python实现粒⼦算法（PSO）优化神经⽹络超参数——以预测英雄联盟⽐赛结
果为例
⽬录
程序简介
本实验根据英雄联盟的对局数据，搭建全连接⽹络分类模型，以粒⼦算法对神经⽹络的节点数和dropout概率进⾏调优，最后对⽐默认模型和优化后的模型对英雄联盟⽐赛结果的预测准确率
粒⼦优化算法(PSO)是⼀种进化计算技术源于对鸟捕⾷的⾏为研究。粒⼦优化算法的基本思想：是通过体中个体之间的协作和信息共享来寻最优解。它的优点是收敛快、实现简单，缺点则是容易陷⼊局部最优
程序/数据集下载
代码分析
导⼊模块
from tensorflow.keras.layers import Input,Dense,Dropout,Activation
import matplotlib.pyplot as plt
from dels import load_model
from dels import Model
from tensorflow.keras.optimizers import Adam
ics import accuracy_score
import pandas as pd
import numpy as np
import json
from copy import deepcopy
数据是英雄联盟各对局10分钟的战况，英雄联盟对战⽅分为红蓝双⽅，blueWins就是指是否为蓝⽅获胜，其他列基本就是⼩龙、野怪、经济啥的，原谅我解释不清。。。读研的时候升到⽩银，因为出装问题每⼀局都要被骂，顺便说⼀下，我⽼是在⾥⾯装妹⼦，玩辅助，因为这样骂我的⼈就少了很多，好友已经满了┭┮﹏┭┮错的不是我，是这个世界
data = pd.read_csv("Static/high_diamond_ranked_10min.csv").iloc[:,1:]
print("数据尺⼨:",data.shape)
print("展⽰下数据前5⾏和前5列")
data.iloc[:,:5].head()
数据尺⼨: (9879, 39)
展⽰下数据前5⾏和前5列
blueWins blueWardsPlaced blueWardsDestroyed blueFirstBlood blueKills
0028219
1012105
2015007
3043104
4075406
切割数据集，分成训练、验证和测试，这⾥只⽤验证集给PSO调优⽤，并没有给神经⽹络训练保存最佳checkpoint
data = data.sample(frac=1.0)#打乱数据
trainData = data.iloc[:6000]#训练集
xTrain = trainData.values[:,1:]
yTrain = trainData.values[:,:1]
valData = data.iloc[6000:8000]#验证集
xVal = valData.values[:,1:]
yVal = valData.values[:,:1]
testData = data.iloc[8000:]#测试集
xTest = testData.values[:,1:]
yTest = testData.values[:,:1]
1个粒⼦即1个⽅案，在本实验中，1个粒⼦就是1个(节点数、dropout概率)的数组，PSO就是计算每个粒⼦对应⽅案的适应度，到最合适的⽅案的过程，下⽂的PSO类会根据粒⼦需要优化的特征对粒⼦迭代，⽀持⼩数或整数的特征，若为⾃定的离散区间，可在适应度函数中，将⾮法粒⼦的适应度给予惩罚值，实例化该类后，需要指定适应度函数给iterate函数对粒⼦⽅案进⾏迭代，本实验的粒⼦算法为最简单的形式，更新公式如下:
class PSO():
def __init__(self,featureNum,featureArea,featureLimit,featureType,particleNum=5,epochMax=10,c1=2,c2=2): '''
粒⼦算法
:param featureNum: 粒⼦特征数
:param featureArea: 特征上下限矩阵
:param featureLimit: 特征上下阙界，也是区间的开闭 0为不包含 1为包含
:param featureType: 特征类型 int float
:param particleNum: 粒⼦个数
:param epochMax: 最⼤迭代次数
:param c1: ⾃⾝认知学习因⼦
:param c2: 体认知学习因⼦
'''
#如上所⽰
self.featureNum = featureNum
self.featureArea = np.array(featureArea).reshape(featureNum,2)
self.featureLimit = np.array(featureLimit).reshape(featureNum,2)
self.featureType = featureType
self.particleNum = particleNum
self.epochMax = epochMax
self.c1 = c1
self.c2 = c2
self.epoch = 0#已迭代次数
#⾃⾝最优适应度记录
self.pBest = [-1e+10 for i in range(particleNum)]
self.pBestArgs = [None for i in range(particleNum)]
#全局最优适应度记录
self.gBest = -1e+10
self.gBestArgs = None
#初始化所有粒⼦
self.particles = [self.initParticle() for i in range(particleNum)]
#初始化所有粒⼦的学习速度
self.vs = [np.random.uniform(0,1,size=featureNum) for i in range(particleNum)]
#迭代历史
self.gHistory = {"特征%d"%i:[] for i in range(featureNum)}
self.gHistory["内平均"] = []
self.gHistory["全局最优"] = []
def standardValue(self,value,lowArea,upArea,lowLimit,upLimit,valueType):
'''
规范⼀个特征值，使其落在区间内
:param value: 特征值
:param lowArea: 下限
:param upArea: 上限
:param lowLimit: 下限开闭区间
:param upLimit: 上限开闭区间
:param valueType: 特征类型
:return: 修正后的值
'''
if value < lowArea:
value = lowArea
if value > upArea:
value = upArea
if valueType is int:
value = np.round(value,0)
#下限为闭区间
if value <= lowArea and lowLimit==0:
value = lowArea + 1
#上限为闭区间
if value >= upArea and upLimit==0:
value = upArea - 1
elif valueType is float:
#下限为闭区间
if value <= lowArea and lowLimit == 0:
value = lowArea + 1e-10
#上限为闭=间
if value >= upArea and upLimit==0:
value = upArea - 1e-10
return value
def initParticle(self):
'''随机初始化1个粒⼦'''
values = []
#初始化这么多特征数
for i in range(self.featureNum):
#该特征的上下限
lowArea = self.featureArea[i][0]
upArea = self.featureArea[i][1]
#该特征的上下阙界
lowLimit = self.featureLimit[i][0]
upLimit = self.featureLimit[i][1]
#随机值
value = np.random.uniform(0,1) * (upArea-lowArea) + lowArea
value = self.standardValue(value,lowArea,upArea,lowLimit,upLimit,self.featureType[i])
values.append(value)
return values
def iterate(self,calFitness):
'''
开始迭代
:param calFitness：适应度函数输⼊为1个粒⼦的所有特征和全局最佳适应度，输出为适应度
'''
while self.epoch<self.epochMax:
self.epoch += 1
for i,particle in enumerate(self.particles):
#该粒⼦的适应度
fitness = calFitness(particle,self.gBest)
#更新该粒⼦的⾃⾝认知最佳⽅案
if self.pBest[i] < fitness:
self.pBest[i] = fitness
self.pBestArgs[i] = deepcopy(particle)
#更新全局最佳⽅案
if self.gBest < fitness:
self.gBest = fitness
self.gBestArgs = deepcopy(particle)
#更新粒⼦
for i, particle in enumerate(self.particles):
#更新速度
self.vs[i] = np.array(self.vs[i]) + self.c1*np.random.uniform(0,1,size=self.featureNum)*(np.array(self.pBestArgs[i])-np.array(self.particles[i])) + self.c2*np.random.uniform(0,1,size=self.featureNum)*(np.array(self.gBestArgs)-np.array(self #更新特征值
self.particles[i] = np.array(particle) + self.vs[i]
#规范特征值
values = []
for j in range(self.featureNum):
#该特征的上下限
lowArea = self.featureArea[j][0]
upArea = self.featureArea[j][1]
#该特征的上下阙界
lowLimit = self.featureLimit[j][0]
upLimit = self.featureLimit[j][1]
#随机值
value =self.particles[i][j]
value = self.standardValue(value,lowArea,upArea,lowLimit,upLimit,self.featureType[j])
values.append(value)
self.particles[i] = values
#保存历史数据
for i in range(self.featureNum):
self.gHistory["特征%d"%i].append(self.gBestArgs[i])
self.gHistory["内平均"].an(self.pBest))
self.gHistory["全局最优"].append(self.gBest)
print("PSO epoch:%d/%d 内平均:%.4f 全局最优:%.4f"%(self.epoch,self.an(self.pBest),self.gBest))
buildNet函数根据⽹络节点数和dropout概率来构建⼀个简单的全连接分类⽹络，其输⼊特征数为38，输出特征数为1（当然，也可以选择⽹络层数、学习率等超参数来优化，为
⽅便学习，这⾥只选择这俩超参数进⾏实验）并对该⽹络进⾏训练
def buildNet(nodeNum,p):
'''
搭建全连接⽹络进⾏训练，返回模型和训练历史、验证集准确率和测试集准确率
:param nodeNum: ⽹络节点数
:param p: dropout概率
'''
#输⼊层 38个对局特征
inputLayer = Input(shape=(38,))
#中间层
middle = Dense(nodeNum)(inputLayer)
middle = Dropout(p)(middle)
#输出层⼆分类
outputLayer = Dense(1,activation="sigmoid")(middle)
#建模⼆分类损失
model = Model(inputs=inputLayer,outputs=outputLayer)
optimizer = Adam(lr=1e-3)
modelpile(optimizer=optimizer,loss="binary_crossentropy",metrics=['acc'])
#训练
history = model.fit(xTrain,yTrain,verbose=0,batch_size=1000,epochs=100,validation_data=(xVal,yVal)).history
#验证集准确率
valAcc = accuracy_score(yVal,model.predict(xVal).round(0))
#测试集准确率
testAcc = accuracy_score(yTest,model.predict(xTest).round(0))
return model,history,valAcc,testAcc
为了跟优化好的模型有所对⽐，这⾥我们训练⼀个默认参数的神经⽹络，它的超参数取值即各超参数区间的平均值，训练并打印⽹络结构和训练指标
nodeArea = [10,200]#节点数区间
pArea = [0,0.5]#dropout概率区间
#按区间平均值训练⼀个神经⽹络
nodeNum = an(nodeArea))
p = np.mean(pArea)
defaultNet,defaultHistory,defaultValAcc,defaultTestAcc = buildNet(nodeNum,p)
defaultNet.summary()
print("\n默认⽹络的节点数:%d dropout概率:%.2f 验证集准确率:%.4f 测试集准确率:%.4f"%(nodeNum,p,defaultValAcc,defaultTestAcc))
Model: "model_346"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================float up
input_347 (InputLayer) [(None, 38)] 0
_________________________________________________________________
dense_691 (Dense) (None, 105) 4095
_________________________________________________________________
dropout_346 (Dropout) (None, 105) 0
_________________________________________________________________
dense_692 (Dense) (None, 1) 106
=================================================================
Total params: 4,201
Trainable params: 4,201
Non-trainable params: 0
_________________________________________________________________
默认⽹络的节点数:105 dropout概率:0.25 验证集准确率:0.6535 测试集准确率:0.6578
实例化PSO模型，将区间信息输⼊，开始迭代，适应度函数就是输⼊1各粒⼦和全局最优适应度，返回该粒⼦对应⽅案的验证集准确率
featureNum = 2#2个需要优化的特征
featureArea = [nodeArea,pArea]#2个特征取值范围
featureLimit = [[1,1],[0,1]]#取值范围的开闭 0为闭区间 1为开区间
featureType = [int,float]#2个特征的类型
#粒⼦算法类
pso = PSO(featureNum,featureArea,featureLimit,featureType)
def calFitness(particle,gBest):
'''适应度函数，输⼊1个粒⼦的数组和全局最优适应度，返回该粒⼦对应的适应度'''
nodeNum,p = particle#取出粒⼦的特征值
net,history,valAcc,testAcc = buildNet(nodeNum,p)
#该粒⼦⽅案超过全局最优
if valAcc>gBest:
#保存模型和对应信息
net.save("Static/best.h5")
history = pd.DataFrame(history)
<_excel("Static/best.xlsx",index=None)
with open("Static/info.json","w") as f:
f.write(json.dumps({"valAcc":valAcc,"testAcc":testAcc}))
return valAcc
#开始⽤粒⼦算法迅游
pso.iterate(calFitness)
#载⼊最佳模型和对应的训练历史
bestNet = load_model("Static/best.h5")
with open("Static/info.json","r") as f:
info = json.ad())
bestValAcc = float(info["valAcc"])
bestTestAcc = float(info["testAcc"])
bestHistory = pd.read_excel("Static/best.xlsx")
print("最优模型的验证集准确率:%.4f 测试集准确率:%.4f"%(bestValAcc,bestTestAcc)) PSO epoch:1/10 内平均:0.7210 全局最优:0.7280
PSO epoch:2/10 内平均:0.7210 全局最优:0.7280
PSO epoch:3/10 内平均:0.7251 全局最优:0.7280
PSO epoch:4/10 内平均:0.7275 全局最优:0.7350
PSO epoch:5/10 内平均:0.7275 全局最优:0.7350
PSO epoch:6/10 内平均:0.7299 全局最优:0.7350
PSO epoch:7/10 内平均:0.7313 全局最优:0.7350
PSO epoch:8/10 内平均:0.7313 全局最优:0.7350
PSO epoch:9/10 内平均:0.7313 全局最优:0.7350
PSO epoch:10/10 内平均:0.7313 全局最优:0.7350
最优模型的验证集准确率:0.7350 测试集准确率:0.7350
查看PSO最优解随迭代次数的变换
history = pd.DataFrame(pso.gHistory)
history["epoch"] = range(1,history.shape[0]+1)
history
特征0特征1内平均全局最优epoch
050.00.2677060.72100.7281
150.00.2677060.72100.7282
250.00.2677060.72510.7283
357.00.2013360.72750.7354
457.00.2013360.72750.7355
557.00.2013360.72990.7356
657.00.2013360.73130.7357
757.00.2013360.73130.7358
857.00.2013360.73130.7359
957.00.2013360.73130.73510
对⽐下默认参数模型和PSO调优模型的准确率，是有点效果，仅供学习... fig, ax = plt.subplots()
x = np.arange(2)
a = [defaultValAcc,bestValAcc]
b = [defaultTestAcc,bestTestAcc]
total_width, n = 0.8, 2
width = total_width / n
x = x - (total_width - width) / 2
ax.bar(x, a, width=width, label='val',color="#00BFFF")
for x1,y1 in zip(x,a):
<(x1,y1+0.01,'%.3f' %y1, ha='center',va='bottom')
ax.bar(x + width, b, width=width, label='test',color="#FFA500")
for x1,y1 in zip(x,b):
<(x1+width,y1+0.01,'%.3f' %y1, ha='center',va='bottom')
ax.legend()
ax.set_xticks([0, 1])
ax.set_ylim([0,1.2])
ax.set_ylabel("acc")
ax.set_xticklabels(["default net","PSO-net"])
fig.savefig("Static/对⽐.png",dpi=250)
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