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svm_pso_predictor_hTVAC.py
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svm_pso_predictor_hTVAC.py
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# 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 sklearn.model_selection import cross_val_score
from sklearn.model_selection import ShuffleSplit
# 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_HTVAC():
def __init__(self, max_iter, data_X, data_y, pN=30, dim=2, n_splits=3):
self.data_X = data_X
self.data_y = data_y
self.n_splits = n_splits
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.r5 = random.uniform(0, 1)
self.pN = pN # 粒子数量
self.dim = dim # 搜索维度
self.maxC = 1000 # 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.rv = random.uniform(0, self.max_v[random.randint(0, 1)])
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
cv = ShuffleSplit(n_splits=self.n_splits, test_size=.4, random_state=0)
score = cross_val_score(model, self.data_X, self.data_y, cv=cv)
print(score)
return -score.mean()
# ---------------------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]
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.r5 = random.uniform(0, 1)
for i in range(self.pN):
for d in range(self.dim): # 对维度遍历
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])
self.rv = random.uniform(0, self.max_v[d])
if self.V[i][d] == 0:
if self.r3 < 0.5:
self.V[i][d] = self.r4 * self.rv
else:
self.V[i][d] = -self.r5 * self.rv
# 限制速度
self.V[i][d] = np.sign(self.V[i][d]) * min(abs(self.V[i][d]), self.max_v[d])
# self.V[i][d] = self.max_v if self.V[i][d] > self.max_v else self.V[i][d] # 当速度大于最大速度时，赋值为最大速度
self.X[i][d] = self.X[i][d] + self.V[i][d]
# 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)
print('V: %.5f,%.5f' % (self.V[0][0], self.V[0][1]), end="\t")
print('X: %.5f,%.5f' % (self.X[0][0], self.X[0][1]), end="\t")
print('fit: %.4f' % self.fit, end="\t") # 输出最优值
print('gBest: %.5f,%.5f' % (self.gbest[0], self.gbest[1]), end="\t") # 输出gBest
print('PSO-hTVAC 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
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()