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For OCGNN models, update hypersphere radius, and filter training data with active_mask #95

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Jan 3, 2024
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For OCGNN models, update hypersphere radius, and filter training data with active_mask #95

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ce15c10
Update hypersphere radius, and filter training data with active_mask
ahmed3amerai Dec 15, 2023
77d754c
Update hypersphere radius, and filter training data with active_mask
ahmed3amerai Dec 15, 2023
2695273
Set default warmup as math.inf
ahmed3amerai Dec 15, 2023
4a36ac8
Updating other models
ahmed3amerai Dec 19, 2023
d061151
Merge branch 'main' into main
ahmed3amerai Dec 21, 2023
ecc2caa
Keep accepted changes and Revert rest
ahmed3amerai Jan 3, 2024
a134943
Keep accepted changes and Revert rest
ahmed3amerai Jan 3, 2024
b36165d
Keep accepted changes and Revert rest
ahmed3amerai Jan 3, 2024
76f3fb9
Keep accepted changes and Revert rest
ahmed3amerai Jan 3, 2024
b1d1d05
Cleaning the pull request
ahmed3amerai Jan 3, 2024
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1 change: 1 addition & 0 deletions docs/examples/intro.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,6 +69,7 @@
# To train the detector with the loaded data, simply feed the
# ``torch_geometric.data.Data`` object into the detector via ``fit``.

# To train the model with train_mask only, use `data.active_mask = data.train_mask`

detector.fit(data)

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34 changes: 23 additions & 11 deletions pygod/detector/adone.py
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Expand Up @@ -189,7 +189,7 @@ def init_model(self, **kwargs):
w5=self.w5,
**kwargs).to(self.device)

def forward_model(self, data):
def forward_model(self, data, is_train=True):
batch_size = data.batch_size
node_idx = data.n_id

Expand All @@ -198,16 +198,28 @@ def forward_model(self, data):
edge_index = data.edge_index.to(self.device)

x_, s_, h_a, h_s, dna, dns, dis_a, dis_s = self.model(x, s, edge_index)
loss, oa, os, oc = self.model.loss_func(x[:batch_size],
x_[:batch_size],
s[:batch_size],
s_[:batch_size],
h_a[:batch_size],
h_s[:batch_size],
dna[:batch_size],
dns[:batch_size],
dis_a[:batch_size],
dis_s[:batch_size])
if 'active_mask' in data.keys():
loss, oa, os, oc = self.model.loss_func(x[:batch_size][data.active_mask, :],
x_[:batch_size][data.active_mask, :],
s[:batch_size][data.active_mask, :],
s_[:batch_size][data.active_mask, :],
h_a[:batch_size][data.active_mask, :],
h_s[:batch_size][data.active_mask, :],
dna[:batch_size][data.active_mask, :],
dns[:batch_size][data.active_mask, :],
dis_a[:batch_size][data.active_mask, :],
dis_s[:batch_size][data.active_mask, :])
else:
loss, oa, os, oc = self.model.loss_func(x[:batch_size],
x_[:batch_size],
s[:batch_size],
s_[:batch_size],
h_a[:batch_size],
h_s[:batch_size],
dna[:batch_size],
dns[:batch_size],
dis_a[:batch_size],
dis_s[:batch_size])

self.attribute_score_[node_idx[:batch_size]] = oa.detach().cpu()
self.structural_score_[node_idx[:batch_size]] = os.detach().cpu()
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26 changes: 17 additions & 9 deletions pygod/detector/anomalydae.py
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Expand Up @@ -167,7 +167,7 @@ def init_model(self, **kwargs):
act=self.act,
**kwargs).to(self.device)

def forward_model(self, data):
def forward_model(self, data, is_train=True):
batch_size = data.batch_size
node_idx = data.n_id

Expand All @@ -181,14 +181,22 @@ def forward_model(self, data):
weight = 1 - self.alpha
pos_weight_a = self.eta / (1 + self.eta)
pos_weight_s = self.theta / (1 + self.theta)

score = self.model.loss_func(x[:batch_size],
x_[:batch_size],
s[:batch_size, node_idx],
s_[:batch_size],
weight,
pos_weight_a,
pos_weight_s)
if 'active_mask' in data.keys():
score = self.model.loss_func(x[:batch_size][data.active_mask, :],
x_[:batch_size][data.active_mask, :],
s[:batch_size, node_idx][data.active_mask, :],
s_[:batch_size][data.active_mask, :],
weight,
pos_weight_a,
pos_weight_s)
else:
score = self.model.loss_func(x[:batch_size],
x_[:batch_size],
s[:batch_size, node_idx],
s_[:batch_size],
weight,
pos_weight_a,
pos_weight_s)

loss = torch.mean(score)

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52 changes: 36 additions & 16 deletions pygod/detector/base.py
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Expand Up @@ -473,23 +473,32 @@ def fit(self, data, label=None):
else:
self.emb[node_idx[:batch_size]] = \
self.model.emb[:batch_size].cpu()
self.decision_score_[node_idx[:batch_size]] = score
if 'active_mask' in data.keys():
self.decision_score_[node_idx[:batch_size][data.active_mask]] = score
else:
self.decision_score_[node_idx[:batch_size]] = score

optimizer.zero_grad()
loss.backward()
optimizer.step()

loss_value = epoch_loss / data.x.shape[0]
if 'active_mask' in data.keys():
loss_value = epoch_loss / data.x[data.active_mask, :].shape[0]
else:
loss_value = epoch_loss / data.x.shape[0]
if self.gan:
loss_value = (self.epoch_loss_g / data.x.shape[0], loss_value)
if 'active_mask' in data.keys():
loss_value = (self.epoch_loss_g / data.x[data.active_mask, :].shape[0], loss_value)
else:
loss_value = (self.epoch_loss_g / data.x.shape[0], loss_value)
logger(epoch=epoch,
loss=loss_value,
score=self.decision_score_,
target=label,
time=time.time() - start_time,
verbose=self.verbose,
train=True)

if 'active_mask' in data.keys():
self.decision_score_ = self.decision_score_[data.active_mask]
self._process_decision_score()
return self

Expand All @@ -510,7 +519,7 @@ def decision_function(self, data, label=None):
self.emb = torch.zeros(data.x.shape[0], self.hid_dim)
start_time = time.time()
for sampled_data in loader:
loss, score = self.forward_model(sampled_data)
loss, score = self.forward_model(sampled_data, is_train=False)
batch_size = sampled_data.batch_size
node_idx = sampled_data.n_id
if self.save_emb:
Expand All @@ -522,15 +531,26 @@ def decision_function(self, data, label=None):
else:
self.emb[node_idx[:batch_size]] = \
self.model.emb[:batch_size].cpu()

outlier_score[node_idx[:batch_size]] = score

logger(loss=loss.item() / data.x.shape[0],
score=outlier_score,
target=label,
time=time.time() - start_time,
verbose=self.verbose,
train=False)
if 'active_mask' in data.keys():
outlier_score[node_idx[:batch_size][data.active_mask]] = score
else:
outlier_score[node_idx[:batch_size]] = score
if 'active_mask' in data.keys():
logger(loss=loss.item() / data.x[data.active_mask, :].shape[0],
score=outlier_score,
target=label,
time=time.time() - start_time,
verbose=self.verbose,
train=False)
else:
logger(loss=loss.item() / data.x.shape[0],
score=outlier_score,
target=label,
time=time.time() - start_time,
verbose=self.verbose,
train=False)
if 'active_mask' in data.keys():
outlier_score = outlier_score[data.active_mask]
return outlier_score

def predict(self,
Expand Down Expand Up @@ -629,7 +649,7 @@ def init_model(self):
"""

@abstractmethod
def forward_model(self, data):
def forward_model(self, data, is_train=True):
"""
Forward pass of the neural network detector.

Expand Down
2 changes: 1 addition & 1 deletion pygod/detector/cola.py
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Original file line number Diff line number Diff line change
Expand Up @@ -137,7 +137,7 @@ def init_model(self, **kwargs):
backbone=self.backbone,
**kwargs).to(self.device)

def forward_model(self, data):
def forward_model(self, data, is_train=True):
batch_size = data.batch_size

x = data.x.to(self.device)
Expand Down
20 changes: 13 additions & 7 deletions pygod/detector/conad.py
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Original file line number Diff line number Diff line change
Expand Up @@ -185,7 +185,7 @@ def init_model(self, **kwargs):
backbone=self.backbone,
**kwargs).to(self.device)

def forward_model(self, data):
def forward_model(self, data, is_train=True):
batch_size = data.batch_size
node_idx = data.n_id

Expand All @@ -204,12 +204,18 @@ def forward_model(self, data):

x_, s_ = self.model(x, edge_index)
h = self.model.emb

score = self.model.loss_func(x[:batch_size],
x_[:batch_size],
s[:batch_size, node_idx],
s_[:batch_size],
self.weight)
if 'active_mask' in data.keys():
score = self.model.loss_func(x[:batch_size][data.active_mask, :],
x_[:batch_size][data.active_mask, :],
s[:batch_size, node_idx][data.active_mask, :],
s_[:batch_size][data.active_mask, :],
self.weight)
else:
score = self.model.loss_func(x[:batch_size],
x_[:batch_size],
s[:batch_size, node_idx],
s_[:batch_size],
self.weight)

if self.model.training:
margin_loss = self.margin_loss_func(h, h, h_aug) * label_aug
Expand Down
20 changes: 13 additions & 7 deletions pygod/detector/dominant.py
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Expand Up @@ -150,7 +150,7 @@ def init_model(self, **kwargs):
backbone=self.backbone,
**kwargs).to(self.device)

def forward_model(self, data):
def forward_model(self, data, is_train=True):
batch_size = data.batch_size
node_idx = data.n_id

Expand All @@ -159,12 +159,18 @@ def forward_model(self, data):
edge_index = data.edge_index.to(self.device)

x_, s_ = self.model(x, edge_index)

score = self.model.loss_func(x[:batch_size],
x_[:batch_size],
s[:batch_size, node_idx],
s_[:batch_size],
self.weight)
if 'active_mask' in data.keys():
score = self.model.loss_func(x[:batch_size][data.active_mask, :],
x_[:batch_size][data.active_mask, :],
s[:batch_size, node_idx][data.active_mask, :],
s_[:batch_size][data.active_mask, :],
self.weight)
else:
score = self.model.loss_func(x[:batch_size],
x_[:batch_size],
s[:batch_size, node_idx],
s_[:batch_size],
self.weight)

loss = torch.mean(score)

Expand Down
28 changes: 19 additions & 9 deletions pygod/detector/done.py
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Original file line number Diff line number Diff line change
Expand Up @@ -188,7 +188,7 @@ def init_model(self, **kwargs):
w5=self.w5,
**kwargs).to(self.device)

def forward_model(self, data):
def forward_model(self, data, is_train=True):
batch_size = data.batch_size
node_idx = data.n_id

Expand All @@ -197,14 +197,24 @@ def forward_model(self, data):
edge_index = data.edge_index.to(self.device)

x_, s_, h_a, h_s, dna, dns = self.model(x, s, edge_index)
loss, oa, os, oc = self.model.loss_func(x[:batch_size],
x_[:batch_size],
s[:batch_size],
s_[:batch_size],
h_a[:batch_size],
h_s[:batch_size],
dna[:batch_size],
dns[:batch_size])
if 'active_mask' in data.keys():
loss, oa, os, oc = self.model.loss_func(x[:batch_size][data.active_mask,:],
x_[:batch_size][data.active_mask,:],
s[:batch_size][data.active_mask,:],
s_[:batch_size][data.active_mask,:],
h_a[:batch_size][data.active_mask,:],
h_s[:batch_size][data.active_mask,:],
dna[:batch_size][data.active_mask,:],
dns[:batch_size][data.active_mask,:])
else:
loss, oa, os, oc = self.model.loss_func(x[:batch_size],
x_[:batch_size],
s[:batch_size],
s_[:batch_size],
h_a[:batch_size],
h_s[:batch_size],
dna[:batch_size],
dns[:batch_size])

self.attribute_score_[node_idx[:batch_size]] = oa.detach().cpu()
self.structural_score_[node_idx[:batch_size]] = os.detach().cpu()
Expand Down
2 changes: 1 addition & 1 deletion pygod/detector/gaan.py
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Original file line number Diff line number Diff line change
Expand Up @@ -160,7 +160,7 @@ def init_model(self, **kwargs):
act=self.act,
**kwargs).to(self.device)

def forward_model(self, data):
def forward_model(self, data, is_train=True):
batch_size = data.batch_size
node_idx = data.n_id

Expand Down
13 changes: 9 additions & 4 deletions pygod/detector/gae.py
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Original file line number Diff line number Diff line change
Expand Up @@ -151,7 +151,7 @@ def init_model(self, **kwargs):
backbone=self.backbone,
**kwargs).to(self.device)

def forward_model(self, data):
def forward_model(self, data, is_train=True):

batch_size = data.batch_size
node_idx = data.n_id
Expand All @@ -165,9 +165,14 @@ def forward_model(self, data):
h = self.model(x, edge_index)

target = s if self.recon_s else x
score = torch.mean(self.model.loss_func(target[:batch_size],
h[:batch_size],
reduction='none'), dim=1)
if 'active_mask' in data.keys():
score = torch.mean(self.model.loss_func(target[:batch_size][data.active_mask,:],
h[:batch_size][data.active_mask,:],
reduction='none'), dim=1)
else:
score = torch.mean(self.model.loss_func(target[:batch_size],
h[:batch_size],
reduction='none'), dim=1)

loss = torch.mean(score)

Expand Down
20 changes: 13 additions & 7 deletions pygod/detector/guide.py
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Original file line number Diff line number Diff line change
Expand Up @@ -184,7 +184,7 @@ def init_model(self, **kwargs):
act=self.act,
**kwargs).to(self.device)

def forward_model(self, data):
def forward_model(self, data, is_train=True):

batch_size = data.batch_size

Expand All @@ -193,12 +193,18 @@ def forward_model(self, data):
edge_index = data.edge_index.to(self.device)

x_, s_ = self.model(x, s, edge_index)

score = self.model.loss_func(x[:batch_size],
x_[:batch_size],
s[:batch_size],
s_[:batch_size],
self.alpha)
if 'active_mask' in data.keys():
score = self.model.loss_func(x[:batch_size][data.active_mask, :],
x_[:batch_size][data.active_mask, :],
s[:batch_size][data.active_mask, :],
s_[:batch_size][data.active_mask, :],
self.alpha)
else:
score = self.model.loss_func(x[:batch_size],
x_[:batch_size],
s[:batch_size],
s_[:batch_size],
self.alpha)

loss = torch.mean(score)

Expand Down
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