def task_bitcoinalpha(args):
A, X = utils.load_XA(args.dataset, datadir="../Generate_XA_Data/XAL")
L = utils.load_labels(args.dataset, datadir="../Generate_XA_Data/XAL")
num_classes = max(L) + 1
print("NUMBER OF CLASS IS: " + str(num_classes))
input_dim = X.shape[1]
print("Input dimension is: ", input_dim)
model = models.GcnEncoderNode(
input_dim,
args.hidden_dim,
args.output_dim,
num_classes,
args.num_gc_layers,
bn=args.bn,
args=args,
)
train_node_classifier.train(model,
A,
X,
L,
args,
normalize_adjacency=False)
def bitcoin(args):
A, X = utils.load_XA(args.dataset, datadir = "../Generate_XA_Data/XAL")
L = utils.load_labels(args.dataset, datadir = "../Generate_XA_Data/XAL")
num_classes = max(L) + 1
input_dim = X.shape[1]
num_nodes = X.shape[0]
ckpt = utils.load_ckpt(args)
print("input dim: ", input_dim, "; num classes: ", num_classes)
model = models.GcnEncoderNode(
input_dim=input_dim,
hidden_dim=args.hidden_dim,
embedding_dim=args.output_dim,
label_dim=num_classes,
num_layers=args.num_gc_layers,
bn=args.bn,
args=args,
)
model.load_state_dict(ckpt["model_state"])
pred = ckpt["save_data"]["pred"]
explainer = pe.Node_Explainer(model, A, X, pred, args.num_gc_layers)
node_to_explain = [i for [i] in np.argwhere(np.sum(A,axis = 0) > 2)]
explanations = explainer.explain_range(node_to_explain, num_samples = args.num_perturb_samples, top_node = args.top_node)
print(explanations)
savename = utils.gen_filesave(args)
np.save(savename,explanations)
def task_syn(args):
A, X = utils.load_XA(args.dataset, datadir = "../Generate_XA_Data/XAL")
L = utils.load_labels(args.dataset, datadir = "../Generate_XA_Data/XAL")
num_classes = max(L) + 1
input_dim = X.shape[1]
ckpt = utils.load_ckpt(args)
print("input dim: ", input_dim, "; num classes: ", num_classes)
model = models.GcnEncoderNode(
input_dim=input_dim,
hidden_dim=args.hidden_dim,
embedding_dim=args.output_dim,
label_dim=num_classes,
num_layers=args.num_gc_layers,
bn=args.bn,
args=args,
)
model.load_state_dict(ckpt["model_state"])
pred = ckpt["save_data"]["pred"]
explainer = pe.Node_Explainer(model, A, X, pred, args.num_gc_layers)
explanations = {}
if args.explain_node == None:
if args.dataset == 'syn1':
explanations = explainer.explain_range(list(range(300,700)), num_samples = args.num_perturb_samples, top_node = args.top_node)
elif args.dataset == 'syn2':
explanations = explainer.explain_range(list(range(300,700)) + list(range(1000,1400)), num_samples = args.num_perturb_samples, top_node = args.top_node, pred_threshold = 0.1)
elif args.dataset == 'syn3':
explanations = explainer.explain_range(list(range(300,1020)), num_samples = args.num_perturb_samples, top_node = args.top_node,pred_threshold = 0.05)
elif args.dataset == 'syn4':
explanations = explainer.explain_range(list(range(511,871)), num_samples = args.num_perturb_samples, top_node = args.top_node, pred_threshold = 0.1)
elif args.dataset == 'syn5':
explanations = explainer.explain_range(list(range(511,1231)), num_samples = args.num_perturb_samples, top_node = args.top_node, pred_threshold = 0.05)
elif args.dataset == 'syn6':
explanations = explainer.explain_range(list(range(300,700)), num_samples = args.num_perturb_samples, top_node = args.top_node)
else:
explanation = explainer.explain(args.explain_node, num_samples = args.num_perturb_samples, top_node = args.top_node)
print(explanation)
explanations[args.explain_node] = explanation
print(explanations)
savename = utils.gen_filesave(args)
np.save(savename,explanations)
def task_syn(args):
A, X = utils.load_XA(args.dataset, datadir="../Generate_XA_Data/XAL")
L = utils.load_labels(args.dataset, datadir="../Generate_XA_Data/XAL")
num_classes = max(L) + 1
input_dim = X.shape[1]
model = models.GcnEncoderNode(
args.input_dim,
args.hidden_dim,
args.output_dim,
num_classes,
args.num_gc_layers,
bn=args.bn,
args=args,
)
train_node_classifier.train(model,
A,
X,
L,
args,
normalize_adjacency=False)