Nothing
#------------------------------------------------------------------------------
# EN regression hjn function
#------------------------------------------------------------------------------
en.reg.hjn <- function(x = x, y = y, cross = cross, fast = fast, loss = loss) {
dat <- cbind(y, x)
x <- as.matrix(dummy(x))
# initialize list
results <- list()
#------------------------------------------------------------------------------
# EN regression resub function
#------------------------------------------------------------------------------
# Function for regular speed
en.reg.opt.resub <- function(params, x, y, loss){
pr <- NULL
try(pr <- glmnet::glmnet(x, y, family = "gaussian", alpha = params[1],
lambda = exp(params[2])))
if(!is.null(pr)){
pred <- stats::predict(pr, newx = x, type = "response")
l <- loss.reg(pred = pred, true_y = y, loss = loss)
} else {
l <- 1e+150
}
l
}
#------------------------------------------------------------------------------
# EN regression CV function
#------------------------------------------------------------------------------
en.reg.cv <- function(x, y, cross, alpha, lambda, loss) {
yval <- rep(0, nrow(x))
xvs <- rep(1:cross, length = nrow(x))
xvs <- sample(xvs)
cv.acc <- rep(0, cross)
for(i in 1:cross) {
train.x <- x[xvs != i, ]
train.y <- y[xvs != i]
test.x <- x[xvs == i, ]
test.y <- y[xvs == i]
en.t <- glmnet::glmnet(train.x, train.y, distribution = "gaussian", alpha = alpha,
lambda = exp(lambda))
yval[xvs == i] <- stats::predict(en.t, newx = test.x, type="response")
}
l <- loss.reg(pred = yval, true_y = y, loss = loss)
l
}
en.reg.opt.cv <- function(params, x, y, cross, loss) {
pr <- NULL
try(pr <- en.reg.cv(x = x, y = y, cross = cross, alpha = params[1],
lambda = params[2], loss = loss))
if(!is.null(pr)){
l <- pr
} else {
l <- 1e+150
}
l
}
#------------------------------------------------------------------------------
# EN regression fast functions
#------------------------------------------------------------------------------
en.reg.pred.fast <- function(x, y, n, alpha, lambda, loss) {
dat <- cbind(y, x)
dat2 <- dat[sample(nrow(dat)), ]
train <- dat2[c(1:n), ]
test <- dat2[-c(1:n), ]
en.t <- glmnet::glmnet(train[, -1], train[, 1], family = "gaussian",
alpha = alpha, lambda = exp(lambda))
pred <- stats::predict(en.t, newx = test[, -1], type="response")
loss.reg(pred = pred, true_y = test[, 1], loss = loss)
}
en.reg.opt.fast <- function(params, x, y, n, loss){
pr <- NULL
try(pr <- en.reg.pred.fast(x = x, y = y, n = n, alpha = params[1],
lambda = params[2], loss))
if(!is.null(pr)){
l <- pr
} else {
l <- 1e+150
}
l
}
# setup fitness function based on user inputs
if(is.null(cross) & !fast) {
fit <- function(p) {en.reg.opt.resub(p, x, y, loss)}
} else if (fast > 0) {
if(fast > 1) {
n <- fast
} else if(fast < 1) {
n <- round(fast * nrow(dat))
} else {
n <- find.n(dat, fast)
}
fit <- function(p) {en.reg.opt.fast(p, x, y, n, loss)}
results$n <- n
} else if(!is.null(cross)) {
if(cross >= 2) {
fit <- function(p) {en.reg.opt.cv(p, x, y, cross, loss)}
} else {
stop("Invalid number of folds for cross-validation. Use integer > 1.")
}
results$nfold <- cross
} else {
warning("Invalid option for fast. Default for fast used in computations.")
n <- find.n(dat, fast)
fit <- function(p) {en.reg.opt.fast(p, x, y, n, loss)}
results$n <- n
}
lmin <- log(min(glmnet::cv.glmnet(x, y, family = "gaussian", alpha = 1)$lambda))
lmax <- log(max(glmnet::cv.glmnet(x, y, family = "gaussian", alpha = 0)$lambda)) / 2
lstart <- lmin + (lmax - lmin) / 3
hjn.obj <- optimx::hjn(par = c(0.75, lstart), fn = fit, lower = c(0, lmin),
upper = c(1, lmax))
results$alpha <- hjn.obj$par[1]
results$lambda <- exp(hjn.obj$par[2])
results$loss <- as.numeric(hjn.obj$value)
results$model <- glmnet::glmnet(x, y, family = "gaussian",
alpha = results$alpha,
lambda = results$lambda)
results
}
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