_dict[row['SUB_ID']] = 100
else:
scores_dict[row['SUB_ID']] = float(row[score])
else:
scores_dict[row['SUB_ID']] = row[score]
return scores_dict
# preprocess phenotypes. Categorical -> ordinal representation
def preprocess_phenotypes(pheno_ft, params):
if params['model'] == 'MIDA':
ct = ColumnTransformer([("ordinal", OrdinalEncoder(), [0, 1, 2])], remainder='passthrough')
else:
ct = ColumnTransformer([("ordinal", OrdinalEncoder(), [0, 1, 2, 3])], remainder='passthrough')
pheno_ft = ct.fit_transform(pheno_ft)
pheno_ft = pheno_ft.astype('float32')
return (pheno_ft)
# create phenotype feature vector to concatenate with fmri feature vectors
def phenotype_ft_vector(pheno_ft, num_subjects, params):
gender = pheno_ft[:, 0]
if params['model'] == 'MIDA':
eye = pheno_ft[:, 0]
hand = pheno_ft[:, 2]
age = pheno_ft[:, 3]
fiq = pheno_ft[:, 4]
else:
eye = pheno_ft[:, 2]
hand = pheno_ft[:, 3]
age = pheno_ft[:, 4]
fiq = pheno_ft[:, 5]
phenotype_ft = np.zeros((num_subjects, 4))
phenotype_ft_eye = np.zeros((num_subjects, 2))
phenotype_ft_hand = np.zeros((num_subjects, 3))
for i in range(num_subjects):
phenotype_ft[i, int(gender[i])] = 1
phenotype_ft[i, -2] = age[i]
phenotype_ft[i, -1] = fiq[i]
phenotype_ft_eye[i, int(eye[i])] = 1
phenotype_ft_hand[i, int(hand[i])] = 1
if params['model'] == 'MIDA':
phenotype_ft = np.concatenate([phenotype_ft, phenotype_ft_hand], axis=1)
else:
phenotype_ft = np.concatenate([phenotype_ft, phenotype_ft_hand, phenotype_ft_eye], axis=1)
return phenotype_ft
# Load precomputed fMRI connectivity networks
def get_networks(subject_list, kind, iter_no='', seed=1234, n_subjects='', atlas_name="aal",
variable='connectivity'):
"""
subject_list : list of subject IDs
kind : the kind of connectivity to be used, e.g. lasso, partial correlation, correlation
atlas_name : name of the parcellation atlas used
variable : variable name in the .mat file that has been used to save the precomputed networks
return:
matrix : feature matrix of connectivity networks (num_subjects x network_size)
"""
all_networks = []
for subject in subject_list:
if len(kind.split()) == 2:
kind = '_'.join(kind.split())
fl = os.path.join(data_folder, subject,
subject + "_" + atlas_name + "_" + kind.replace(' ', '_') + ".mat")
matrix = sio.loadmat(fl)[variable]
all_networks.append(matrix)
if kind in ['TE', 'TPE']:
norm_networks = [mat for mat in all_networks]
else:
norm_networks = [np.arctanh(mat) for mat in all_networks]
networks = np.stack(norm_networks)
return networks
================================================
FILE: imports/read_abide_stats_parall.py
================================================
'''
Author: Xiaoxiao Li
Date: 2019/02/24
'''
import os.path as osp
from os import listdir
import os
import glob
import h5py
import torch
import numpy as np
from scipy.io import loadmat
from torch_geometric.data import Data
import networkx as nx
from networkx.convert_matrix import from_numpy_matrix
import multiprocessing
from torch_sparse import coalesce
from torch_geometric.utils import remove_self_loops
from functools import partial
import deepdish as dd
from imports.gdc import GDC
def split(data, batch):
node_slice = torch.cumsum(torch.from_numpy(np.bincount(batch)), 0)
node_slice = torch.cat([torch.tensor([0]), node_slice])
row, _ = data.edge_index
edge_slice = torch.cumsum(torch.from_numpy(np.bincount(batch[row])), 0)
edge_slice = torch.cat([torch.tensor([0]), edge_slice])
# Edge indices should start at zero for every graph.
data.edge_index -= node_slice[batch[row]].unsqueeze(0)
slices = {'edge_index': edge_slice}
if data.x is not None:
slices['x'] = node_slice
if data.edge_attr is not None:
slices['edge_attr'] = edge_slice
if data.y is not None:
if data.y.size(0) == batch.size(0):
slices['y'] = node_slice
else:
slices['y'] = torch.arange(0, batch[-1] + 2, dtype=torch.long)
if data.pos is not None:
slices['pos'] = node_slice
return data, slices
def cat(seq):
seq = [item for item in seq if item is not None]
seq = [item.unsqueeze(-1) if item.dim() == 1 else item for item in seq]
return torch.cat(seq, dim=-1).squeeze() if len(seq) > 0 else None
class NoDaemonProcess(multiprocessing.Process):
@property
def daemon(self):
return False
@daemon.setter
def daemon(self, value):
pass
class NoDaemonContext(type(multiprocessing.get_context())):
Process = NoDaemonProcess
def read_data(data_dir):
onlyfiles = [f for f in listdir(data_dir) if osp.isfile(osp.join(data_dir, f))]
onlyfiles.sort()
batch = []
pseudo = []
y_list = []
edge_att_list, edge_index_list,att_list = [], [], []
# parallar computing
cores = multiprocessing.cpu_count()
pool = multiprocessing.Pool(processes=cores)
#pool = MyPool(processes = cores)
func = partial(read_sigle_data, data_dir)
import timeit
start = timeit.default_timer()
res = pool.map(func, onlyfiles)
pool.close()
pool.join()
stop = timeit.default_timer()
print('Time: ', stop - start)
for j in range(len(res)):
edge_att_list.append(res[j][0])
edge_index_list.append(res[j][1]+j*res[j][4])
att_list.append(res[j][2])
y_list.append(res[j][3])
batch.append([j]*res[j][4])
pseudo.append(np.diag(np.ones(res[j][4])))
edge_att_arr = np.concatenate(edge_att_list)
edge_index_arr = np.concatenate(edge_index_list, axis=1)
att_arr = np.concatenate(att_list, axis=0)
pseudo_arr = np.concatenate(pseudo, axis=0)
y_arr = np.stack(y_list)
edge_att_torch = torch.from_numpy(edge_att_arr.reshape(len(edge_att_arr), 1)).float()
att_torch = torch.from_numpy(att_arr).float()
y_torch = torch.from_numpy(y_arr).long() # classification
batch_torch = torch.from_numpy(np.hstack(batch)).long()
edge_index_torch = torch.from_numpy(edge_index_arr).long()
pseudo_torch = torch.from_numpy(pseudo_arr).float()
data = Data(x=att_torch, edge_index=edge_index_torch, y=y_torch, edge_attr=edge_att_torch, pos = pseudo_torch )
data, slices = split(data, batch_torch)
return data, slices
def read_sigle_data(data_dir,filename,use_gdc =False):
temp = dd.io.load(osp.join(data_dir, filename))
# read edge and edge attribute
pcorr = np.abs(temp['pcorr'][()])
num_nodes = pcorr.shape[0]
G = from_numpy_matrix(pcorr)
A = nx.to_scipy_sparse_matrix(G)
adj = A.tocoo()
edge_att = np.zeros(len(adj.row))
for i in range(len(adj.row)):
edge_att[i] = pcorr[adj.row[i], adj.col[i]]
edge_index = np.stack([adj.row, adj.col])
edge_index, edge_att = remove_self_loops(torch.from_numpy(edge_index), torch.from_numpy(edge_att))
edge_index = edge_index.long()
edge_index, edge_att = coalesce(edge_index, edge_att, num_nodes,
num_nodes)
att = temp['corr'][()]
label = temp['label'][()]
att_torch = torch.from_numpy(att).float()
y_torch = torch.from_numpy(np.array(label)).long() # classification
data = Data(x=att_torch, edge_index=edge_index.long(), y=y_torch, edge_attr=edge_att)
if use_gdc:
'''
Implementation of https://papers.nips.cc/paper/2019/hash/23c894276a2c5a16470e6a31f4618d73-Abstract.html
'''
data.edge_attr = data.edge_attr.squeeze()
gdc = GDC(self_loop_weight=1, normalization_in='sym',
normalization_out='col',
diffusion_kwargs=dict(method='ppr', alpha=0.2),
sparsification_kwargs=dict(method='topk', k=20,
dim=0), exact=True)
data = gdc(data)
return data.edge_attr.data.numpy(),data.edge_index.data.numpy(),data.x.data.numpy(),data.y.data.item(),num_nodes
else:
return edge_att.data.numpy(),edge_index.data.numpy(),att,label,num_nodes
if __name__ == "__main__":
data_dir = '/home/azureuser/projects/BrainGNN/data/ABIDE_pcp/cpac/filt_noglobal/raw'
filename = '50346.h5'
read_sigle_data(data_dir, filename)
================================================
FILE: imports/utils.py
================================================
from scipy import stats
import matplotlib.pyplot as plt
import numpy as np
import torch
from scipy.io import loadmat
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import KFold
def train_val_test_split(kfold = 5, fold = 0):
n_sub = 1035
id = list(range(n_sub))
import random
random.seed(123)
random.shuffle(id)
kf = KFold(n_splits=kfold, random_state=123,shuffle = True)
kf2 = KFold(n_splits=kfold-1, shuffle=True, random_state = 666)
test_index = list()
train_index = list()
val_index = list()
for tr,te in kf.split(np.array(id)):
test_index.append(te)
tr_id, val_id = list(kf2.split(tr))[0]
train_index.append(tr[tr_id])
val_index.append(tr[val_id])
train_id = train_index[fold]
test_id = test_index[fold]
val_id = val_index[fold]
return train_id,val_id,test_id
================================================
FILE: net/braingnn.py
================================================
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch_geometric.nn import TopKPooling
from torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmp
from torch_geometric.utils import (add_self_loops, sort_edge_index,
remove_self_loops)
from torch_sparse import spspmm
from net.braingraphconv import MyNNConv
##########################################################################################################################
class Network(torch.nn.Module):
def __init__(self, indim, ratio, nclass, k=8, R=200):
'''
:param indim: (int) node feature dimension
:param ratio: (float) pooling ratio in (0,1)
:param nclass: (int) number of classes
:param k: (int) number of communities
:param R: (int) number of ROIs
'''
super(Network, self).__init__()
self.indim = indim
self.dim1 = 32
self.dim2 = 32
self.dim3 = 512
self.dim4 = 256
self.dim5 = 8
self.k = k
self.R = R
self.n1 = nn.Sequential(nn.Linear(self.R, self.k, bias=False), nn.ReLU(), nn.Linear(self.k, self.dim1 * self.indim))
self.conv1 = MyNNConv(self.indim, self.dim1, self.n1, normalize=False)
self.pool1 = TopKPooling(self.dim1, ratio=ratio, multiplier=1, nonlinearity=torch.sigmoid)
self.n2 = nn.Sequential(nn.Linear(self.R, self.k, bias=False), nn.ReLU(), nn.Linear(self.k, self.dim2 * self.dim1))
self.conv2 = MyNNConv(self.dim1, self.dim2, self.n2, normalize=False)
self.pool2 = TopKPooling(self.dim2, ratio=ratio, multiplier=1, nonlinearity=torch.sigmoid)
#self.fc1 = torch.nn.Linear((self.dim2) * 2, self.dim2)
self.fc1 = torch.nn.Linear((self.dim1+self.dim2)*2, self.dim2)
self.bn1 = torch.nn.BatchNorm1d(self.dim2)
self.fc2 = torch.nn.Linear(self.dim2, self.dim3)
self.bn2 = torch.nn.BatchNorm1d(self.dim3)
self.fc3 = torch.nn.Linear(self.dim3, nclass)
def forward(self, x, edge_index, batch, edge_attr, pos):
x = self.conv1(x, edge_index, edge_attr, pos)
x, edge_index, edge_attr, batch, perm, score1 = self.pool1(x, edge_index, edge_attr, batch)
pos = pos[perm]
x1 = torch.cat([gmp(x, batch), gap(x, batch)], dim=1)
edge_attr = edge_attr.squeeze()
edge_index, edge_attr = self.augment_adj(edge_index, edge_attr, x.size(0))
x = self.conv2(x, edge_index, edge_attr, pos)
x, edge_index, edge_attr, batch, perm, score2 = self.pool2(x, edge_index,edge_attr, batch)
x2 = torch.cat([gmp(x, batch), gap(x, batch)], dim=1)
x = torch.cat([x1,x2], dim=1)
x = self.bn1(F.relu(self.fc1(x)))
x = F.dropout(x, p=0.5, training=self.training)
x = self.bn2(F.relu(self.fc2(x)))
x= F.dropout(x, p=0.5, training=self.training)
x = F.log_softmax(self.fc3(x), dim=-1)
return x,self.pool1.weight,self.pool2.weight, torch.sigmoid(score1).view(x.size(0),-1), torch.sigmoid(score2).view(x.size(0),-1)
def augment_adj(self, edge_index, edge_weight, num_nodes):
edge_index, edge_weight = add_self_loops(edge_index, edge_weight,
num_nodes=num_nodes)
edge_index, edge_weight = sort_edge_index(edge_index, edge_weight,
num_nodes)
edge_index, edge_weight = spspmm(edge_index, edge_weight, edge_index,
edge_weight, num_nodes, num_nodes,
num_nodes)
edge_index, edge_weight = remove_self_loops(edge_index, edge_weight)
return edge_index, edge_weight
================================================
FILE: net/braingraphconv.py
================================================
import torch
import torch.nn.functional as F
from torch.nn import Parameter
from net.brainmsgpassing import MyMessagePassing
from torch_geometric.utils import add_remaining_self_loops,softmax
from torch_geometric.typing import (OptTensor)
from net.inits import uniform
class MyNNConv(MyMessagePassing):
def __init__(self, in_channels, out_channels, nn, normalize=False, bias=True,
**kwargs):
super(MyNNConv, self).__init__(aggr='mean', **kwargs)
self.in_channels = in_channels
self.out_channels = out_channels
self.normalize = normalize
self.nn = nn
#self.weight = Parameter(torch.Tensor(self.in_channels, out_channels))
if bias:
self.bias = Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
# uniform(self.in_channels, self.weight)
uniform(self.in_channels, self.bias)
def forward(self, x, edge_index, edge_weight=None, pseudo= None, size=None):
""""""
edge_weight = edge_weight.squeeze()
if size is None and torch.is_tensor(x):
edge_index, edge_weight = add_remaining_self_loops(
edge_index, edge_weight, 1, x.size(0))
weight = self.nn(pseudo).view(-1, self.in_channels, self.out_channels)
if torch.is_tensor(x):
x = torch.matmul(x.unsqueeze(1), weight).squeeze(1)
else:
x = (None if x[0] is None else torch.matmul(x[0].unsqueeze(1), weight).squeeze(1),
None if x[1] is None else torch.matmul(x[1].unsqueeze(1), weight).squeeze(1))
# weight = self.nn(pseudo).view(-1, self.out_channels,self.in_channels)
# if torch.is_tensor(x):
# x = torch.matmul(x.unsqueeze(1), weight.permute(0,2,1)).squeeze(1)
# else:
# x = (None if x[0] is None else torch.matmul(x[0].unsqueeze(1), weight).squeeze(1),
# None if x[1] is None else torch.matmul(x[1].unsqueeze(1), weight).squeeze(1))
return self.propagate(edge_index, size=size, x=x,
edge_weight=edge_weight)
def message(self, edge_index_i, size_i, x_j, edge_weight, ptr: OptTensor):
edge_weight = softmax(edge_weight, edge_index_i, ptr, size_i)
return x_j if edge_weight is None else edge_weight.view(-1, 1) * x_j
def update(self, aggr_out):
if self.bias is not None:
aggr_out = aggr_out + self.bias
if self.normalize:
aggr_out = F.normalize(aggr_out, p=2, dim=-1)
return aggr_out
def __repr__(self):
return '{}({}, {})'.format(self.__class__.__name__, self.in_channels,
self.out_channels)
================================================
FILE: net/brainmsgpassing.py
================================================
import sys
import inspect
import torch
# from torch_geometric.utils import scatter_
from torch_scatter import scatter,scatter_add
special_args = [
'edge_index', 'edge_index_i', 'edge_index_j', 'size', 'size_i', 'size_j'
]
__size_error_msg__ = ('All tensors which should get mapped to the same source '
'or target nodes must be of same size in dimension 0.')
is_python2 = sys.version_info[0] < 3
getargspec = inspect.getargspec if is_python2 else inspect.getfullargspec
class MyMessagePassing(torch.nn.Module):
r"""Base class for creating message passing layers
.. math::
\mathbf{x}_i^{\prime} = \gamma_{\mathbf{\Theta}} \left( \mathbf{x}_i,
\square_{j \in \mathcal{N}(i)} \, \phi_{\mathbf{\Theta}}
\left(\mathbf{x}_i, \mathbf{x}_j,\mathbf{e}_{i,j}\right) \right),
where :math:`\square` denotes a differentiable, permutation invariant
function, *e.g.*, sum, mean or max, and :math:`\gamma_{\mathbf{\Theta}}`
and :math:`\phi_{\mathbf{\Theta}}` denote differentiable functions such as
MLPs.
See `here <https://pytorch-geometric.readthedocs.io/en/latest/notes/
create_gnn.html>`__ for the accompanying tutorial.
Args:
aggr (string, optional): The aggregation scheme to use
(:obj:`"add"`, :obj:`"mean"` or :obj:`"max"`).
(default: :obj:`"add"`)
flow (string, optional): The flow direction of message passing
(:obj:`"source_to_target"` or :obj:`"target_to_source"`).
(default: :obj:`"source_to_target"`)
node_dim (int, optional): The axis along which to propagate.
(default: :obj:`0`)
"""
def __init__(self, aggr='add', flow='source_to_target', node_dim=0):
super(MyMessagePassing, self).__init__()
self.aggr = aggr
assert self.aggr in ['add', 'mean', 'max']
self.flow = flow
assert self.flow in ['source_to_target', 'target_to_source']
self.node_dim = node_dim
assert self.node_dim >= 0
self.__message_args__ = getargspec(self.message)[0][1:]
self.__special_args__ = [(i, arg)
for i, arg in enumerate(self.__message_args__)
if arg in special_args]
self.__message_args__ = [
arg for arg in self.__message_args__ if arg not in special_args
]
self.__update_args__ = getargspec(self.update)[0][2:]
def propagate(self, edge_index, size=None, **kwargs):
r"""The initial call to start propagating messages.
Args:
edge_index (Tensor): The indices of a general (sparse) assignment
matrix with shape :obj:`[N, M]` (can be directed or
undirected).
size (list or tuple, optional): The size :obj:`[N, M]` of the
assignment matrix. If set to :obj:`None`, the size is tried to
get automatically inferred and assumed to be symmetric.
(default: :obj:`None`)
**kwargs: Any additional data which is needed to construct messages
and to update node embeddings.
"""
dim = self.node_dim
size = [None, None] if size is None else list(size)
assert len(size) == 2
i, j = (0, 1) if self.flow == 'target_to_source' else (1, 0)
ij = {"_i": i, "_j": j}
message_args = []
for arg in self.__message_args__:
if arg[-2:] in ij.keys():
tmp = kwargs.get(arg[:-2], None)
if tmp is None: # pragma: no cover
message_args.append(tmp)
else:
idx = ij[arg[-2:]]
if isinstance(tmp, tuple) or isinstance(tmp, list):
assert len(tmp) == 2
if tmp[1 - idx] is not None:
if size[1 - idx] is None:
size[1 - idx] = tmp[1 - idx].size(dim)
if size[1 - idx] != tmp[1 - idx].size(dim):
raise ValueError(__size_error_msg__)
tmp = tmp[idx]
if tmp is None:
message_args.append(tmp)
else:
if size[idx] is None:
size[idx] = tmp.size(dim)
if size[idx] != tmp.size(dim):
raise ValueError(__size_error_msg__)
tmp = torch.index_select(tmp, dim, edge_index[idx])
message_args.append(tmp)
else:
message_args.append(kwargs.get(arg, None))
size[0] = size[1] if size[0] is None else size[0]
size[1] = size[0] if size[1] is None else size[1]
kwargs['edge_index'] = edge_index
kwargs['size'] = size
for (idx, arg) in self.__special_args__:
if arg[-2:] in ij.keys():
message_args.insert(idx, kwargs[arg[:-2]][ij[arg[-2:]]])
else:
message_args.insert(idx, kwargs[arg])
update_args = [kwargs[arg] for arg in self.__update_args__]
out = self.message(*message_args)
# out = scatter_(self.aggr, out, edge_index[i], dim, dim_size=size[i])
out = scatter_add(out, edge_index[i], dim, dim_size=size[i])
out = self.update(out, *update_args)
return out
def message(self, x_j): # pragma: no cover
r"""Constructs messages to node :math:`i` in analogy to
:math:`\phi_{\mathbf{\Theta}}` for each edge in
:math:`(j,i) \in \mathcal{E}` if :obj:`flow="source_to_target"` and
:math:`(i,j) \in \mathcal{E}` if :obj:`flow="target_to_source"`.
Can take any argument which was initially passed to :meth:`propagate`.
In addition, tensors passed to :meth:`propagate` can be mapped to the
respective nodes :math:`i` and :math:`j` by appending :obj:`_i` or
:obj:`_j` to the variable name, *.e.g.* :obj:`x_i` and :obj:`x_j`.
"""
return x_j
def update(self, aggr_out): # pragma: no cover
r"""Updates node embeddings in analogy to
:math:`\gamma_{\mathbf{\Theta}}` for each node
:math:`i \in \mathcal{V}`.
Takes in the output of aggregation as first argument and any argument
which was initially passed to :meth:`propagate`."""
return aggr_out
================================================
FILE: net/inits.py
================================================
import math
def uniform(size, tensor):
bound = 1.0 / math.sqrt(size)
if tensor is not None:
tensor.data.uniform_(-bound, bound)
def kaiming_uniform(tensor, fan, a):
if tensor is not None:
bound = math.sqrt(6 / ((1 + a**2) * fan))
tensor.data.uniform_(-bound, bound)
def glorot(tensor):
if tensor is not None:
stdv = math.sqrt(6.0 / (tensor.size(-2) + tensor.size(-1)))
tensor.data.uniform_(-stdv, stdv)
def zeros(tensor):
if tensor is not None:
tensor.data.fill_(0)
def ones(tensor):
if tensor is not None:
tensor.data.fill_(1)
================================================
FILE: requirements.txt
================================================
alabaster @ file:///home/ktietz/src/ci/alabaster_1611921544520/work
anaconda-client==1.7.2
anaconda-project @ file:///tmp/build/80754af9/anaconda-project_1610472525955/work
anyio @ file:///tmp/build/80754af9/anyio_1617783275907/work/dist
appdirs==1.4.4
argh==0.26.2
argon2-cffi @ file:///tmp/build/80754af9/argon2-cffi_1613037097816/work
arrow==0.13.1
ase==3.21.1
asn1crypto @ file:///tmp/build/80754af9/asn1crypto_1596577642040/work
astroid @ file:///tmp/build/80754af9/astroid_1613500854201/work
astropy @ file:///tmp/build/80754af9/astropy_1617745353437/work
async-generator @ file:///home/ktietz/src/ci/async_generator_1611927993394/work
atomicwrites==1.4.0
attrs @ file:///tmp/build/80754af9/attrs_1604765588209/work
autopep8 @ file:///tmp/build/80754af9/autopep8_1615918855173/work
Babel @ file:///tmp/build/80754af9/babel_1607110387436/work
backcall @ file:///home/ktietz/src/ci/backcall_1611930011877/work
backports.shutil-get-terminal-size @ file:///tmp/build/80754af9/backports.shutil_get_terminal_size_1608222128777/work
beautifulsoup4 @ file:///home/linux1/recipes/ci/beautifulsoup4_1610988766420/work
binaryornot @ file:///tmp/build/80754af9/binaryornot_1617751525010/work
bitarray @ file:///tmp/build/80754af9/bitarray_1618431750766/work
bkcharts==0.2
black==19.10b0
bleach @ file:///tmp/build/80754af9/bleach_1612211392645/work
bokeh @ file:///tmp/build/80754af9/bokeh_1617824541184/work
boto==2.49.0
Bottleneck==1.3.2
brotlipy==0.7.0
certifi==2020.12.5
cffi @ file:///tmp/build/80754af9/cffi_1613246945912/work
chardet @ file:///tmp/build/80754af9/chardet_1607706746162/work
click @ file:///home/linux1/recipes/ci/click_1610990599742/work
cloudpickle @ file:///tmp/build/80754af9/cloudpickle_1598884132938/work
clyent==1.2.2
colorama @ file:///tmp/build/80754af9/colorama_1607707115595/work
contextlib2==0.6.0.post1
cookiecutter @ file:///tmp/build/80754af9/cookiecutter_1617748928239/work
cryptography @ file:///tmp/build/80754af9/cryptography_1616769286105/work
cycler==0.10.0
Cython @ file:///tmp/build/80754af9/cython_1618435160151/work
cytoolz==0.11.0
dask @ file:///tmp/build/80754af9/dask-core_1617390489108/work
decorator @ file:///tmp/build/80754af9/decorator_1617916966915/work
deepdish==0.3.6
defusedxml @ file:///tmp/build/80754af9/defusedxml_1615228127516/work
diff-match-patch @ file:///tmp/build/80754af9/diff-match-patch_1594828741838/work
distributed @ file:///tmp/build/80754af9/distributed_1617381497899/work
docutils @ file:///tmp/build/80754af9/docutils_1617624660125/work
entrypoints==0.3
et-xmlfile==1.0.1
fastcache==1.1.0
filelock @ file:///home/linux1/recipes/ci/filelock_1610993975404/work
flake8 @ file:///tmp/build/80754af9/flake8_1615834841867/work
Flask @ file:///home/ktietz/src/ci/flask_1611932660458/work
fsspec @ file:///tmp/build/80754af9/fsspec_1617959894824/work
future==0.18.2
gevent @ file:///tmp/build/80754af9/gevent_1616770671827/work
glob2 @ file:///home/linux1/recipes/ci/glob2_1610991677669/work
gmpy2==2.0.8
googledrivedownloader==0.4
greenlet @ file:///tmp/build/80754af9/greenlet_1611957705398/work
h5py==2.10.0
HeapDict==1.0.1
html5lib @ file:///tmp/build/80754af9/html5lib_1593446221756/work
idna @ file:///home/linux1/recipes/ci/idna_1610986105248/work
imageio @ file:///tmp/build/80754af9/imageio_1617700267927/work
imagesize @ file:///home/ktietz/src/ci/imagesize_1611921604382/work
importlib-metadata @ file:///tmp/build/80754af9/importlib-metadata_1617874469820/work
inflection==0.5.1
iniconfig @ file:///home/linux1/recipes/ci/iniconfig_1610983019677/work
intervaltree @ file:///tmp/build/80754af9/intervaltree_1598376443606/work
ipykernel @ file:///tmp/build/80754af9/ipykernel_1596207638929/work/dist/ipykernel-5.3.4-py3-none-any.whl
ipython @ file:///tmp/build/80754af9/ipython_1617120885885/work
ipython-genutils @ file:///tmp/build/80754af9/ipython_genutils_1606773439826/work
ipywidgets @ file:///tmp/build/80754af9/ipywidgets_1610481889018/work
isodate==0.6.0
isort @ file:///tmp/build/80754af9/isort_1616355431277/work
itsdangerous @ file:///home/ktietz/src/ci/itsdangerous_1611932585308/work
jdcal==1.4.1
jedi @ file:///tmp/build/80754af9/jedi_1606932564285/work
jeepney @ file:///tmp/build/80754af9/jeepney_1606148855031/work
Jinja2 @ file:///tmp/build/80754af9/jinja2_1612213139570/work
jinja2-time @ file:///tmp/build/80754af9/jinja2-time_1617751524098/work
joblib @ file:///tmp/build/80754af9/joblib_1613502643832/work
json5==0.9.5
jsonschema @ file:///tmp/build/80754af9/jsonschema_1602607155483/work
jupyter==1.0.0
jupyter-client @ file:///tmp/build/80754af9/jupyter_client_1616770841739/work
jupyter-console @ file:///tmp/build/80754af9/jupyter_console_1616615302928/work
jupyter-core @ file:///tmp/build/80754af9/jupyter_core_1612213311222/work
jupyter-packaging @ file:///tmp/build/80754af9/jupyter-packaging_1613502826984/work
jupyter-server @ file:///tmp/build/80754af9/jupyter_server_1616083640759/work
jupyterlab @ file:///tmp/build/80754af9/jupyterlab_1619133235951/work
jupyterlab-pygments @ file:///tmp/build/80754af9/jupyterlab_pygments_1601490720602/work
jupyterlab-server @ file:///tmp/build/80754af9/jupyterlab_server_1617134334258/work
jupyterlab-widgets @ file:///tmp/build/80754af9/jupyterlab_widgets_1609884341231/work
keyring @ file:///tmp/build/80754af9/keyring_1614616740399/work
kiwisolver @ file:///tmp/build/80754af9/kiwisolver_1612282420641/work
lazy-object-proxy @ file:///tmp/build/80754af9/lazy-object-proxy_1616526917483/work
libarchive-c @ file:///tmp/build/80754af9/python-libarchive-c_1617780486945/work
llvmlite==0.36.0
locket==0.2.1
lxml @ file:///tmp/build/80754af9/lxml_1616443220220/work
MarkupSafe==1.1.1
matplotlib @ file:///tmp/build/80754af9/matplotlib-suite_1613407855456/work
mccabe==0.6.1
mistune==0.8.4
mkl-fft==1.3.0
mkl-random @ file:///tmp/build/80754af9/mkl_random_1618853849286/work
mkl-service==2.3.0
mock @ file:///tmp/build/80754af9/mock_1607622725907/work
more-itertools @ file:///tmp/build/80754af9/more-itertools_1613676688952/work
mpmath==1.2.1
msgpack @ file:///tmp/build/80754af9/msgpack-python_1612287151062/work
multipledispatch==0.6.0
mypy-extensions==0.4.3
nbclassic @ file:///tmp/build/80754af9/nbclassic_1616085367084/work
nbclient @ file:///tmp/build/80754af9/nbclient_1614364831625/work
nbconvert @ file:///tmp/build/80754af9/nbconvert_1601914830498/work
nbformat @ file:///tmp/build/80754af9/nbformat_1617383369282/work
nest-asyncio @ file:///tmp/build/80754af9/nest-asyncio_1613680548246/work
networkx @ file:///tmp/build/80754af9/networkx_1598376031484/work
nibabel==3.2.1
nilearn==0.7.1
nltk @ file:///tmp/build/80754af9/nltk_1618327084230/work
nose @ file:///tmp/build/80754af9/nose_1606773131901/work
notebook @ file:///tmp/build/80754af9/notebook_1616443462982/work
numba @ file:///tmp/build/80754af9/numba_1616774046117/work
numexpr @ file:///tmp/build/80754af9/numexpr_1618856167419/work
numpy @ file:///tmp/build/80754af9/numpy_and_numpy_base_1618497241363/work
numpydoc @ file:///tmp/build/80754af9/numpydoc_1605117425582/work
olefile==0.46
openpyxl @ file:///tmp/build/80754af9/openpyxl_1615411699337/work
packaging @ file:///tmp/build/80754af9/packaging_1611952188834/work
pandas==1.2.4
pandocfilters @ file:///tmp/build/80754af9/pandocfilters_1605120460739/work
parso==0.7.0
partd @ file:///tmp/build/80754af9/partd_1618000087440/work
path @ file:///tmp/build/80754af9/path_1614022220526/work
pathlib2 @ file:///tmp/build/80754af9/pathlib2_1607024983162/work
pathspec==0.7.0
pathtools==0.1.2
patsy==0.5.1
pep8==1.7.1
pexpect @ file:///tmp/build/80754af9/pexpect_1605563209008/work
pickleshare @ file:///tmp/build/80754af9/pickleshare_1606932040724/work
Pillow @ file:///tmp/build/80754af9/pillow_1617383569452/work
pkginfo==1.7.0
pluggy @ file:///tmp/build/80754af9/pluggy_1615976321666/work
ply==3.11
poyo @ file:///tmp/build/80754af9/poyo_1617751526755/work
prometheus-client @ file:///tmp/build/80754af9/prometheus_client_1618088486455/work
prompt-toolkit @ file:///tmp/build/80754af9/prompt-toolkit_1616415428029/work
protobuf==3.17.0
psutil @ file:///tmp/build/80754af9/psutil_1612298023621/work
ptyprocess @ file:///tmp/build/80754af9/ptyprocess_1609355006118/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl
py @ file:///tmp/build/80754af9/py_1607971587848/work
pycodestyle @ file:///home/ktietz/src/ci_mi/pycodestyle_1612807597675/work
pycosat==0.6.3
pycparser @ file:///tmp/build/80754af9/pycparser_1594388511720/work
pycurl==7.43.0.6
pydocstyle @ file:///tmp/build/80754af9/pydocstyle_1616182067796/work
pyerfa @ file:///tmp/build/80754af9/pyerfa_1619390903914/work
pyflakes @ file:///home/ktietz/src/ci_ipy2/pyflakes_1612551159640/work
Pygments @ file:///tmp/build/80754af9/pygments_1615143339740/work
pylint @ file:///tmp/build/80754af9/pylint_1617135829881/work
pyls-black @ file:///tmp/build/80754af9/pyls-black_1607553132291/work
pyls-spyder @ file:///tmp/build/80754af9/pyls-spyder_1613849700860/work
pyodbc===4.0.0-unsupported
pyOpenSSL @ file:///tmp/build/80754af9/pyopenssl_1608057966937/work
pyparsing @ file:///home/linux1/recipes/ci/pyparsing_1610983426697/work
pyrsistent @ file:///tmp/build/80754af9/pyrsistent_1600141720057/work
PySocks @ file:///tmp/build/80754af9/pysocks_1605305779399/work
pytest==6.2.3
python-dateutil @ file:///home/ktietz/src/ci/python-dateutil_1611928101742/work
python-jsonrpc-server @ file:///tmp/build/80754af9/python-jsonrpc-server_1600278539111/work
python-language-server @ file:///tmp/build/80754af9/python-language-server_1607972495879/work
python-louvain==0.15
python-slugify @ file:///tmp/build/80754af9/python-slugify_1620405669636/work
pytz @ file:///tmp/build/80754af9/pytz_1612215392582/work
PyWavelets @ file:///tmp/build/80754af9/pywavelets_1601658317819/work
pyxdg @ file:///tmp/build/80754af9/pyxdg_1603822279816/work
PyYAML==5.4.1
pyzmq==20.0.0
QDarkStyle @ file:///tmp/build/80754af9/qdarkstyle_1617386714626/work
qstylizer @ file:///tmp/build/80754af9/qstylizer_1617713584600/work/dist/qstylizer-0.1.10-py2.py3-none-any.whl
QtAwesome @ file:///tmp/build/80754af9/qtawesome_1615991616277/work
qtconsole @ file:///tmp/build/80754af9/qtconsole_1616775094278/work
QtPy==1.9.0
rdflib==5.0.0
regex @ file:///tmp/build/80754af9/regex_1617569202463/work
requests @ file:///tmp/build/80754af9/requests_1608241421344/work
rope @ file:///tmp/build/80754af9/rope_1602264064449/work
Rtree @ file:///tmp/build/80754af9/rtree_1618420845272/work
ruamel-yaml-conda @ file:///tmp/build/80754af9/ruamel_yaml_1616016699510/work
scikit-image==0.16.2
scikit-learn @ file:///tmp/build/80754af9/scikit-learn_1614446682169/work
scipy @ file:///tmp/build/80754af9/scipy_1618855647378/work
seaborn @ file:///tmp/build/80754af9/seaborn_1608578541026/work
SecretStorage @ file:///tmp/build/80754af9/secretstorage_1614022784285/work
Send2Trash @ file:///tmp/build/80754af9/send2trash_1607525499227/work
simplegeneric==0.8.1
singledispatch @ file:///tmp/build/80754af9/singledispatch_1614366001199/work
sip==4.19.13
six @ file:///tmp/build/80754af9/six_1605205327372/work
sniffio @ file:///tmp/build/80754af9/sniffio_1614030475067/work
snowballstemmer @ file:///tmp/build/80754af9/snowballstemmer_1611258885636/work
sortedcollections @ file:///tmp/build/80754af9/sortedcollections_1611172717284/work
sortedcontainers @ file:///tmp/build/80754af9/sortedcontainers_1606865132123/work
soupsieve @ file:///tmp/build/80754af9/soupsieve_1616183228191/work
Sphinx @ file:///tmp/build/80754af9/sphinx_1616268783226/work
sphinxcontrib-applehelp @ file:///home/ktietz/src/ci/sphinxcontrib-applehelp_1611920841464/work
sphinxcontrib-devhelp @ file:///home/ktietz/src/ci/sphinxcontrib-devhelp_1611920923094/work
sphinxcontrib-htmlhelp @ file:///home/ktietz/src/ci/sphinxcontrib-htmlhelp_1611920974801/work
sphinxcontrib-jsmath @ file:///home/ktietz/src/ci/sphinxcontrib-jsmath_1611920942228/work
sphinxcontrib-qthelp @ file:///home/ktietz/src/ci/sphinxcontrib-qthelp_1611921055322/work
sphinxcontrib-serializinghtml @ file:///home/ktietz/src/ci/sphinxcontrib-serializinghtml_1611920755253/work
sphinxcontrib-websupport @ file:///tmp/build/80754af9/sphinxcontrib-websupport_1597081412696/work
spyder @ file:///tmp/build/80754af9/spyder_1618327905127/work
spyder-kernels @ file:///tmp/build/80754af9/spyder-kernels_1617396566288/work
SQLAlchemy @ file:///tmp/build/80754af9/sqlalchemy_1618089170652/work
statsmodels @ file:///tmp/build/80754af9/statsmodels_1614023746358/work
sympy @ file:///tmp/build/80754af9/sympy_1618252284338/work
tables==3.6.1
tblib @ file:///tmp/build/80754af9/tblib_1597928476713/work
tensorboardX==2.2
terminado==0.9.4
testpath @ file:///home/ktietz/src/ci/testpath_1611930608132/work
text-unidecode==1.3
textdistance @ file:///tmp/build/80754af9/textdistance_1612461398012/work
threadpoolctl @ file:///tmp/tmp9twdgx9k/threadpoolctl-2.1.0-py3-none-any.whl
three-merge @ file:///tmp/build/80754af9/three-merge_1607553261110/work
tinycss @ file:///tmp/build/80754af9/tinycss_1617713798712/work
toml @ file:///tmp/build/80754af9/toml_1616166611790/work
toolz @ file:///home/linux1/recipes/ci/toolz_1610987900194/work
torch==1.7.0
torch-cluster==1.5.9
torch-geometric==1.7.0
torch-scatter==2.0.6
torch-sparse==0.6.9
torch-spline-conv==1.2.1
torchaudio==0.7.0a0+ac17b64
torchvision==0.8.0
tornado @ file:///tmp/build/80754af9/tornado_1606942300299/work
tqdm @ file:///tmp/build/80754af9/tqdm_1615925068909/work
traitlets @ file:///home/ktietz/src/ci/traitlets_1611929699868/work
tsBNgen==1.0.0
typed-ast @ file:///tmp/build/80754af9/typed-ast_1610484547928/work
typing-extensions @ file:///home/ktietz/src/ci_mi/typing_extensions_1612808209620/work
ujson @ file:///tmp/build/80754af9/ujson_1611259522456/work
unicodecsv==0.14.1
Unidecode @ file:///tmp/build/80754af9/unidecode_1614712377438/work
urllib3 @ file:///tmp/build/80754af9/urllib3_1615837158687/work
watchdog @ file:///tmp/build/80754af9/watchdog_1612471027849/work
wcwidth @ file:///tmp/build/80754af9/wcwidth_1593447189090/work
webencodings==0.5.1
Werkzeug @ file:///home/ktietz/src/ci/werkzeug_1611932622770/work
whichcraft @ file:///tmp/build/80754af9/whichcraft_1617751293875/work
widgetsnbextension==3.5.1
wrapt==1.12.1
wurlitzer @ file:///tmp/build/80754af9/wurlitzer_1617224664226/work
xlrd @ file:///tmp/build/80754af9/xlrd_1608072521494/work
XlsxWriter @ file:///tmp/build/80754af9/xlsxwriter_1617224712951/work
xlwt==1.3.0
yapf @ file:///tmp/build/80754af9/yapf_1615749224965/work
zict==2.0.0
zipp @ file:///tmp/build/80754af9/zipp_1615904174917/work
zope.event==4.5.0
zope.interface @ file:///tmp/build/80754af9/zope.interface_1616357211867/work
gitextract_qan2yim0/ ├── .idea/ │ ├── .gitignore │ ├── GNN_biomarker_MEDIA.iml │ ├── deployment.xml │ ├── encodings.xml │ ├── inspectionProfiles/ │ │ └── Project_Default.xml │ ├── misc.xml │ ├── modules.xml │ └── webServers.xml ├── 01-fetch_data.py ├── 02-process_data.py ├── 03-main.py ├── README.md ├── data/ │ └── subject_ID.txt ├── imports/ │ ├── ABIDEDataset.py │ ├── __inits__.py │ ├── gdc.py │ ├── preprocess_data.py │ ├── read_abide_stats_parall.py │ └── utils.py ├── net/ │ ├── braingnn.py │ ├── braingraphconv.py │ ├── brainmsgpassing.py │ └── inits.py └── requirements.txt
SYMBOL INDEX (68 symbols across 12 files)
FILE: 01-fetch_data.py
function str2bool (line 36) | def str2bool(v):
function main (line 47) | def main():
FILE: 02-process_data.py
function str2bool (line 31) | def str2bool(v):
function main (line 42) | def main():
FILE: 03-main.py
function topk_loss (line 96) | def topk_loss(s,ratio):
function consist_loss (line 104) | def consist_loss(s):
function train (line 116) | def train(epoch):
function test_acc (line 163) | def test_acc(loader):
function test_loss (line 174) | def test_loss(loader,epoch):
FILE: imports/ABIDEDataset.py
class ABIDEDataset (line 10) | class ABIDEDataset(InMemoryDataset):
method __init__ (line 11) | def __init__(self, root, name, transform=None, pre_transform=None):
method raw_file_names (line 18) | def raw_file_names(self):
method processed_file_names (line 24) | def processed_file_names(self):
method download (line 27) | def download(self):
method process (line 31) | def process(self):
method __repr__ (line 47) | def __repr__(self):
FILE: imports/gdc.py
function jit (line 10) | def jit():
class GDC (line 20) | class GDC(object):
method __init__ (line 70) | def __init__(self, self_loop_weight=1, normalization_in='sym',
method __call__ (line 86) | def __call__(self, data):
method transition_matrix (line 127) | def transition_matrix(self, edge_index, edge_weight, num_nodes,
method diffusion_matrix_exact (line 173) | def diffusion_matrix_exact(self, edge_index, edge_weight, num_nodes,
method diffusion_matrix_approx (line 232) | def diffusion_matrix_approx(self, edge_index, edge_weight, num_nodes,
method sparsify_dense (line 305) | def sparsify_dense(self, matrix, method, **kwargs):
method sparsify_sparse (line 363) | def sparsify_sparse(self, edge_index, edge_weight, num_nodes, method,
method __expm__ (line 397) | def __expm__(self, matrix, symmetric):
method __calculate_eps__ (line 412) | def __calculate_eps__(self, matrix, num_nodes, avg_degree):
method __neighbors_to_graph__ (line 425) | def __neighbors_to_graph__(self, neighbors, neighbor_weights,
method __calc_ppr__ (line 456) | def __calc_ppr__(indptr, indices, out_degree, alpha, eps):
method __repr__ (line 503) | def __repr__(self):
FILE: imports/preprocess_data.py
function fetch_filenames (line 44) | def fetch_filenames(subject_IDs, file_type, atlas):
function get_timeseries (line 74) | def get_timeseries(subject_list, atlas_name, silence=False):
function subject_connectivity (line 95) | def subject_connectivity(timeseries, subjects, atlas_name, kind, iter_no...
function get_ids (line 144) | def get_ids(num_subjects=None):
function get_subject_score (line 159) | def get_subject_score(subject_list, score):
function preprocess_phenotypes (line 188) | def preprocess_phenotypes(pheno_ft, params):
function phenotype_ft_vector (line 201) | def phenotype_ft_vector(pheno_ft, num_subjects, params):
function get_networks (line 234) | def get_networks(subject_list, kind, iter_no='', seed=1234, n_subjects='...
FILE: imports/read_abide_stats_parall.py
function split (line 26) | def split(data, batch):
function cat (line 53) | def cat(seq):
class NoDaemonProcess (line 58) | class NoDaemonProcess(multiprocessing.Process):
method daemon (line 60) | def daemon(self):
method daemon (line 64) | def daemon(self, value):
class NoDaemonContext (line 68) | class NoDaemonContext(type(multiprocessing.get_context())):
function read_data (line 72) | def read_data(data_dir):
function read_sigle_data (line 128) | def read_sigle_data(data_dir,filename,use_gdc =False):
FILE: imports/utils.py
function train_val_test_split (line 10) | def train_val_test_split(kfold = 5, fold = 0):
FILE: net/braingnn.py
class Network (line 14) | class Network(torch.nn.Module):
method __init__ (line 15) | def __init__(self, indim, ratio, nclass, k=8, R=200):
method forward (line 52) | def forward(self, x, edge_index, batch, edge_attr, pos):
method augment_adj (line 77) | def augment_adj(self, edge_index, edge_weight, num_nodes):
FILE: net/braingraphconv.py
class MyNNConv (line 12) | class MyNNConv(MyMessagePassing):
method __init__ (line 13) | def __init__(self, in_channels, out_channels, nn, normalize=False, bia...
method reset_parameters (line 30) | def reset_parameters(self):
method forward (line 34) | def forward(self, x, edge_index, edge_weight=None, pseudo= None, size=...
method message (line 58) | def message(self, edge_index_i, size_i, x_j, edge_weight, ptr: OptTens...
method update (line 62) | def update(self, aggr_out):
method __repr__ (line 69) | def __repr__(self):
FILE: net/brainmsgpassing.py
class MyMessagePassing (line 18) | class MyMessagePassing(torch.nn.Module):
method __init__ (line 40) | def __init__(self, aggr='add', flow='source_to_target', node_dim=0):
method propagate (line 61) | def propagate(self, edge_index, size=None, **kwargs):
method message (line 133) | def message(self, x_j): # pragma: no cover
method update (line 146) | def update(self, aggr_out): # pragma: no cover
FILE: net/inits.py
function uniform (line 4) | def uniform(size, tensor):
function kaiming_uniform (line 10) | def kaiming_uniform(tensor, fan, a):
function glorot (line 16) | def glorot(tensor):
function zeros (line 22) | def zeros(tensor):
function ones (line 27) | def ones(tensor):
Condensed preview — 24 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (121K chars).
[
{
"path": ".idea/.gitignore",
"chars": 176,
"preview": "# Default ignored files\n/shelf/\n/workspace.xml\n# Datasource local storage ignored files\n/dataSources/\n/dataSources.local"
},
{
"path": ".idea/GNN_biomarker_MEDIA.iml",
"chars": 421,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<module type=\"PYTHON_MODULE\" version=\"4\">\n <component name=\"NewModuleRootManager"
},
{
"path": ".idea/deployment.xml",
"chars": 870,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n <component name=\"PublishConfigData\" serverName=\"li-gan\">\n"
},
{
"path": ".idea/encodings.xml",
"chars": 135,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n <component name=\"Encoding\" addBOMForNewFiles=\"with NO BOM"
},
{
"path": ".idea/inspectionProfiles/Project_Default.xml",
"chars": 16512,
"preview": "<component name=\"InspectionProjectProfileManager\">\n <profile version=\"1.0\">\n <option name=\"myName\" value=\"Project De"
},
{
"path": ".idea/misc.xml",
"chars": 396,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n <component name=\"ProjectRootManager\" version=\"2\" project-"
},
{
"path": ".idea/modules.xml",
"chars": 290,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n <component name=\"ProjectModuleManager\">\n <modules>\n "
},
{
"path": ".idea/webServers.xml",
"chars": 603,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n <component name=\"WebServers\">\n <option name=\"servers\">"
},
{
"path": "01-fetch_data.py",
"chars": 3951,
"preview": "# Copyright (c) 2019 Mwiza Kunda\n# Copyright (C) 2017 Sarah Parisot <s.parisot@imperial.ac.uk>, , Sofia Ira Ktena <ira.k"
},
{
"path": "02-process_data.py",
"chars": 3562,
"preview": "# Copyright (c) 2019 Mwiza Kunda\n#\n# This program is free software: you can redistribute it and/or modify\n# it under the"
},
{
"path": "03-main.py",
"chars": 10678,
"preview": "import os\nimport numpy as np\nimport argparse\nimport time\nimport copy\n\nimport torch\nimport torch.nn.functional as F\nfrom "
},
{
"path": "README.md",
"chars": 1393,
"preview": "# Graph Neural Network for Brain Network Analysis\n A preliminary implementation of BrainGNN. The example presented here "
},
{
"path": "data/subject_ID.txt",
"chars": 6210,
"preview": "50128\n51203\n50325\n50117\n50573\n50741\n50779\n51009\n50746\n50574\n50110\n50322\n51036\n51204\n50119\n50126\n50314\n51490\n50784\n51464\n"
},
{
"path": "imports/ABIDEDataset.py",
"chars": 1671,
"preview": "import torch\nfrom torch_geometric.data import InMemoryDataset,Data\nfrom os.path import join, isfile\nfrom os import listd"
},
{
"path": "imports/__inits__.py",
"chars": 0,
"preview": ""
},
{
"path": "imports/gdc.py",
"chars": 23308,
"preview": "import torch\nimport numba\nimport numpy as np\nfrom scipy.linalg import expm\nfrom torch_geometric.utils import add_self_lo"
},
{
"path": "imports/preprocess_data.py",
"chars": 10249,
"preview": "# Copyright (c) 2019 Mwiza Kunda\n# Copyright (C) 2017 Sarah Parisot <s.parisot@imperial.ac.uk>, Sofia Ira Ktena <ira.kte"
},
{
"path": "imports/read_abide_stats_parall.py",
"chars": 5517,
"preview": "'''\nAuthor: Xiaoxiao Li\nDate: 2019/02/24\n'''\n\nimport os.path as osp\nfrom os import listdir\nimport os\nimport glob\nimport "
},
{
"path": "imports/utils.py",
"chars": 899,
"preview": "from scipy import stats\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nfrom scipy.io import loadmat\nfro"
},
{
"path": "net/braingnn.py",
"chars": 3771,
"preview": "import torch\nimport torch.nn.functional as F\nimport torch.nn as nn\nfrom torch_geometric.nn import TopKPooling\nfrom torch"
},
{
"path": "net/braingraphconv.py",
"chars": 2809,
"preview": "import torch\nimport torch.nn.functional as F\nfrom torch.nn import Parameter\nfrom net.brainmsgpassing import MyMessagePas"
},
{
"path": "net/brainmsgpassing.py",
"chars": 6467,
"preview": "import sys\nimport inspect\n\nimport torch\n# from torch_geometric.utils import scatter_\nfrom torch_scatter import scatter,s"
},
{
"path": "net/inits.py",
"chars": 617,
"preview": "import math\n\n\ndef uniform(size, tensor):\n bound = 1.0 / math.sqrt(size)\n if tensor is not None:\n tensor.dat"
},
{
"path": "requirements.txt",
"chars": 14531,
"preview": "alabaster @ file:///home/ktietz/src/ci/alabaster_1611921544520/work\nanaconda-client==1.7.2\nanaconda-project @ file:///tm"
}
]
About this extraction
This page contains the full source code of the xxlya/BrainGNN_Pytorch GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 24 files (112.3 KB), approximately 31.7k tokens, and a symbol index with 68 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.