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svm_pso_predictor_K.py
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svm_pso_predictor_K.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
# 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_K:
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.wS = 0.9
self.wE = 0.4
self.c1 = 2.05
self.c2 = 2.05
self.L = self.c1 + self.c2
self.K = 2 / (abs(2 - self.L - cmath.sqrt(self.L ** 2 - 4 * self.L))) # 收敛因子
self.r1 = random.uniform(0, 1)
# self.r1 = 0.6
self.r2 = random.uniform(0, 1)
# self.r2 = 0.3
self.max_v = 10 # 最大速度
self.pN = pN # 粒子数量
self.dim = dim # 搜索维度
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(0, 10) # 位置的初始范围
self.V[i][j] = random.uniform(0, 10) # 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)
for i in range(self.pN): # 这里不需要对维度遍历，因为numpy是一一对应的
self.V[i] = self.K * (self.V[i] + self.c1 * self.r1 * (self.pbest[i] - self.X[i]) +
self.c2 * self.r2 * (self.gbest - self.X[i]))
self.V[i] = [self.max_v if v > self.max_v else v for v in self.V[i]] # 当速度大于最大速度时，赋值为最大速度
self.X[i] = self.X[i] + self.V[i]
fitness.append(self.fit)
print('V: ', self.V[0], end=" ")
print('X: ', self.X[0], end=" ")
print(self.fit, end=" ") # 输出最优值
print('PSO-K 当前迭代次数：', iter)
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()