R 如何利用遗传算法优化参数
我想使用R中的GA优化eps回归(SVR)中的三个参数(gamma、cost和epsilon)R 如何利用遗传算法优化参数,r,optimization,genetic-algorithm,R,Optimization,Genetic Algorithm,我想使用R中的GA优化eps回归(SVR)中的三个参数(gamma、cost和epsilon) library(e1071) data(Ozone, package="mlbench") a<-na.omit(Ozone) index<-sample(1:nrow(a), trunc(nrow(a)/3)) trainset<-a[index,] testset<-a[-index,] model<-svm(V4 ~ .,data=trainse
library(e1071)
data(Ozone, package="mlbench")
a<-na.omit(Ozone)
index<-sample(1:nrow(a), trunc(nrow(a)/3))
trainset<-a[index,]
testset<-a[-index,]
model<-svm(V4 ~ .,data=trainset, cost=0.1, gamma=0.1, epsilon=0.1, type="eps-regression", kernel="radial")
error<-model$residuals
rmse <- function(error) #root mean sqaured error
{
sqrt(mean(error^2))
}
rmse(error)
库(e1071)
数据(臭氧,包装=“mlbench”)
a在这种应用程序中,将其值要优化的参数(在您的情况下,成本
,伽马
和ε
)作为适应度函数的参数,然后运行模型拟合+评估函数,并使用模型性能度量作为适应度度量。因此,目标函数的显式形式并不直接相关
在下面的实现中,我使用5倍交叉验证来估计给定参数集的RMSE。特别是,由于packageGA
最大化了适应度函数,我将给定参数值的适应度值写为减去交叉验证数据集上的平均rmse。因此,可以达到的最大适应度为零
这是:
library(e1071)
library(GA)
data(Ozone, package="mlbench")
Data <- na.omit(Ozone)
# Setup the data for cross-validation
K = 5 # 5-fold cross-validation
fold_inds <- sample(1:K, nrow(Data), replace = TRUE)
lst_CV_data <- lapply(1:K, function(i) list(
train_data = Data[fold_inds != i, , drop = FALSE],
test_data = Data[fold_inds == i, , drop = FALSE]))
# Given the values of parameters 'cost', 'gamma' and 'epsilon', return the rmse of the model over the test data
evalParams <- function(train_data, test_data, cost, gamma, epsilon) {
# Train
model <- svm(V4 ~ ., data = train_data, cost = cost, gamma = gamma, epsilon = epsilon, type = "eps-regression", kernel = "radial")
# Test
rmse <- mean((predict(model, newdata = test_data) - test_data$V4) ^ 2)
return (rmse)
}
# Fitness function (to be maximized)
# Parameter vector x is: (cost, gamma, epsilon)
fitnessFunc <- function(x, Lst_CV_Data) {
# Retrieve the SVM parameters
cost_val <- x[1]
gamma_val <- x[2]
epsilon_val <- x[3]
# Use cross-validation to estimate the RMSE for each split of the dataset
rmse_vals <- sapply(Lst_CV_Data, function(in_data) with(in_data,
evalParams(train_data, test_data, cost_val, gamma_val, epsilon_val)))
# As fitness measure, return minus the average rmse (over the cross-validation folds),
# so that by maximizing fitness we are minimizing the rmse
return (-mean(rmse_vals))
}
# Range of the parameter values to be tested
# Parameters are: (cost, gamma, epsilon)
theta_min <- c(cost = 1e-4, gamma = 1e-3, epsilon = 1e-2)
theta_max <- c(cost = 10, gamma = 2, epsilon = 2)
# Run the genetic algorithm
results <- ga(type = "real-valued", fitness = fitnessFunc, lst_CV_data,
names = names(theta_min),
min = theta_min, max = theta_max,
popSize = 50, maxiter = 10)
summary(results)
非常感谢你!代码用于臭氧数据。然而,如果我从臭氧数据中删除了一些行,或者如果我更改了特定列中的数字,它将不起作用,并给出“predict.svm中的错误(ret,xhold,decision.values=TRUE):模型为空!”错误。我怎样才能解决这个问题?
library(e1071)
library(GA)
data(Ozone, package="mlbench")
Data <- na.omit(Ozone)
# Setup the data for cross-validation
K = 5 # 5-fold cross-validation
fold_inds <- sample(1:K, nrow(Data), replace = TRUE)
lst_CV_data <- lapply(1:K, function(i) list(
train_data = Data[fold_inds != i, , drop = FALSE],
test_data = Data[fold_inds == i, , drop = FALSE]))
# Given the values of parameters 'cost', 'gamma' and 'epsilon', return the rmse of the model over the test data
evalParams <- function(train_data, test_data, cost, gamma, epsilon) {
# Train
model <- svm(V4 ~ ., data = train_data, cost = cost, gamma = gamma, epsilon = epsilon, type = "eps-regression", kernel = "radial")
# Test
rmse <- mean((predict(model, newdata = test_data) - test_data$V4) ^ 2)
return (rmse)
}
# Fitness function (to be maximized)
# Parameter vector x is: (cost, gamma, epsilon)
fitnessFunc <- function(x, Lst_CV_Data) {
# Retrieve the SVM parameters
cost_val <- x[1]
gamma_val <- x[2]
epsilon_val <- x[3]
# Use cross-validation to estimate the RMSE for each split of the dataset
rmse_vals <- sapply(Lst_CV_Data, function(in_data) with(in_data,
evalParams(train_data, test_data, cost_val, gamma_val, epsilon_val)))
# As fitness measure, return minus the average rmse (over the cross-validation folds),
# so that by maximizing fitness we are minimizing the rmse
return (-mean(rmse_vals))
}
# Range of the parameter values to be tested
# Parameters are: (cost, gamma, epsilon)
theta_min <- c(cost = 1e-4, gamma = 1e-3, epsilon = 1e-2)
theta_max <- c(cost = 10, gamma = 2, epsilon = 2)
# Run the genetic algorithm
results <- ga(type = "real-valued", fitness = fitnessFunc, lst_CV_data,
names = names(theta_min),
min = theta_min, max = theta_max,
popSize = 50, maxiter = 10)
summary(results)
GA results:
Iterations = 100
Fitness function value = -14.66315
Solution =
cost gamma epsilon
[1,] 2.643109 0.07910103 0.09864132