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node_cls_tasker.py
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node_cls_tasker.py
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import taskers_utils as tu
import torch
import utils as u
class Node_Cls_Tasker():
def __init__(self,args,dataset):
self.data = dataset
self.max_time = dataset.max_time
self.args = args
self.num_classes = 2
self.feats_per_node = dataset.feats_per_node
self.nodes_labels_times = dataset.nodes_labels_times
self.get_node_feats = self.build_get_node_feats(args,dataset)
self.prepare_node_feats = self.build_prepare_node_feats(args,dataset)
self.is_static = False
def build_get_node_feats(self,args,dataset):
if args.use_2_hot_node_feats:
max_deg_out, max_deg_in = tu.get_max_degs(args,dataset,all_window = True)
self.feats_per_node = max_deg_out + max_deg_in
def get_node_feats(i,adj):
return tu.get_2_hot_deg_feats(adj,
max_deg_out,
max_deg_in,
dataset.num_nodes)
elif args.use_1_hot_node_feats:
max_deg,_ = tu.get_max_degs(args,dataset)
self.feats_per_node = max_deg
def get_node_feats(i,adj):
return tu.get_1_hot_deg_feats(adj,
max_deg,
dataset.num_nodes)
else:
def get_node_feats(i,adj):
return dataset.nodes_feats#[i] I'm ignoring the index since the features for Elliptic are static
return get_node_feats
def build_prepare_node_feats(self,args,dataset):
if args.use_2_hot_node_feats or args.use_1_hot_node_feats:
def prepare_node_feats(node_feats):
return u.sparse_prepare_tensor(node_feats,
torch_size= [dataset.num_nodes,
self.feats_per_node])
# elif args.use_1_hot_node_feats:
else:
def prepare_node_feats(node_feats):
return node_feats[0] #I'll have to check this up
return prepare_node_feats
def get_sample(self,idx,test):
hist_adj_list = []
hist_ndFeats_list = []
hist_mask_list = []
hist_adj_list_unnormalized = []
for i in range(idx - self.args.num_hist_steps, idx+1):
#all edgess included from the beginning
cur_adj = tu.get_sp_adj(edges = self.data.edges,
time = i,
weighted = True,
time_window = self.args.adj_mat_time_window) #changed this to keep only a time window
node_mask = tu.get_node_mask(cur_adj, self.data.num_nodes)
node_feats = self.get_node_feats(i,cur_adj)
cur_adj_unnormalized = cur_adj
cur_adj = tu.normalize_adj(adj = cur_adj, num_nodes = self.data.num_nodes)
hist_adj_list.append(cur_adj)
hist_ndFeats_list.append(node_feats)
hist_mask_list.append(node_mask)
hist_adj_list_unnormalized.append(cur_adj_unnormalized)
label_adj = self.get_node_labels(idx)
return {'idx': idx,
'hist_adj_list': hist_adj_list,
'hist_ndFeats_list': hist_ndFeats_list,
'label_sp': label_adj,
'node_mask_list': hist_mask_list,
'hist_adj_list_u': hist_adj_list_unnormalized}
def get_node_labels(self,idx):
# window_nodes = tu.get_sp_adj(edges = self.data.edges,
# time = idx,
# weighted = False,
# time_window = self.args.adj_mat_time_window)
# window_nodes = window_nodes['idx'].unique()
# fraud_times = self.data.nodes_labels_times[window_nodes]
# non_fraudulent = ((fraud_times > idx) + (fraud_times == -1))>0
# non_fraudulent = window_nodes[non_fraudulent]
# fraudulent = (fraud_times <= idx) * (fraud_times > max(idx - self.args.adj_mat_time_window,0))
# fraudulent = window_nodes[fraudulent]
# label_idx = torch.cat([non_fraudulent,fraudulent]).view(-1,1)
# label_vals = torch.cat([torch.zeros(non_fraudulent.size(0)),
# torch.ones(fraudulent.size(0))])
node_labels = self.nodes_labels_times
subset = node_labels[:,2]==idx
label_idx = node_labels[subset,0]
label_vals = node_labels[subset,1]
return {'idx': label_idx,
'vals': label_vals}
class Static_Node_Cls_Tasker(Node_Cls_Tasker):
def __init__(self,args,dataset):
self.data = dataset
self.args = args
self.num_classes = 2
self.adj_matrix = tu.get_static_sp_adj(edges = self.data.edges, weighted = False)
if args.use_2_hot_node_feats:
max_deg_out, max_deg_in = tu.get_max_degs_static(self.data.num_nodes,self.adj_matrix)
self.feats_per_node = max_deg_out + max_deg_in
#print ('feats_per_node',self.feats_per_node ,max_deg_out, max_deg_in)
self.nodes_feats = tu.get_2_hot_deg_feats(self.adj_matrix ,
max_deg_out,
max_deg_in,
dataset.num_nodes)
#print('XXXX self.nodes_feats',self.nodes_feats)
self.nodes_feats = u.sparse_prepare_tensor(self.nodes_feats, torch_size= [self.data.num_nodes,self.feats_per_node], ignore_batch_dim = False)
else:
self.feats_per_node = dataset.feats_per_node
self.nodes_feats = self.data.node_feats
self.adj_matrix = tu.normalize_adj(adj = self.adj_matrix, num_nodes = self.data.num_nodes)
self.is_static = True
def get_sample(self,idx,test):
#print ('self.adj_matrix',self.adj_matrix.size())
idx=int(idx)
#node_feats = self.data.node_feats_dict[idx]
label = self.data.nodes_labels[idx]
return {'idx': idx,
#'node_feats': self.data.node_feats,
#'adj': self.adj_matrix,
'label': label
}
if __name__ == '__main__':
fraud_times = torch.tensor([10,5,3,6,7,-1,-1])
idx = 6
non_fraudulent = ((fraud_times > idx) + (fraud_times == -1))>0
print(non_fraudulent)
exit()