-
Notifications
You must be signed in to change notification settings - Fork 0
/
9_test_attack_models.py
186 lines (147 loc) · 7.43 KB
/
9_test_attack_models.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix, plot_roc_curve, roc_curve, auc
import numpy as np
import pickle
from utils import load_data_csv, fix_randomness, visualize_cm
import yaml
fix_randomness()
# read config file
with open("./config/config.yaml", "r") as ymlfile:
try:
cfg = yaml.safe_load(ymlfile)
except yaml.YAMLError as exc:
print(exc)
problem = cfg['problem']
# Load attack feature configuration file
path_to_cfg_target_feature = "./config/target_attributes_" + problem + ".yaml"
with open(path_to_cfg_target_feature, "r") as ymlfile:
try:
cfg_target_attribute = yaml.safe_load(ymlfile)
except yaml.YAMLError as exc:
print(exc)
#
X_test = load_data_csv(cfg["dataset"][problem]["path_to_x_test"])
y_test = load_data_csv(cfg["dataset"][problem]["path_to_y_test"])
y_true = y_test
print("Test {} attack model".format(cfg["attack_model"]["model"]))
# load the attack model from disk
if cfg["attack_model"]["model"] == "KNN":
attack_model = pickle.load(open(cfg["attack_model"]["KNN"]["model_path"], 'rb'))
elif cfg["attack_model"]["model"] == "NB":
attack_model = pickle.load(open(cfg["attack_model"]["NB"]["model_path"], 'rb'))
elif cfg["attack_model"]["model"] == "LR":
attack_model = pickle.load(open(cfg["attack_model"]["LR"]["model_path"], 'rb'))
elif cfg["attack_model"]["model"] == "DT":
attack_model = pickle.load(open(cfg["attack_model"]["DT"]["model_path"], 'rb'))
elif cfg["attack_model"]["model"] == "MLP":
attack_model = pickle.load(open(cfg["attack_model"]["MLP"]["model_path"], 'rb'))
elif cfg["attack_model"]["model"] == "XGBoost":
attack_model = pickle.load(open(cfg["attack_model"]["XGBoost"]["model_path"], 'rb'))
test_preds = attack_model.predict(X_test)
print("===========Test model===========")
testAcc = accuracy_score(y_true, test_preds)
print("+ Accuracy = ", testAcc)
if cfg_target_attribute['target_attribute']['isBinary'] == True:
testF1 = f1_score(y_true, test_preds)
testPrecision = precision_score(y_true, test_preds)
testRecall = recall_score(y_true, test_preds)
print("+ F1 score = ", testF1)
print("+ Precision = ", testPrecision)
print("+ Recall = ", testRecall)
cm = confusion_matrix(y_true, test_preds)
print("+ Confusion matrix:\n", cm)
#
X_test = load_data_csv(cfg["dataset"][problem]["path_to_x_test_with_noise"])
y_test = load_data_csv(cfg["dataset"][problem]["path_to_y_test"])
y_true = y_test
print("Test {} attack model".format(cfg["attack_model"]["model"]))
# load the attack model from disk
if cfg["attack_model"]["model"] == "KNN":
attack_model = pickle.load(open(cfg["attack_model"]["KNN"]["model_path_with_noise"], 'rb'))
elif cfg["attack_model"]["model"] == "NB":
attack_model = pickle.load(open(cfg["attack_model"]["NB"]["model_path_with_noise"], 'rb'))
elif cfg["attack_model"]["model"] == "LR":
attack_model = pickle.load(open(cfg["attack_model"]["LR"]["model_path_with_noise"], 'rb'))
elif cfg["attack_model"]["model"] == "DT":
attack_model = pickle.load(open(cfg["attack_model"]["DT"]["model_path_with_noise"], 'rb'))
elif cfg["attack_model"]["model"] == "MLP":
attack_model = pickle.load(open(cfg["attack_model"]["MLP"]["model_path_with_noise"], 'rb'))
elif cfg["attack_model"]["model"] == "XGBoost":
attack_model = pickle.load(open(cfg["attack_model"]["XGBoost"]["model_path_with_noise"], 'rb'))
test_preds = attack_model.predict(X_test)
print("===========Test model (with noise)===========")
testAcc = accuracy_score(y_true, test_preds)
print("+ Accuracy = ", testAcc)
if cfg_target_attribute['target_attribute']['isBinary'] == True:
testF1 = f1_score(y_true, test_preds)
testPrecision = precision_score(y_true, test_preds)
testRecall = recall_score(y_true, test_preds)
print("+ F1 score = ", testF1)
print("+ Precision = ", testPrecision)
print("+ Recall = ", testRecall)
cm = confusion_matrix(y_true, test_preds)
print("+ Confusion matrix:\n", cm)
#
X_test = load_data_csv(cfg["dataset"][problem]["path_to_x_test_with_DP"])
y_test = load_data_csv(cfg["dataset"][problem]["path_to_y_test"])
y_true = y_test
print("Test {} attack model".format(cfg["attack_model"]["model"]))
# load the attack model from disk
if cfg["attack_model"]["model"] == "KNN":
attack_model = pickle.load(open(cfg["attack_model"]["KNN"]["model_path_with_DP"], 'rb'))
elif cfg["attack_model"]["model"] == "NB":
attack_model = pickle.load(open(cfg["attack_model"]["NB"]["model_path_with_DP"], 'rb'))
elif cfg["attack_model"]["model"] == "LR":
attack_model = pickle.load(open(cfg["attack_model"]["LR"]["model_path_with_DP"], 'rb'))
elif cfg["attack_model"]["model"] == "DT":
attack_model = pickle.load(open(cfg["attack_model"]["DT"]["model_path_with_DP"], 'rb'))
elif cfg["attack_model"]["model"] == "MLP":
attack_model = pickle.load(open(cfg["attack_model"]["MLP"]["model_path_with_DP"], 'rb'))
elif cfg["attack_model"]["model"] == "XGBoost":
attack_model = pickle.load(open(cfg["attack_model"]["XGBoost"]["model_path_with_DP"], 'rb'))
test_preds = attack_model.predict(X_test)
print("===========Test model (with DP)===========")
testAcc = accuracy_score(y_true, test_preds)
print("+ Accuracy = ", testAcc)
if cfg_target_attribute['target_attribute']['isBinary'] == True:
testF1 = f1_score(y_true, test_preds)
testPrecision = precision_score(y_true, test_preds)
testRecall = recall_score(y_true, test_preds)
print("+ F1 score = ", testF1)
print("+ Precision = ", testPrecision)
print("+ Recall = ", testRecall)
cm = confusion_matrix(y_true, test_preds)
print("+ Confusion matrix:\n", cm)
#
X_test = load_data_csv(cfg["dataset"][problem]["path_to_x_test_with_DP_with_noise"])
y_test = load_data_csv(cfg["dataset"][problem]["path_to_y_test"])
y_true = y_test
print("Test {} attack model".format(cfg["attack_model"]["model"]))
# load the attack model from disk
if cfg["attack_model"]["model"] == "KNN":
attack_model = pickle.load(open(cfg["attack_model"]["KNN"]["model_path_with_DP_with_noise"], 'rb'))
elif cfg["attack_model"]["model"] == "NB":
attack_model = pickle.load(open(cfg["attack_model"]["NB"]["model_path_with_DP_with_noise"], 'rb'))
elif cfg["attack_model"]["model"] == "LR":
attack_model = pickle.load(open(cfg["attack_model"]["LR"]["model_path_with_DP_with_noise"], 'rb'))
elif cfg["attack_model"]["model"] == "DT":
attack_model = pickle.load(open(cfg["attack_model"]["DT"]["model_path_with_DP_with_noise"], 'rb'))
elif cfg["attack_model"]["model"] == "MLP":
attack_model = pickle.load(open(cfg["attack_model"]["MLP"]["model_path_with_DP_with_noise"], 'rb'))
elif cfg["attack_model"]["model"] == "XGBoost":
attack_model = pickle.load(open(cfg["attack_model"]["XGBoost"]["model_path_with_DP_with_noise"], 'rb'))
test_preds = attack_model.predict(X_test)
print("===========Test model (with DP, with noise)===========")
testAcc = accuracy_score(y_true, test_preds)
print("+ Accuracy = ", testAcc)
if cfg_target_attribute['target_attribute']['isBinary'] == True:
testF1 = f1_score(y_true, test_preds)
testPrecision = precision_score(y_true, test_preds)
testRecall = recall_score(y_true, test_preds)
print("+ F1 score = ", testF1)
print("+ Precision = ", testPrecision)
print("+ Recall = ", testRecall)
cm = confusion_matrix(y_true, test_preds)
print("+ Confusion matrix:\n", cm)