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
|
"""
Date: 2022-04-13
Author: [email protected]
Desc: target model: MLP
"""
from sklearn.neural_network import MLPClassifier
import torch
from sklearn.metrics import confusion_matrix
import joblib
from torch.utils.data import DataLoader
import numpy as np
import warnings
warnings.filterwarnings("ignore")
class TargetMLP():
"""
"""
def __init__(self, param):
# 正则化
self.clf = MLPClassifier(
activation=param['activate'],
hidden_layer_sizes=param['hidden_size'],
learning_rate_init=param['learning_rate_init'],
max_iter=param['max_iter'],
momentum=param['momentum'],
solver=param['solver'],
alpha=param['alpha'],
batch_size=param['batch_size'],
)
def train(self, dataloader):
X = []
y = []
for batch_x, batch_y in dataloader:
X += batch_x.data.numpy().tolist()
y += batch_y.data.numpy().tolist()
X = np.array(X)
y = np.array(y)
# print("X.shape:{}".format(X.size))
# print("y.shape:{}".format(y.size))
self.clf.fit(X, y)
print("training score:{}".format(self.clf.score(X, y)))
def eval(self, dataloader):
X = []
y = []
for batch_x, batch_y in dataloader:
X += batch_x.data.numpy().tolist()
y += batch_y.data.numpy().tolist()
X = np.array(X)
y = np.array(y)
# print("X.shape:{}".format(X.size))
# print("y.shape:{}".format(y.size))
y_pred = self.clf.predict(X)
return y_pred, y
def save(self, filename):
joblib.dump(self.clf, filename)
def load(self, filename):
self.clf = joblib.load(filename)
if __name__ == '__main__':
param = {
'activate': 'relu',
'hidden_size': (100, 100),
'learning_rate_init': 0.001,
'max_iter': 200,
'momentum': 0.9,
'solver': 'adam',
'alpha': 0.01,
'batch_size': 128
}
sample_szie = 580
botname = "Gozi"
normal = "CTUNone"
batch_size = 128
total_size = sample_szie * 2
test_size = int(total_size * 0.2)
train_size = int((total_size - test_size) * 0.8)
valid_size = total_size - test_size - train_size
print("train data: {}".format(train_size))
print("valid data: {}".format(valid_size))
print("test data: {}".format(test_size))
# c2data = C2Data(botname, number=sample_szie, sequenceLen=30)
# train_data, test_data = torch.utils.data.random_split(c2data, [train_size + valid_size, test_size])
# train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, drop_last=False)
# test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True, drop_last=False)
#
# svm = TargetLR(param)
# svm.train(train_loader)
# y_true, y_pred = svm.eval(test_loader)
# print("confusion_metrix: \n{}".format(confusion_matrix(y_true, y_pred)))
# filename = "../modelFile/target_{}_{}_{}.pkt".format(arch, botname, normal)
# svm.save(filename)
|
