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utils.py
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utils.py
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import argparse
import random
import torch
import dgl
from dgl import DGLGraph
from dgl.data import register_data_args, load_data
from ogb.nodeproppred import DglNodePropPredDataset
import numpy as np
import os
from sklearn.metrics import accuracy_score
from get_args import get_my_args
def node_mask(train_mask,mask_rate):
mask_rate=int(mask_rate*10)
count=0
for i in range(train_mask.shape[0]):
if train_mask[i]==True:
count=count+1
if count<=mask_rate:
train_mask[i]=False
count=count+1
if count==10:
count=0
return train_mask
def my_load_data(args):
if args.dataset=='cora' or args.dataset=='citeseer' or args.dataset=='pubmed' or args.dataset=='reddit':
data = load_data(args)
g = data[0]
features = g.ndata['feat']
labels = g.ndata['label']
train_mask = g.ndata['train_mask']
if args.half:
train_mask=node_mask(train_mask,args.mask_rate)
else:
train_mask = g.ndata['train_mask']
val_mask = g.ndata['val_mask']
test_mask = g.ndata['test_mask']
in_feats = features.shape[1]
n_classes = data.num_classes
n_edges = g.number_of_edges()
elif args.dataset=='Fraud_yelp' or args.dataset=='Fraud_amazon':
if args.dataset=='Fraud_yelp':
data = dgl.data.FraudDataset('yelp')
else:
data = dgl.data.FraudDataset('amazon')
g = data[0]
g=dgl.to_homogeneous(g,ndata=['feature','label','train_mask','val_mask','test_mask'])
features = g.ndata['feature'].to(torch.float32)
labels = g.ndata['label'].view(1,-1)
train_mask = g.ndata['train_mask']
if args.half:
train_mask=node_mask(train_mask,args.mask_rate)
else:
train_mask = g.ndata['train_mask']
val_mask = g.ndata['val_mask']
test_mask = g.ndata['test_mask']
in_feats = features.shape[1]
n_classes = data.num_classes
n_edges = data.graph.number_of_edges()
elif args.dataset=='CoraFull':
data = dgl.data.CoraFullDataset()
g = data[0]
features = g.ndata['feat']
labels = g.ndata['label']
ind=torch.Tensor(random.choices([0,1,2],weights=[0.3,0.1,0.6],k=features.shape[0]))
g.ndata['train_mask']= (ind==0)
g.ndata['val_mask']= (ind==1)
g.ndata['test_mask']= (ind==2)
train_mask = g.ndata['train_mask']
if args.half:
train_mask=node_mask(train_mask,args.mask_rate)
else:
train_mask = g.ndata['train_mask']
val_mask = g.ndata['val_mask']
test_mask = g.ndata['test_mask']
in_feats = features.shape[1]
n_classes = data.num_classes
n_edges = g.number_of_edges()
elif args.dataset=='AmazonCoBuyComputer' or args.dataset=='AmazonCoBuyPhoto' or args.dataset=='CoauthorCS' :
if args.dataset=='AmazonCoBuyComputer':
data = dgl.data.AmazonCoBuyComputerDataset()
elif args.dataset=='AmazonCoBuyPhoto':
data = dgl.data.AmazonCoBuyPhotoDataset()
elif args.dataset=='CoauthorCS':
data = dgl.data.CoauthorCSDataset()
g = data[0]
features = g.ndata['feat'] # get node feature
labels = g.ndata['label'] # get node labels
ind=torch.Tensor(random.choices([0,1,2],weights=[0.1,0.3,0.6],k=features.shape[0]))
g.ndata['train_mask']= (ind==0)
g.ndata['val_mask']= (ind==1)
g.ndata['test_mask']= (ind==2)
train_mask = g.ndata['train_mask']
if args.half:
train_mask=node_mask(train_mask,args.mask_rate)
else:
train_mask = g.ndata['train_mask']
val_mask = g.ndata['val_mask']
test_mask = g.ndata['test_mask']
in_feats = features.shape[1]
n_classes = data.num_classes
n_edges = g.number_of_edges()
elif args.dataset=='ogbn-arxiv':
dataset = DglNodePropPredDataset(name='ogbn-arxiv')
split_idx = dataset.get_idx_split()
g, labels = dataset[0]
g = dgl.add_reverse_edges(g)
features = g.ndata['feat']
g.ndata['label']=labels.view(-1,)
ind=torch.zeros(labels.shape,dtype=bool)
ind[split_idx['train']]=True
g.ndata['train_mask']= ind.view(-1,)
ind=torch.zeros(labels.shape,dtype=bool)
ind[split_idx['valid']]=True
g.ndata['val_mask']= ind.view(-1,)
ind=torch.zeros(labels.shape,dtype=bool)
ind[split_idx['test']]=True
g.ndata['test_mask']= ind.view(-1,)
train_mask = g.ndata['train_mask']
if args.half:
train_mask=node_mask(train_mask,args.mask_rate)
else:
train_mask = g.ndata['train_mask']
val_mask = g.ndata['val_mask']
test_mask = g.ndata['test_mask']
in_feats = features.shape[1]
n_classes = dataset.num_classes
n_edges = g.number_of_edges()
labels=labels.view(-1,)
pass
else:
g=None
features=None
labels=None
train_mask=None
val_mask=None
test_mask=None
in_feats=None
n_classes=None
n_edges=None
return g,features,labels,train_mask,val_mask,test_mask,in_feats,n_classes,n_edges
def evaluate(model, graph, nid, batch_size, device,sample_list):
sampler = dgl.dataloading.MultiLayerNeighborSampler(sample_list)
valid_dataloader = dgl.dataloading.DataLoader(graph, nid.int(), sampler,batch_size=batch_size,shuffle=False,drop_last=False,num_workers=0,device=device)
model.eval()
predictions = []
labels = []
with torch.no_grad():
for input_nodes, output_nodes, mfgs in valid_dataloader:
inputs = mfgs[0].srcdata['feat']
labels.append(mfgs[-1].dstdata['label'].cpu().numpy())
predictions.append(model(mfgs, inputs).argmax(1).cpu().numpy())
predictions = np.concatenate(predictions)
labels = np.concatenate(labels)
accuracy = accuracy_score(labels, predictions)
return accuracy
def constraint(device, prompt):
if isinstance(prompt, list):
sum = 0
for p in prompt:
sum += torch.norm(torch.mm(p, p.T) - torch.eye(p.shape[0]).to(device))
return sum / len(prompt)
else:
return torch.norm(torch.mm(prompt, prompt.T) - torch.eye(prompt.shape[0]).to(device))
def seed_torch(seed=1029):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
def get_init_info(args):
g,features,labels,train_mask,val_mask,test_mask,in_feats,n_classes,n_edges=my_load_data(args)
print("""----Data statistics------'
#Edges %d
#Classes %d
#Train samples %d
#Val samples %d
#Test samples %d""" %
(n_edges, n_classes,
train_mask.int().sum().item(),
val_mask.int().sum().item(),
test_mask.int().sum().item()))
if args.gpu < 0:
device='cpu'
else:
device='cuda:'+str(args.gpu)
torch.cuda.set_device(args.gpu)
features = features.to(device)
labels = labels.to(device)
train_mask = train_mask.to(device)
val_mask = val_mask.to(device)
test_mask = test_mask.to(device)
print("use cuda:", args.gpu)
train_nid = train_mask.nonzero().squeeze()
val_nid = val_mask.nonzero().squeeze()
test_nid = test_mask.nonzero().squeeze()
g = dgl.remove_self_loop(g)
n_edges = g.number_of_edges()
if args.gpu >= 0:
g = g.int().to(args.gpu)
return g,features,labels,in_feats,n_classes,n_edges,train_nid,val_nid,test_nid,device
if __name__ == '__main__':
args = get_my_args()
print(args)
info=my_load_data(args)
g=info[0]
labels=torch.sparse.sum(g.adj(),1).to_dense().int().view(-1,)
print(labels)
li=list(set(labels.numpy()))
for i in range(labels.shape[0]):
labels[i]=li.index(labels[i])
print(set(labels.numpy()))