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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class GCN(nn.Module):
def __init__(self, in_ft, out_ft, act, bias=True):
super(GCN, self).__init__()
self.fc = nn.Linear(in_ft, out_ft, bias=False)
self.act = nn.PReLU() if act == 'prelu' else act
if bias:
self.bias = nn.Parameter(torch.FloatTensor(out_ft))
self.bias.data.fill_(0.0)
else:
self.register_parameter('bias', None)
for m in self.modules():
self.weights_init(m)
def weights_init(self, m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight.data)
if m.bias is not None:
m.bias.data.fill_(0.0)
def forward(self, seq, adj, sparse=False):
seq_fts = self.fc(seq)
if sparse:
out = torch.unsqueeze(torch.spmm(adj, torch.squeeze(seq_fts, 0)), 0)
else:
out = torch.bmm(adj, seq_fts)
if self.bias is not None:
out += self.bias
return self.act(out)
class AvgReadout(nn.Module):
def __init__(self):
super(AvgReadout, self).__init__()
def forward(self, seq):
return torch.mean(seq, 1)
class MaxReadout(nn.Module):
def __init__(self):
super(MaxReadout, self).__init__()
def forward(self, seq):
return torch.max(seq, 1).values
class MinReadout(nn.Module):
def __init__(self):
super(MinReadout, self).__init__()
def forward(self, seq):
return torch.min(seq, 1).values
class WSReadout(nn.Module):
def __init__(self):
super(WSReadout, self).__init__()
def forward(self, seq, query):
query = query.permute(0, 2, 1)
sim = torch.matmul(seq, query)
sim = F.softmax(sim, dim=1)
sim = sim.repeat(1, 1, 64)
out = torch.mul(seq, sim)
out = torch.sum(out, 1)
return out
class Discriminator(nn.Module):
def __init__(self, n_h, negsamp_round):
super(Discriminator, self).__init__()
self.f_k = nn.Bilinear(n_h, n_h, 1)
for m in self.modules():
self.weights_init(m)
self.negsamp_round = negsamp_round
def weights_init(self, m):
if isinstance(m, nn.Bilinear):
torch.nn.init.xavier_uniform_(m.weight.data)
if m.bias is not None:
m.bias.data.fill_(0.0)
def forward(self, c, h_pl):
scs = []
# positive
scs.append(self.f_k(h_pl, c))
# negative
c_mi = c
for _ in range(self.negsamp_round):
c_mi = torch.cat((c_mi[-2:-1, :], c_mi[:-1, :]), 0)
scs.append(self.f_k(h_pl, c_mi))
logits = torch.cat(tuple(scs))
return logits
class Model(nn.Module):
def __init__(self, n_in, n_h, activation, negsamp_round, readout):
super(Model, self).__init__()
self.read_mode = readout
self.gcn1 = GCN(n_in, n_h, activation)
self.gcn2 = GCN(n_h, n_h, activation)
self.gcn3 = GCN(n_h, n_h, activation)
self.fc1 = nn.Linear(n_h, int(n_h / 2), bias=False)
self.fc2 = nn.Linear(int(n_h / 2), int(n_h / 4), bias=False)
self.fc3 = nn.Linear(int(n_h / 4), 1, bias=False)
self.fc4 = nn.Linear(n_h, n_h, bias=False)
self.fc6 = nn.Linear(n_h, n_h, bias=False)
self.fc5 = nn.Linear(n_h, n_in, bias=False)
self.act = nn.ReLU()
if readout == 'max':
self.read = MaxReadout()
elif readout == 'min':
self.read = MinReadout()
elif readout == 'avg':
self.read = AvgReadout()
elif readout == 'weighted_sum':
self.read = WSReadout()
self.disc = Discriminator(n_h, negsamp_round)
def forward(self, seq1, adj, sample_abnormal_idx, normal_idx, train_flag, args, sparse=False):
h_1 = self.gcn1(seq1, adj, sparse)
# emb = h_1
emb = self.gcn2(h_1, adj, sparse)
emb_con = None
emb_combine = None
emb_abnormal = emb[:, sample_abnormal_idx, :]
noise = torch.randn(emb_abnormal.size()) * args.var + args.mean
emb_abnormal = emb_abnormal + noise
# emb_abnormal = emb_abnormal + noise.cuda()
if train_flag:
# Add noise into the attribute of sampled abnormal nodes
# degree = torch.sum(raw_adj[0, :, :], 0)[sample_abnormal_idx]
# neigh_adj = raw_adj[0, sample_abnormal_idx, :] / torch.unsqueeze(degree, 1)
neigh_adj = adj[0, sample_abnormal_idx, :]
# emb[0, sample_abnormal_idx, :] =self.act(torch.mm(neigh_adj, emb[0, :, :]))
# emb[0, sample_abnormal_idx, :] = self.fc4(emb[0, sample_abnormal_idx, :])
emb_con = torch.mm(neigh_adj, emb[0, :, :])
emb_con = self.act(self.fc4(emb_con))
# emb_con = self.act(self.fc6(emb_con))
emb_combine = torch.cat((emb[:, normal_idx, :], torch.unsqueeze(emb_con, 0)), 1)
# TODO ablation study add noise on the selected nodes
# std = 0.01
# mean = 0.02
# noise = torch.randn(emb[:, sample_abnormal_idx, :].size()) * std + mean
# emb_combine = torch.cat((emb[:, normal_idx, :], emb[:, sample_abnormal_idx, :] + noise), 1)
# TODO ablation study generate outlier from random noise
# std = 0.01
# mean = 0.02
# emb_con = torch.mm(neigh_adj, emb[0, :, :])
# noise = torch.randn(emb_con.size()) * std + mean
# emb_con = self.act(self.fc4(noise))
# emb_combine = torch.cat((emb[:, normal_idx, :], torch.unsqueeze(emb_con, 0)), 1)
f_1 = self.fc1(emb_combine)
f_1 = self.act(f_1)
f_2 = self.fc2(f_1)
f_2 = self.act(f_2)
f_3 = self.fc3(f_2)
# f_3 = torch.sigmoid(f_3)
emb[:, sample_abnormal_idx, :] = emb_con
else:
f_1 = self.fc1(emb)
f_1 = self.act(f_1)
f_2 = self.fc2(f_1)
f_2 = self.act(f_2)
f_3 = self.fc3(f_2)
# f_3 = torch.sigmoid(f_3)
return emb, emb_combine, f_3, emb_con, emb_abnormal