-
Notifications
You must be signed in to change notification settings - Fork 3
/
svm-pso-predictor-compare.py
111 lines (90 loc) · 4.8 KB
/
svm-pso-predictor-compare.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
# 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
from svm_pso_predictor_K import PSO_K
from svm_pso_predictor_TVIW import PSO_W
from svm_pso_predictor_TVAC import PSO_TVAC
from svm_pso_predictor_mTVAC import PSO_MTVAC
from svm_pso_predictor_mTVAC_RIW import PSO_MTVACRW
from svm_pso_predictor_hTVAC import PSO_HTVAC
from svm_pso_predictor_original import PSO
from svm_pso_predictor_RANDIW import PSO_RW
import time
data = pd.read_csv('data/drug_cell/drug/Erlotinib_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 = 1000
for run in range(50):
# ----------------------程序执行-----------------------
start = time.time()
# pso_K = PSO_K(max_iter=MAX_ITER, x_train=x_train, y_train=y_train, x_test=x_test,
# y_test=y_test) # 维度代表变量的个数
# pso_K.init_Population()
# fitness_K = pso_K.iterator()
pso_W = PSO_W(max_iter=MAX_ITER, x_train=x_train, y_train=y_train, x_test=x_test,
y_test=y_test) # 维度代表变量的个数
pso_W.init_Population()
fitness_W = pso_W.iterator()
pso_TVAC = PSO_TVAC(max_iter=MAX_ITER, x_train=x_train, y_train=y_train, x_test=x_test,
y_test=y_test) # 维度代表变量的个数
pso_TVAC.init_Population()
fitness_TVAC = pso_TVAC.iterator()
pso_mTVAC = PSO_MTVAC(max_iter=MAX_ITER, x_train=x_train, y_train=y_train, x_test=x_test,
y_test=y_test) # 维度代表变量的个数
pso_mTVAC.init_Population()
fitness_mTVAC = pso_mTVAC.iterator()
# 去掉了线性递减的惯性权重，改为0.5+rand(0,1)/2
# pso_mTVACRW = PSO_MTVACRW(max_iter=MAX_ITER, x_train=x_train, y_train=y_train, x_test=x_test,
# y_test=y_test) # 维度代表变量的个数
# pso_mTVACRW.init_Population()
# fitness_mTVACRW = pso_mTVACRW.iterator()
# pso_hTVAC = PSO_HTVAC(max_iter=MAX_ITER, x_train=x_train, y_train=y_train, x_test=x_test,
# y_test=y_test) # 维度代表变量的个数
# pso_hTVAC.init_Population()
# fitness_hTVAC = pso_hTVAC.iterator()
pso_original = PSO(max_iter=MAX_ITER, x_train=x_train, y_train=y_train, x_test=x_test,
y_test=y_test) # 维度代表变量的个数
pso_original.init_Population()
fitness_original = pso_original.iterator()
# pso_RW = PSO_RW(max_iter=MAX_ITER, x_train=x_train, y_train=y_train, x_test=x_test,
# y_test=y_test) # 维度代表变量的个数
# pso_RW.init_Population()
# fitness_RW = pso_RW.iterator()
end = time.time()
# -------------------画图--------------------
plt.figure(figsize=(10, 8))
plt.title('PSO comparison, cost: %.2f seconds' % (end-start), size=16)
plt.xlabel("number of generations", size=16)
plt.ylabel("SVM model accuracy rate", size=16)
t = np.array([t for t in range(0, MAX_ITER)])
# fitness_K = np.array(fitness_K)
# fitness_K2 = [-v for v in fitness_K] # 取反，得到正数，模型准确率
fitness_W2 = [-v for v in fitness_W] # 取反，得到正数，模型准确率
fitness_TVAC2 = [-v for v in fitness_TVAC] # 取反，得到正数，模型准确率
fitness_mTVAC2 = [-v for v in fitness_mTVAC] # 取反，得到正数，模型准确率
# fitness_mTVACRW2 = [-v for v in fitness_mTVACRW] # 取反，得到正数，模型准确率
# fitness_hTVAC2 = [-v for v in fitness_hTVAC] # 取反，得到正数，模型准确率
pso_original2 = [-v for v in fitness_original] # 取反，得到正数，模型准确率
# fitness_RW2 = [-v for v in fitness_RW] # 取反，得到正数，模型准确率
# plt.plot(t, fitness_K2, color='b', linewidth=3, label='PSO-K')
plt.plot(t, pso_original2, color='b', linewidth=3, label='PSO', ls='-.')
plt.plot(t, fitness_W2, color='r', linewidth=3, label='PSO-TVIW', ls=':')
plt.plot(t, fitness_TVAC2, color='g', linewidth=3, label='PSO-TVAC', ls='--')
# plt.plot(t, fitness_RW2, color='lightblue', linewidth=3, label='PSO-RANDIW', ls='-')
plt.plot(t, fitness_mTVAC2, color='orange', linewidth=3, label='PSO-mTVAC')
# plt.plot(t, fitness_mTVACRW2, color='black', linewidth=3, label='PSO-mTVACRW')
# plt.plot(t, fitness_hTVAC2, color='yellow', linewidth=3, label='PSO-hTVAC')
plt.legend(loc="lower right", fontsize=16)
plt.yticks(fontsize='14')
plt.xticks(fontsize='14')
# plt.rcParams['savefig.dpi'] = 300 # 图片像素
# plt.rcParams['figure.dpi'] = 300 # 分辨率
plt.savefig('image/pso_m-rand_ms0.9-0.1TV/Erlotinib/pso-compare%d.png' % (1+run))
# plt.show()
plt.cla()