-
Notifications
You must be signed in to change notification settings - Fork 3
/
svm_pso_predictor_mTVAC_RIW.py
166 lines (141 loc) · 6.75 KB
/
svm_pso_predictor_mTVAC_RIW.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
# coding: utf-8
import numpy as np
import random
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import svm
from sklearn.model_selection import train_test_split
import cmath
# data = pd.read_csv('data/drug_cell/drug/AEW541_train_data-rfe.csv')
# X = data.iloc[:, :-1]
# y = data.iloc[:, -1]
# x_train, x_test, y_train, y_test = train_test_split(X, y, random_state=1, train_size=0.7)
#
# MAX_ITER = 500
# ----------------------PSO参数设置---------------------------------
class PSO_MTVACRW():
def __init__(self, max_iter, x_train, y_train, x_test, y_test, pN=30, dim=2):
self.x_train = x_train
self.y_train = y_train
self.x_test = x_test
self.y_test = y_test
self.w = 0.9 # 惯性权重
self.wS = 0.9
self.wE = 0.4
self.c1 = 2
self.c2 = 2
self.c1f = 2.5
self.c1i = 0.5
self.c2f = 0.5
self.c2i = 2.5
self.r1 = random.uniform(0, 1)
self.r2 = random.uniform(0, 1)
self.r3 = random.uniform(0, 1)
self.r4 = random.uniform(0, 1)
self.mprop = random.uniform(0, 1) # 突变概率
self.rp = random.randint(0, pN - 1) # 随机选择一个微粒(index)
self.rd = random.randint(0, dim - 1) # 随机选择一个维度(index)
self.m = 2 # 常量, 怎么取值？？？
self.pN = pN # 粒子数量
self.dim = dim # 搜索维度
self.maxC = 10 # 惩罚因子C的最大值
self.minC = 0.00001 # 惩罚因子C的最小值
self.maxGamma = 5 # 参数gamma的最大值
self.minGamma = 0.00001 # 参数gamma的最小值
self.max_v = np.array([self.maxC, self.maxGamma]) # 最大速度
self.min_v = np.array([-self.maxC, -self.maxGamma]) # 最小速度，反方向
self.max_x = np.array([self.maxC, self.maxGamma]) # 粒子位置的上界
self.min_x = np.array([self.minC, self.minGamma]) # 粒子位置的下界
self.max_iter = max_iter # 迭代次数
self.X = np.zeros((self.pN, self.dim)) # 所有粒子的位置和速度
self.V = np.zeros((self.pN, self.dim))
self.pbest = np.zeros((self.pN, self.dim)) # 个体经历的最佳位置和全局最佳位置
self.gbest = np.zeros((1, self.dim))
self.p_fit = np.zeros(self.pN) # 每个个体的历史最佳适应值
self.fit = 1e10 # 全局最佳适应值
# ---------------------目标函数Sphere函数-----------------------------
def function(self, c, g):
if g <= 0 or c <= 0:
return 1e10
model = svm.SVC(C=c, gamma=g) # gamma缺省值为 1.0/x.shape[1]
model.fit(self.x_train, self.y_train)
y_score = model.score(self.x_test, self.y_test)
return -y_score
# ---------------------初始化种群----------------------------------
def init_Population(self):
for i in range(self.pN):
for j in range(self.dim):
self.X[i][j] = random.uniform(self.min_x[j], self.max_x[j]) # 位置的初始范围
self.V[i][j] = random.uniform(self.min_v[j], self.max_v[j]) # 速度的初始范围
self.pbest[i] = self.X[i]
tmp = self.function(self.X[i][0], self.X[i][1])
self.p_fit[i] = tmp
if tmp < self.fit:
self.fit = tmp
self.gbest = self.X[i]
# ----------------------更新粒子位置----------------------------------
def iterator(self):
fitness = []
for iter in range(self.max_iter):
for i in range(self.pN): # 更新gbest\pbest
temp = self.function(self.X[i][0], self.X[i][1])
if temp < self.p_fit[i]: # 更新个体最优
self.p_fit[i] = temp
self.pbest[i] = self.X[i]
if self.p_fit[i] < self.fit: # 更新全局最优
self.gbest = self.X[i]
self.fit = self.p_fit[i]
# 每次迭代都更新随机系数
self.r1 = random.uniform(0, 1)
self.r2 = random.uniform(0, 1)
self.r3 = random.uniform(0, 1)
self.r4 = random.uniform(0, 1)
self.rp = random.randint(0, self.pN - 1) # 随机选择一个微粒(index)
self.rd = random.randint(0, self.dim - 1) # 随机选择一个维度(index)
for i in range(self.pN):
for d in range(self.dim): # 对维度遍历
self.V[i][d] = self.w * self.V[i][d] + self.c1 * self.r1 * (self.pbest[i][d] - self.X[i][d]) + \
self.c2 * self.r2 * (self.gbest[d] - self.X[i][d])
# 限制粒子速度边界
if self.V[i][d] > self.max_v[d]:
self.V[i][d] = self.max_v[d]
elif self.V[i][d] < self.min_v[d]:
self.V[i][d] = self.min_v[d]
self.X[i][d] = self.X[i][d] + self.V[i][d] # 更新粒子位置
# 限制粒子位置边界
if self.X[i][d] > self.max_x[d]:
self.X[i][d] = self.max_x[d]
elif self.X[i][d] < self.min_x[d]:
self.X[i][d] = self.min_x[d]
fitness.append(self.fit)
if len(fitness) >= 2 and (fitness[-1] - fitness[-2] <= 0):
if self.r1 < self.mprop:
if self.r2 < 0.5:
self.V[self.rp][self.rd] += self.r3 * self.max_v[self.rd] / self.m
else:
self.V[self.rp][self.rd] -= self.r4 * self.max_v[self.rd] / self.m
print('V: ', self.V[0], end=" ")
print('X: ', self.X[0], end=" ")
print(self.fit, end=" ") # 输出最优值
print('PSO-mTVAC 当前迭代次数：', iter)
# 更新学习因子
self.c1 = (self.c1f - self.c1i) * iter / self.max_iter + self.c1i
self.c2 = (self.c2f - self.c2i) * iter / self.max_iter + self.c2i
# 更新惯性权重
# self.w = self.wS - (self.wS - self.wE) * iter / self.max_iter
# self.w = 0.5 + random.uniform(0, 1) / 2
return fitness
# ----------------------程序执行-----------------------
# my_pso = PSO(pN=30, dim=2, max_iter=MAX_ITER) # 维度代表变量的个数
# my_pso.init_Population()
# fitness = my_pso.iterator()
# # -------------------画图--------------------
# plt.figure(1)
# plt.title("Figure1")
# plt.xlabel("iterators", size=14)
# plt.ylabel("fitness", size=14)
# t = np.array([t for t in range(0, MAX_ITER)])
# fitness = np.array(fitness)
# fitness_2 = [-v for v in fitness] # 取反，得到正数，模型准确率
# plt.plot(t, fitness_2, color='b', linewidth=3)
# plt.show()