如何使用库(插入符号)更改指标?
How to change metrics using the library(caret)?
我想使用
将指标从 RMSE 更改为 RMSLE
caret library
给定一些示例数据:
ivar1<-rnorm(500, mean = 3, sd = 1)
ivar2<-rnorm(500, mean = 4, sd = 1)
ivar3<-rnorm(500, mean = 5, sd = 1)
ivar4<-rnorm(500, mean = 4, sd = 1)
dvar<-rpois(500, exp(3+ 0.1*ivar1 - 0.25*ivar2))
data<-data.frame(dvar,ivar4,ivar3,ivar2,ivar1)
ctrl <- rfeControl(functions=rfFuncs,
method="cv",
repeats = 5,
verbose = FALSE,
number=5)
model <- rfe(data[,2:4], data[,1], sizes=c(1:4), rfeControl=ctrl)
这里我想改成RMSLE并保持图的思路
plot <-ggplot(model,type=c("g", "o"), metric="RMSE")+ scale_x_continuous(breaks = 2:4, labels = names(data)[2:4])
我不确定如何/是否可以轻松地将 RMSE 转换为 RMSLE,因此您可以尝试更改控制函数。
看看rfFuncs$summary
它调用了一个函数postResample
。这是计算 RMSE 的地方 - 查看
部分
mse <- mean((pred - obs)^2)
n <- length(obs)
out <- c(sqrt(mse), resamplCor^2)
因此您可以修改此函数来计算 RMSLE:
msle <- mean((log(pred) - log(obs))^2)
out <- sqrt(msle)
}
names(out) <- "RMSLE"
然后如果这个修改后的函数已经保存在一个叫做mypostResample
的函数中,那么你需要更新rfFuncs$summary
.
所以一共:
首先更新汇总函数 - 这将使用 RMSLE 调用新函数
newSumm <- function (data, lev = NULL, model = NULL)
{
if (is.character(data$obs))
data$obs <- factor(data$obs, levels = lev)
mypostResample(data[, "pred"], data[, "obs"])
}
然后定义新函数来计算 RMSLE
mypostResample <- function (pred, obs)
{
isNA <- is.na(pred)
pred <- pred[!isNA]
obs <- obs[!isNA]
msle <- mean((log(pred) - log(obs))^2)
out <- sqrt(msle)
names(out) <- "RMSLE"
if (any(is.nan(out)))
out[is.nan(out)] <- NA
out
}
更新 rfFuncs
# keep old settings for future use
oldSumm <- rfFuncs$summary
# update with new function
rfFuncs$summary <- newSumm
ctrl <- rfeControl(functions=rfFuncs,
method="cv",
repeats = 5,
verbose = FALSE,
number=5)
set.seed(1)
model <- rfe(data[,2:4], data[,1], sizes=c(1:4), rfeControl=ctrl, metric="RMSLE")
# plot
ggplot(model,type=c("g", "o"), metric="RMSLE")+ scale_x_continuous(breaks = 2:4, labels = names(data)[2:4])
我想使用
将指标从 RMSE 更改为 RMSLE caret library
给定一些示例数据:
ivar1<-rnorm(500, mean = 3, sd = 1)
ivar2<-rnorm(500, mean = 4, sd = 1)
ivar3<-rnorm(500, mean = 5, sd = 1)
ivar4<-rnorm(500, mean = 4, sd = 1)
dvar<-rpois(500, exp(3+ 0.1*ivar1 - 0.25*ivar2))
data<-data.frame(dvar,ivar4,ivar3,ivar2,ivar1)
ctrl <- rfeControl(functions=rfFuncs,
method="cv",
repeats = 5,
verbose = FALSE,
number=5)
model <- rfe(data[,2:4], data[,1], sizes=c(1:4), rfeControl=ctrl)
这里我想改成RMSLE并保持图的思路
plot <-ggplot(model,type=c("g", "o"), metric="RMSE")+ scale_x_continuous(breaks = 2:4, labels = names(data)[2:4])
我不确定如何/是否可以轻松地将 RMSE 转换为 RMSLE,因此您可以尝试更改控制函数。
看看rfFuncs$summary
它调用了一个函数postResample
。这是计算 RMSE 的地方 - 查看
mse <- mean((pred - obs)^2)
n <- length(obs)
out <- c(sqrt(mse), resamplCor^2)
因此您可以修改此函数来计算 RMSLE:
msle <- mean((log(pred) - log(obs))^2)
out <- sqrt(msle)
}
names(out) <- "RMSLE"
然后如果这个修改后的函数已经保存在一个叫做mypostResample
的函数中,那么你需要更新rfFuncs$summary
.
所以一共:
首先更新汇总函数 - 这将使用 RMSLE 调用新函数
newSumm <- function (data, lev = NULL, model = NULL)
{
if (is.character(data$obs))
data$obs <- factor(data$obs, levels = lev)
mypostResample(data[, "pred"], data[, "obs"])
}
然后定义新函数来计算 RMSLE
mypostResample <- function (pred, obs)
{
isNA <- is.na(pred)
pred <- pred[!isNA]
obs <- obs[!isNA]
msle <- mean((log(pred) - log(obs))^2)
out <- sqrt(msle)
names(out) <- "RMSLE"
if (any(is.nan(out)))
out[is.nan(out)] <- NA
out
}
更新 rfFuncs
# keep old settings for future use
oldSumm <- rfFuncs$summary
# update with new function
rfFuncs$summary <- newSumm
ctrl <- rfeControl(functions=rfFuncs,
method="cv",
repeats = 5,
verbose = FALSE,
number=5)
set.seed(1)
model <- rfe(data[,2:4], data[,1], sizes=c(1:4), rfeControl=ctrl, metric="RMSLE")
# plot
ggplot(model,type=c("g", "o"), metric="RMSLE")+ scale_x_continuous(breaks = 2:4, labels = names(data)[2:4])