R/GD_k fold cv_sequential.R

In FrancescoBarile/FJLPackage: OurPackage

#' Sequential method to k-fold crossing validation
#' 
#' cvsequencial is used to parallelize the k-fold cross validation

#' @param k [numeric] number of fold to create
#' @param formula [character] a symbolic description of the model to be fitted (as lm), with -1 if the intercept is not included
#' @param df [numeric] dataset containing the variables of interest
#' @param tolerance [numeric] tolerance level, stopping criteria of the algorithm (error<tolerance: stop)
#' @param maxit [numeric] maximum number of iterations, used if the stopping criteria is never matched
#' @param stepsize [numeric] learning parameter, to update the parameters at each iteration 
#' @param verbose [logical] indicating if the function write messages during the computation
#'
#' @import dplyr
#' @import tidyr
#' @import tibble
#' @import stringr
#' @returns [numeric] a vector of sample mean square errors calculated
#' @export
#' 

###### using sequential method 

#### divide the data into k parts
### where k means k-fold cross validation

cvsequencial<-function(k,formula, df, tolerance, maxit, stepsize, verbose){
  n=dim(df)[1]
  set.seed(2021)
  subset=rep(sample(1:k,k),floor(n/k)) ## k-fold cross validation
  se=0
  for(index in 1:k){
    train=lm_GD_optimizer(formula,df[c(1:n)[subset!=index],], tolerance, maxit, stepsize, verbose)
    ypred=y_pred(formula,train,df[c(1:n)[subset==index],])
    data=data_xy(formula,df[c(1:n)[subset==index],])
    se=se+sum((data$Y-ypred)^2) ## to calculate MSE 
  }
  mse=se/n #### k,'fold',':MSE is', mse
  return(mse)
  
}