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Conformal Kernel Ridge Regression on small data
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# rm(list=ls()) | |
install.packages('learningmachine', | |
repos = c('https://techtonique.r-universe.dev', | |
'https://cloud.r-project.org')) | |
# or | |
# install.packages("remotes", repos = c(CRAN="https://cloud.r-project.org")) | |
#remotes::install_github("Techtonique/learningmachine") | |
library(learningmachine) | |
## Data ----------------------------------------------------------------------------- | |
X <- as.matrix(mtcars[,-1]) | |
y <- mtcars$mpg | |
set.seed(123) | |
(index_train <- base::sample.int(n = nrow(X), | |
size = floor(0.8*nrow(X)), | |
replace = FALSE)) | |
X_train <- X[index_train, ] | |
y_train <- y[index_train] | |
X_test <- X[-index_train, ] | |
y_test <- y[-index_train] | |
dim(X_train) | |
dim(X_test) | |
## Kernel Ridge Regressor (KRR) object ----------------------------------------------------------------------------- | |
obj <- learningmachine::KernelRidgeRegressor$new() | |
obj$get_type() | |
obj$get_name() | |
## Fit KRR ----------------------------------------------------------------------------- | |
t0 <- proc.time()[3] | |
obj$fit(X_train, y_train, lambda = 0.05) | |
cat("Elapsed: ", proc.time()[3] - t0, "s \n") | |
## Test RMSE ----------------------------------------------------------------------------- | |
print(sqrt(mean((obj$predict(X_test) - y_test)^2))) | |
## Graph ------------------------------------------------------------ | |
res <- obj$predict(X = X_test, level = 95, | |
method = "splitconformal") | |
res2 <- obj$predict(X = X_test, level = 95, | |
method = "jackknifeplus") | |
par(mfrow=c(1, 2)) | |
plot(c(y_train, res$preds), type='l', | |
main="split conformal", | |
ylab="", | |
ylim = c(3, 33)) | |
lines(c(y_train, res$upper), col="gray60") | |
lines(c(y_train, res$lower), col="gray60") | |
lines(c(y_train, res$preds), col = "red") | |
lines(c(y_train, y_test), col = "blue") | |
plot(c(y_train, res2$preds), type='l', | |
main="jackknife +", | |
ylab="", | |
ylim = c(3, 33)) | |
lines(c(y_train, res2$upper), col="gray60") | |
lines(c(y_train, res2$lower), col="gray60") | |
lines(c(y_train, res2$preds), col = "red") | |
lines(c(y_train, y_test), col = "blue") | |
# coverage rate | |
mean((y_test >= as.numeric(res$lower)) * (y_test <= as.numeric(res$upper))) | |
mean((y_test >= as.numeric(res2$lower)) * (y_test <= as.numeric(res2$upper))) |
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