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araison 2022-12-17 18:13:11 +01:00
commit 45d39edbd9
4 changed files with 204 additions and 19 deletions
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52
explaining_framework/explainers/wrappers/test.py
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@ -2,6 +2,10 @@ import traceback
import torch
import torch.nn as nn
from explaining_framework.explaining_framework.metric.accuracy import Accuracy
from explaining_framework.explaining_framework.metric.fidelity import Fidelity
from explaining_framework.explaining_framework.metric.robust import Attack
from explaining_framework.explaining_framework.metric.sparsity import Sparsity
from torch_geometric.data import Batch, Data
from torch_geometric.explain import Explainer
from torch_geometric.nn import GATConv, GCNConv, GINConv, global_mean_pool
@ -27,16 +31,16 @@ __all__captum = [
__all__graphxai = [
"CAM",
"GradCAM",
"GNN_LRP",
"GradExplainer",
"GuidedBackPropagation",
"IntegratedGradients",
"PGExplainer",
"PGMExplainer",
"RandomExplainer",
"SubgraphX",
"GraphMASK",
# "GradCAM",
# "GNN_LRP",
# "GradExplainer",
# "GuidedBackPropagation",
# "IntegratedGradients",
# "PGExplainer",
# "PGMExplainer",
# "RandomExplainer",
# "SubgraphX",
# "GraphMASK",
]
@ -77,9 +81,8 @@ for epoch in range(1, 2):
optimizer.step()
target = torch.LongTensor([[0]])
for kind in ["node", "graph"]:
print(kind)
for name in __all__captum + __all__graphxai:
for kind in ["graph"]:
for name in __all__graphxai:
if name in __all__captum:
explaining_algorithm = CaptumWrapper(name)
elif name in __all__graphxai:
@ -102,6 +105,7 @@ for kind in ["node", "graph"]:
task_level=kind,
return_type="raw",
),
threshold_config=dict(threshold_type="hard", value=0.5),
)
explanation = explainer(
x=batch.x,
@ -109,10 +113,26 @@ for kind in ["node", "graph"]:
index=int(target),
target=batch.y,
)
explanation.__setattr__(
"model_prediction", explainer.get_prediction(x, edge_index)
# explanation.__setattr__(
# "model_prediction", explainer.get_prediction(x, edge_index)
# )
explanation_threshold = explanation._apply_mask(
node_mask=explanation.node_mask, edge_mask=explanation.edge_mask
)
print(explanation.__dict__)
for f_name in [
"precision_score",
"precision_score",
"jaccard_score",
"roc_auc_score",
"f1_score",
"accuracy_score",
]:
acc = Accuracy(f_name)
gt = torch.ones_like(x) / 2
out = acc.forward(mask=explanation_threshold.node_mask, target=gt)
print(out)
except Exception as e:
# print(str(e))
pass

15
explaining_framework/metric/fidelity.py
View File

@ -10,6 +10,8 @@ NUM_CLASS = 5
def softmax(data):
return Softmax(dim=1)(data)
def kl(data1,data2):
return KLDivLoss(dim=1)(data1,data2)
class Fidelity(Metric):
@ -23,12 +25,12 @@ class Fidelity(Metric):
"infidelity_KL",
]
self.metric = self.load_metric(name)
self.exp_sub = None
self.exp_sub_c = None
self.s_exp_sub = None
self.s_exp_sub_c = None
self.s_initial_data = None
self.metric = self.load_metric(name)
def _score_check(self):
if any(
@ -90,6 +92,14 @@ class Fidelity(Metric):
torch.norm(1 - prob_initial, p=1) - torch.norm(1 - prob_exp, p=1)
).item()
def _infidelity_KL(self, exp:Explanation) -> float:
self._score_check()
prob_initial = softmax(self.s_initial_data)
prob_exp = softmax(self.s_exp_sub)
return torch.mean(1 - torch.exp(-kl(prob_exp,prob_initial))).item()
def score(self, exp):
self.exp_sub = exp.get_explanation_subgraph()
self.exp_sub_c = exp.get_complement_subgraph()
@ -107,9 +117,12 @@ class Fidelity(Metric):
self.metric = lambda exp: self._fidelity_plus_prob(exp)
if name == "fidelity_minus_prob":
self.metric = lambda exp: self._fidelity_minus_prob(exp)
if name == "_infidelity_KL":
self.metric = lambda exp: self._infidelity_KL(exp)
else:
raise ValueError(f"{name} is not supported")
return self.metric
def __call__(self, exp: Explanation):
self.score(exp)
return self.metric(exp)

153
explaining_framework/metric/robust.py
View File

@ -1,4 +1,155 @@
import copy
def compute_gradient(model,input,target, loss):
with torch.autograd.set_grad_enabled(True):
out = model(input)
err = loss(out,target)
return torch.autograd.grad(err,input)
class FGSM(object):
def __init__(self,model: torch.nn.Module,loss: torch.nn.Module,lower_bound: float = float("-inf"), upper_bound: float = float("inf")):
self.model = model
self.loss = loss
self.lower_bound = lower_bound
self.upper_bound = upper_bound
self.bound = lambda x: torch.clamp(x, min=lower_bound, max=upper_bound)
self.zero_thresh = 10**-6
def forward(self, input, target, epsilon:float) -> Explanation:
grad = compute_gradient(model=self.model,input=input, target=target, loss=self.loss)
grad = self.bound(grad)
out = torch.where(torch.abs(grad) > self.zero_thresh,input - epsilon * torch.sign(grad),input)
return out
class PGD(object):
def __init__(self,model: torch.nn.Module,loss: torch.nn.Module,lower_bound: float = float("-inf"), upper_bound: float = float("inf")):
self.model = model
self.loss = loss
self.lower_bound = lower_bound
self.upper_bound = upper_bound
self.bound = lambda x: torch.clamp(x, min=lower_bound, max=upper_bound)
self.zero_thresh = 10**-6
self.fgsm = FGSM(model=model,loss=loss,lower_bound=lower_bound,upper_bound=upper_bound)
def forward(self, input, target, epsilon:float, radius:float, step_num:int, random_start:bool = False, norm:str='inf') -> Explanation:
diff = outputs - inputs
if norm == "inf":
return inputs + torch.clamp(diff, -radius, radius)
elif norm == "2":
return inputs + torch.renorm(diff, 2, 0, radius)
else:
raise AssertionError("Norm constraint must be 2 or inf.")
perturbed_inputs = input
if random_start:
perturbed_inputs= self.bound(self._random_point(input, radius, norm))
for _ in range(step_num):
perturbed_inputs = self.fgsm.perturb(
input=perturbed_inputs, epsilon=epsilon, target=target
)
perturbed_inputs = self.forward(input, perturbed_inputs)
perturbed_inputs = self.bound(perturbed_inputs).detach()
return perturbed_inputs
def _random_point(self, center: Tensor, radius: float, norm: str) -> Tensor:
r"""
A helper function that returns a uniform random point within the ball
with the given center and radius. Norm should be either L2 or Linf.
"""
if norm == "2":
u = torch.randn_like(center)
unit_u = F.normalize(u.view(u.size(0), -1)).view(u.size())
d = torch.numel(center[0])
r = (torch.rand(u.size(0)) ** (1.0 / d)) * radius
r = r[(...,) + (None,) * (r.dim() - 1)]
x = r * unit_u
return center + x
elif norm == "inf":
x = torch.rand_like(center) * radius * 2 - radius
return center + x
else:
raise AssertionError("Norm constraint must be L2 or Linf.")
class Attack(Metric):
def __init__(name: str, model: torch.nn.Module, dropout:float = 0.5):
super().__init__(name=name, model=model)
self.name = name
self.model = model
self.authorized_metric = [
"gaussian_noise",
"add_edge",
"remove_edge",
"remove_node"
"pgd",
"fgsm",
]
self.dropout = dropout
self._load_metric(name)
def _gaussian_noise(self,exp) -> Explanation:
x= torch.clone(exp.x)
x=x+torch.randn(*x.shape)
exp_ = copy.copy(exp)
exp_.x = x
return exp_
name in ['gaussian noise attack', 'edge perturbation attack', 'pgm', 'fgsd']:wq
def _add_edge(self,exp,p:float) -> Explanation:
exp_ = copy.copy(exp)
exp_.edge_index, _ = add_random_edge(exp_.edge_index,p=p,num_nodes=exp_.x.shape[0])
return exp_
def _remove_edge(self,exp,p:float) -> Explanation:
exp_ = copy.copy(exp)
exp_.edge_index, _ = dropout_edge(exp_.edge_index,p=p)
return exp_
def _remove_node(self,exp,p:float) -> Explanation:
exp_ = copy.copy(exp)
exp_.edge_index, _ = dropout_node(exp_.edge_index,p=p,num_nodes=exp_.x.shape[0])
return exp_
def _load_add_edge(self):
return lambda exp : self._add_edge(exp,p=self.dropout)
def _load_remove_edge(self):
return lambda exp : self._remove_edge(exp,p=self.dropout)
def _load_remove_node(self):
return lambda exp : self._remove_node(exp,p=self.dropout)
def _load_gaussian_noise(self):
return lambda exp: self._gaussian_noise(exp)
def _load_metric(self):
if name in self.authorized_metric:
if name == "gaussian_noise":
self.metric= self._load_gaussian_noise()
if name == "add_edge":
self.metric=self._load_add_edge()
if name == "remove_edge":
self.metric= self._load_remove_edge()
if name == "remove_node":
self.metric= self._load_remove_node()
if name== "pgd":
print('set LOSS with cfg ')
pgd = PGD(model=self.model,loss=LOSS)
self.metric = lambda exp:pgd.forward(input=exp,target=exp.y,epsilon=1,radius=1, step_num = 50, random_start=False, norm = 'inf')
if name== "fgsm":
print('set LOSS with cfg ')
pgd = FGSM(model=self.model,loss=LOSS)
self.metric = lambda exp:pgd.forward(input=exp,target=exp.y,epsilon=1)
else:
raise ValueError(f'{name} is not supported yet')
return self.metric
def forward(self,exp) -> Explanation:
attack = self.metric(exp)
return attack

3
explaining_framework/metric/sparsity.py
View File

@ -5,4 +5,5 @@ class Sparsity(Metric):
def __init__(self,name):
super().__init__(name=name,model=None)
def
def forward(self, mask):
return torch.mean(mask.float()).item()