import graphgallery as gg
from graphgallery import functional as gf
from graphgallery.datasets import NPZDataset
data = NPZDataset('cora',
root="~/GraphData/datasets/",
verbose=False,
transform="standardize")
graph = data.graph
splits = data.split_nodes(random_state=15)
################### Attacker model ############################
target = 1
attacker = gg.attack.targeted.RAND(graph, seed=123).process()
attacker.attack(target)
################### Victim model ############################
# Before attack
trainer = gg.gallery.nodeclas.GCN(seed=123).make_data(graph).build()
his = trainer.fit(splits.train_nodes, splits.val_nodes, verbose=1, epochs=100)
original_predict = trainer.predict(target, transform="softmax")
# After attack
trainer = gg.gallery.nodeclas.GCN(seed=123).make_data(attacker.g).build()
his = trainer.fit(splits.train_nodes, splits.val_nodes, verbose=1, epochs=100)
perturbed_predict = trainer.predict(target, transform="softmax")
################### Results ############################
print("original prediction", original_predict)
print("perturbed prediction", perturbed_predict)
# coding: utf-8
import torch
import graphgallery
import torch_geometric
from graphgallery.datasets import NPZDataset
from graphgallery.gallery import callbacks
print("GraphGallery version: ", graphgallery.__version__)
print("PyTorch version: ", torch.__version__)
print("Torch_Geometric version: ", torch_geometric.__version__)
'''
Load Datasets
Synthetic node classification dataset from PDN: https://github.com/benedekrozemberczki/PDN
'''
data = NPZDataset('pdn', root="~/GraphData/datasets/", verbose=False)
graph = data.graph
splits = data.split_nodes()
device = torch.device('cuda') if torch.cuda.is_available() else torch.device(
'cpu')
graphgallery.set_backend("pyg")
from graphgallery.gallery.nodeclas import PDN
trainer = PDN(device=device, seed=123).setup_graph(graph).build()
cb = callbacks.ModelCheckpoint('model.pth', monitor='val_accuracy')
trainer.fit(splits.train_nodes, splits.val_nodes, verbose=1, callbacks=[cb])
results = trainer.evaluate(splits.test_nodes)
print(f'Test loss {results.loss:.5}, Test accuracy {results.accuracy:.2%}')
