def compute_p_a_prime(self, instance):
# find X'A(x')
new_list = []
for attack_instance in self.new_list:
if not fv_equals(attack_instance.get_feature_vector(),
instance.get_feature_vector()):
new_list.append(attack_instance)
p_x_ = 0
for attack_instance in new_list:
p_x_ += np.exp(self.posterior_proba(attack_instance))[0, 1]
return p_x_ + self.i_x(instance) * \
np.exp(self.posterior_proba(instance))[0, 1]
def find_new_list(self, instances):
"""
Find the changed instances in the attacker's modified data from training data
:param instances: attacker's modified data
:return: None
"""
new_list = []
for attack_instance in instances:
equal = False
for instance in self.training_instances:
if fv_equals(attack_instance.get_feature_vector(),
instance.get_feature_vector()):
equal = True
break
if not equal:
new_list.append(attack_instance)
self.new_list = new_list
def train(self):
'''
This is implemented according to Algorithm 1 in Central Rettraining Framework
for Scalable Adversarial Classification. This will iterate between computing
a classifier and adding the adversarial instances to the training data that evade
the previously computed classifier.
:return: None
'''
self.model.train(self.training_instances)
iteration = self.iteration_times
#self.attacker = self.attack_alg()
#self.attacker.set_params(self.adv_params)
#self.attacker.set_adversarial_params(self.model, self.training_instances)
print("==> Training...")
malicious_instances = [
x for x in self.training_instances if self.model.predict(x) == 1
]
augmented_instances = self.training_instances
while iteration != 0:
new = []
transformed_instances = self.attacker.attack(malicious_instances)
for instance in transformed_instances:
in_list = False
for idx, old_instance in enumerate(augmented_instances):
if fv_equals(old_instance.get_feature_vector(),
instance.get_feature_vector()):
in_list = True
if not in_list:
new.append(instance)
augmented_instances.append(
Instance(label=1,
feature_vector=instance.get_feature_vector()))
self.model.train(augmented_instances)
malicious_instances = [
x for x in augmented_instances if self.model.predict(x) == 1
]
iteration -= 1
if new is None:
break