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adversarial_dt.py
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adversarial_dt.py
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import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from .defense import get_aug_data
class AdversarialDt(DecisionTreeClassifier):
def __init__(self, ord=np.inf, sep_measure=None, attack_model=None,
train_type='adv', **kwargs):
"""Decision Tree Classifier with defense
Keyword Arguments:
ord {float} -- adversarial example perturbation measure (default: {np.inf})
sep_measure {float} -- The distance measure for data, if None, it will be the same as `ord` (default: {None})
attack_model {Attack Model} -- The Attack Model, only use when `train_type` is 'adv' (default: {None})
train_type {str} -- None for undefended classifier, 'robustv2' for adversarial pruning, 'adv' for adversarial training (default: {'adv'})
Other Arguments follows the original scikit-learn argument (sklearn.tree.DecisionTreeClassifier).
"""
self.ord = ord
self.sep_measure = sep_measure
self.attack_model = attack_model
self.train_type = train_type
if self.train_type is None:
pass
elif self.train_type == 'adv':
self.eps = kwargs.pop('eps')
elif self.train_type == 'robust':
kwargs['splitter'] = 'robust'
#kwargs['eps'] = self.eps
elif self.train_type[:7] == 'robust_':
# for hybrid
kwargs['splitter'] = 'robust'
#kwargs['eps'] = self.eps
self.train_type = self.train_type[7:]
# The modified DecisionTreeClassifier eats the esp argument
super().__init__(**kwargs)
def fit(self, X, y, eps:float=None):
print("original X", np.shape(X), len(y))
self.augX, self.augy = get_aug_data(self, X, y, eps)
print("number of augX", np.shape(self.augX), len(self.augy))
return super().fit(self.augX, self.augy)
class AdversarialRf(RandomForestClassifier):
def __init__(self, ord=np.inf, sep_measure=None, attack_model=None,
train_type='adv', **kwargs):
"""Random Forest Classifier with defense
Keyword Arguments:
ord {float} -- adversarial example perturbation measure (default: {np.inf})
sep_measure {float} -- The distance measure for data, if None, it will be the same as `ord` (default: {None})
attack_model {Attack Model} -- The Attack Model, only use when `train_type` is 'adv' (default: {None})
train_type {str} -- None for undefended classifier, 'robustv2' for adversarial pruning, 'adv' for adversarial training (default: {'adv'})
Other Arguments follows the original scikit-learn argument (sklearn.tree.RandomForestClassifier).
"""
self.ord = ord
self.sep_measure = sep_measure
self.attack_model = attack_model
self.train_type = train_type
if self.train_type is None:
pass
elif self.train_type == 'adv':
self.eps = kwargs.pop('eps')
elif self.train_type == 'robust':
kwargs['splitter'] = 'robust'
elif self.train_type[:7] == 'robust_':
# for hybrid
kwargs['splitter'] = 'robust'
self.train_type = self.train_type[7:]
# The modified RandomForestClassifier eats the esp argument
super().__init__(**kwargs)
def fit(self, X, y, eps:float=None):
print("original X", np.shape(X), len(y))
self.augX, self.augy = get_aug_data(self, X, y, eps)
print("number of augX", np.shape(self.augX), len(self.augy))
return super().fit(self.augX, self.augy)