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# Do this in a separate file to see the generated help:
#library(devtools)
#document()
#load_all(as.package("../../onlineforecast"))
#?rls_optim
#' Optimize parameters (transformation stage) of RLS model
#'
#' This is a wrapper for \code{\link{optim}} to enable easy use of bounds and caching in the optimization.
#'
#' @title Optimize parameters for onlineforecast model fitted with RLS
#' @param model The onlineforecast model, including inputs, output, kseq, p
#' @param data The data.list including the variables used in the model.
#' @param scorefun The function to be score used for calculating the score to be optimized.
#' @param cachedir A character specifying the path (and prefix) of the cache file name. If set to \code{""}, then no cache will be loaded or written.
#' @param printout A logical determining if the score function is printed out in each iteration of the optimization.
#' @param method The method argument for \code{\link{optim}}.
#' @param ... Additional parameters to \code{\link{optim}}
#' @return Result object of optim().
#' Parameters resulting from the optimization can be found from \code{result$par}
#' @seealso \code{link{optim}} for how to control the optimization.
#' @examples
#'
#' # Take data (See vignette ??(ref) for better model and more details)
#' D <- subset(Dbuilding, c("2010-12-15", "2011-01-01"))
#' D$y <- D$heatload
#' # Define a model
#' model <- forecastmodel$new()
#' model$add_inputs(Ta = "Ta", mu = "ones()")
#' model$add_regprm("rls_prm(lambda=0.99)")
#'
#' # Before fitting the model, define which points to include in the evaluation of the score function
#' D$scoreperiod <- in_range("2010-12-20", D$t)
#' # And the sequence of horizons to fit for
#' model$kseq <- 1:6
#' # Now we can fit the model and get the score, as it is
#' rls_fit(model=model, data=D, scorefun=rmse, returnanalysis=FALSE)
#' # Or we can change the lambda
#' rls_fit(c(lambda=0.9), model, D, rmse, returnanalysis=FALSE)
#'
#' # This could be passed to optim() (or any optimizer, see forecastmodel$insert_prm()).
#' optim(c(lambda=0.98), rls_fit, model=model, data=D, scorefun=rmse, returnanalysis=FALSE,
#' lower=c(lambda=0.9), upper=c(lambda=0.999), method="L-BFGS-B")
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#'
#' # rls_optim is simply a helper, it's makes using bounds easiere and enables caching of the results
#' # First add bounds for lambda (lower, init, upper)
#' model$add_prmbounds(lambda = c(0.9, 0.98, 0.999))
#'
#' # Now the same optimization as above can be done by
#' val <- rls_optim(model, D)
#' val
#'
#' # Caching can be done by providing a path (try rerunning and see the file in "cache" folder)
#' val <- rls_optim(model, D, cachedir="cache")
#' val
#'
#' # If anything affecting the results are changed, then the cache is not loaded
#' model$add_prmbounds(lambda = c(0.89, 0.98, 0.999))
#' val <- rls_optim(model, D, cachedir="cache")
#'
#' # To delete the cache
#' file.remove(dir("cache", full.names=TRUE))
#' file.remove("cache")
#'
#' @export
rls_optim <- function(model, data, scorefun = rmse, cachedir="", printout=TRUE, method="L-BFGS-B", ...){
# Take the parameters bounds from the parameter bounds set in the model
init <- model$get_prmbounds("init")
lower <- model$get_prmbounds("lower")
upper <- model$get_prmbounds("upper")
# If bounds are NA, then set
if(any(is.na(lower))){ lower[is.na(lower)] <- -Inf}
if(any(is.na(upper))){ lower[is.na(upper)] <- Inf}
# Caching the results based on some of the function arguments
if(cachedir != ""){
# Have to insert the parameters in the expressions to get the right state of the model for unique checksum
model$insert_prm(init)
# Give all the elements needed to calculate the unique cache name
# This is maybe smarter, don't have to calculate the transformation of the data: cnm <- cache_name(model$regprm, getse(model$inputs, nms="expr"), model$output, model$prmbounds, model$kseq, data, objfun, init, lower, upper, cachedir = cachedir)
# Have to reset the state first to remove dependency of previous calls
model$reset_state()
cnm <- cache_name(rls_fit, rls_optim, model$outputrange, model$regprm, model$transform_data(data), data[[model$output]], scorefun, init, lower, upper, cachedir = cachedir)
# Maybe load the cached result
if(file.exists(cnm)){ return(readRDS(cnm)) }
}
# Run the optimization
res <- optim(par = init,
fn = rls_fit,
# Parameters to pass to rls_fit
model = model,
data = data,
scorefun = scorefun,
printout = printout,
returnanalysis = FALSE,
# Parameters to pass to optim
lower = lower,
upper = upper,
method = method,
...)
# Save the result in the cachedir
if(cachedir != ""){ cache_save(res, cnm) }
# Return the result
return(res)
}