如何使用遗传算法优化参数
How to optimize parameters using genetic algorithms
我想在 R 中使用 GA 优化 eps 回归 (SVR) 中的三个参数(gamma、cost 和 epsilon)。这就是我所做的。
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$residuals
rmse <- function(error) #root mean sqaured error
{
sqrt(mean(error^2))
}
rmse(error)
这里我把cost、gamma和epsilon分别设置为0.1,但我觉得不是最好的值。所以,我想用遗传算法来优化这些参数。
GA <- ga(type = "real-valued", fitness = rmse,
min = c(0.1,3), max = c(0.1,3),
popSize = 50, maxiter = 100)
这里,我使用了RMSE作为适应度函数。但我认为适应度函数必须包括要优化的参数。但是,在 SVR 中,objective 函数太复杂了,无法用 R 代码写出来,我试图找到它很长时间但无济于事。同时了解SVR和GA的人,有使用GA优化SVR参数经验的人,请帮助我。请
在这样的应用程序中,传递其值要优化的参数(在您的情况下,cost
、gamma
和 epsilon
)作为适应度函数的参数,然后运行模型拟合+评估函数,并使用模型性能的度量作为适应度的度量。因此,objective 函数的显式形式并不直接相关。
在下面的实现中,我使用 5 折交叉验证来估计一组给定参数的 RMSE。特别是,由于包 GA
最大化适应度函数,我将给定参数值的适应度值写为减去交叉验证数据集的平均 rmse。因此,可以达到的最大适应度为零。
这里是:
library(e1071)
library(GA)
data(Ozone, package="mlbench")
Data <- na.omit(Ozone)
# Setup the data for cross-validation
K = 5 # 5-fold cross-validation
fold_inds <- sample(1:K, nrow(Data), replace = TRUE)
lst_CV_data <- lapply(1:K, function(i) list(
train_data = Data[fold_inds != i, , drop = FALSE],
test_data = Data[fold_inds == i, , drop = FALSE]))
# Given the values of parameters 'cost', 'gamma' and 'epsilon', return the rmse of the model over the test data
evalParams <- function(train_data, test_data, cost, gamma, epsilon) {
# Train
model <- svm(V4 ~ ., data = train_data, cost = cost, gamma = gamma, epsilon = epsilon, type = "eps-regression", kernel = "radial")
# Test
rmse <- mean((predict(model, newdata = test_data) - test_data$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)
产生结果(针对我指定的参数值范围,可能需要根据数据微调):
GA results:
Iterations = 100
Fitness function value = -14.66315
Solution =
cost gamma epsilon
[1,] 2.643109 0.07910103 0.09864132
我想在 R 中使用 GA 优化 eps 回归 (SVR) 中的三个参数(gamma、cost 和 epsilon)。这就是我所做的。
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$residuals
rmse <- function(error) #root mean sqaured error
{
sqrt(mean(error^2))
}
rmse(error)
这里我把cost、gamma和epsilon分别设置为0.1,但我觉得不是最好的值。所以,我想用遗传算法来优化这些参数。
GA <- ga(type = "real-valued", fitness = rmse,
min = c(0.1,3), max = c(0.1,3),
popSize = 50, maxiter = 100)
这里,我使用了RMSE作为适应度函数。但我认为适应度函数必须包括要优化的参数。但是,在 SVR 中,objective 函数太复杂了,无法用 R 代码写出来,我试图找到它很长时间但无济于事。同时了解SVR和GA的人,有使用GA优化SVR参数经验的人,请帮助我。请
在这样的应用程序中,传递其值要优化的参数(在您的情况下,cost
、gamma
和 epsilon
)作为适应度函数的参数,然后运行模型拟合+评估函数,并使用模型性能的度量作为适应度的度量。因此,objective 函数的显式形式并不直接相关。
在下面的实现中,我使用 5 折交叉验证来估计一组给定参数的 RMSE。特别是,由于包 GA
最大化适应度函数,我将给定参数值的适应度值写为减去交叉验证数据集的平均 rmse。因此,可以达到的最大适应度为零。
这里是:
library(e1071)
library(GA)
data(Ozone, package="mlbench")
Data <- na.omit(Ozone)
# Setup the data for cross-validation
K = 5 # 5-fold cross-validation
fold_inds <- sample(1:K, nrow(Data), replace = TRUE)
lst_CV_data <- lapply(1:K, function(i) list(
train_data = Data[fold_inds != i, , drop = FALSE],
test_data = Data[fold_inds == i, , drop = FALSE]))
# Given the values of parameters 'cost', 'gamma' and 'epsilon', return the rmse of the model over the test data
evalParams <- function(train_data, test_data, cost, gamma, epsilon) {
# Train
model <- svm(V4 ~ ., data = train_data, cost = cost, gamma = gamma, epsilon = epsilon, type = "eps-regression", kernel = "radial")
# Test
rmse <- mean((predict(model, newdata = test_data) - test_data$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)
产生结果(针对我指定的参数值范围,可能需要根据数据微调):
GA results:
Iterations = 100
Fitness function value = -14.66315
Solution =
cost gamma epsilon
[1,] 2.643109 0.07910103 0.09864132