R/NNSummary.R

In pkR-pkR/NNbenchmark: Datasets and Functions to Benchmark Neural Network Packages

## 2020-06-18


#' @title Summarize Calculations of RMSE, MSE, MAE, and WAE
#' @description
#' Summarize measures of fit and time for a single training. Measures of fit 
#' include the Root Mean Squared Error (RMSE), the Mean Squared Error (MSE), 
#' the Mean Absolute Error (MAE), and the Worst Absolute Error (WAE) rounded
#' by default to 4 digits and set to \code{na.rm = TRUE}. See more at \code{\link{funRMSE}}. 
#' The summary can also include the results of time from \code{\link{getTimer}} in
#' NNbenchmark or the result of \code{\link{timediff}}.
#' 
#' @param   y_pred  numeric vector of the predicted values
#' @param   y0      numeric vector of the observed values
#' @param   time    numeric value of time 
#' @param   dgts    integer value for how many digits to round to, see \code{\link{round}}
#' @return  
#' A vector of RMSE, MSE, MAE, WAE, and time values for a single iteration.
#' 
#' @examples
#' ## With 2019 legacy code, no longer usable with 2020 trainPredict
#' old <- options("digits.secs" = 4)
#' timeTT <- createTimer()
#' 
#' timeTT$start("event")
#' y0 <- 1:19
#' y_pred <- y0 + rnorm(length(y0), sd = 0.3)
#' timeTT$stop("event") 
#' 
#' time <- getTimer(timeTT)
#' 
#' NNsummary(y_pred, y0, time[,4], 4)
#' 
#' ## With 2020 code
#' timestart()
#' y0 <- 1:19
#' y_pred <- y0 + rnorm(length(y0), sd = 0.3)
#' time <- timediff()
#'
#' NNsummary(y_pred, y0, time, 4)
#'
#' options(old)      
#'   
#' @export
#' @name NNsummary
NNsummary <- function(y_pred, y0, time, dgts = 4) {
  c(RMSE = funRMSE(y_pred, y0, dgts), 
    MSE  = funMSE( y_pred, y0, dgts), 
    MAE  = funMAE( y_pred, y0, dgts), 
    WAE  = funWAE( y_pred, y0, dgts), 
    time = round(time, dgts))
}