R/regression.R

In elpidiofilho/labgeo: Collection of functions to fit models with emphasis in land use and soil mapping

# Ajusta Regressao
#'
#'
#' This function performs the training of the chosen regressor
#' @param df.train   Training dataframe
#' @param formula   A formula of the form y ~ x1 + x2 + ... If users don't inform formula, the first column will be used as Y values and the others columns with x1,x2....xn
#' @param preprocess pre process
#' @param regressor Choice of regressor to be used to train model. Uses  algortims names from Caret package.
#' @param rsample resample method 'boot', 'boot632', 'optimism_boot', 'boot_all', 'cv', 'repeatedcv', 'LOOCV', 'LGOCV','none', 'oob', 'timeslice', 'adaptive_cv', 'adaptive_boot', 'adaptive_LGOCV'
#' @param nfolds Number of folds to be build in crossvalidation
#' @param repeats repeats
#' @param index index
#' @param cpu_cores  Number of CPU cores to be used in parallel processing
#' @param tune_length  This argument is the number of levels for each tuning parameters that should be generated by train
#' @param search search option "grid" or  "random"
#' @param metric metric used to evaluate model fit. For numeric outcome ("RMSE", "Rsquared)
#' @param seeds  seeds
#' @param verbose verbose
#' @keywords Train regression RMSE Rsquared
#' @importFrom parallel makePSOCKcluster stopCluster
#' @importFrom doParallel registerDoParallel
#' @importFrom caret trainControl train getTrainPerf
#' @importFrom stats as.formula
#' @importFrom foreach registerDoSEQ
#' @author Elpidio Filho, \email{elpidio@ufv.br}
#' @details details
#' @export
#' @examples
#' \dontrun{
#' regression(df.train = df, regressor = "rf", metric = "Rsquared", seeds = 313)
#' }


regression <- function(df.train,
                       formula = NULL,
                       preprocess = NULL,
                       regressor = "rf",
                       rsample = "cv",
                       nfolds = 10,
                       repeats =  NA,
                       index = NULL,
                       cpu_cores = 4,
                       tune_length = 5,
                       search = "grid",
                       metric = "Rsquared",
                       seeds = NULL,
                       verbose = FALSE) {
  resample_methods <- c(
    "boot", "boot632", "optimism_boot", "boot_all", "cv",
    "repeatedcv", "LOOCV", "LGOCV", "none", "oob",
    "timeslice", "adaptive_cv", "adaptive_boot",
    "adaptive_LGOCV"
  )

  if (!any(rsample %in% resample_methods)) {
    stop(paste("resample method", rsample, "does not exist"))
  }

  tc <- caret::trainControl(
    method = rsample, number = nfolds,
    index = index, seeds = seeds, search = search
  )
  switch(rsample,
    "cv" = {
      tc <- caret::trainControl(
        method = rsample, number = nfolds,
        index = index, seeds = seeds, search = search
      )
    },
    "repeatedcv" = {
      if ((repeats == "NA") | (repeats < 2)) {
        stop("You must define the number of repeats greater then 1 ")
      }
      tc <- caret::trainControl(
        method = rsample, number = nfolds,
        repeats = repeats,
        index = index, seeds = seeds, search = search
      )
    },
    "none" = {
      tc <- caret::trainControl(method = rsample)
    }
  )

  #inicio <- Sys.time()

  if (cpu_cores > 0) {
    if (get_os() == 'windows'){
    #  cl <- parallel::makePSOCKcluster(cpu_cores)
      cl <- parallel::makeCluster(cpu_cores)
    } else {
      cl <- parallel::makeCluster(cpu_cores, type="FORK")
    }
    doParallel::registerDoParallel(cl)
    on.exit(stopCluster(cl))
  } else {
    cl <- NULL
    foreach::registerDoSEQ()
  }

  if (is.null(formula)) {
    fit <- tryCatch({
        caret::train(
          x = df.train[, -1], y = df.train[, 1],
          method = regressor, metric = metric,
          trControl = tc, tuneLength = tune_length,
          preProcess = preprocess
        )
      },
      error = function(e) {
        print(" ")
        print(e)
        NULL
      }
    )
  } else {
    fit <- tryCatch({
        caret::train(
          formula, data = df.train, method = regressor,
          metric = metric, trControl = tc, tuneLength = tune_length,
          preProcess = preprocess
        )
      },
      error = function(e) {
        print(" ")
        print(e)
        NULL
      }
    )
  }

  if (!is.null(cl)) {
    foreach::registerDoSEQ()
  }
  if (verbose == TRUE) {
    # print(paste("time elapsed : ", hms_span(inicio, Sys.time())))
    # print(caret::getTrainPerf(fit))
  }
  return(fit)
}


get_os <- function(){
  sysinf <- Sys.info()
  if (!is.null(sysinf)){
    os <- sysinf['sysname']
    if (os == 'Darwin')
      os <- "osx"
  } else { ## mystery machine
    os <- .Platform$OS.type
    if (grepl("^darwin", R.version$os))
      os <- "osx"
    if (grepl("linux-gnu", R.version$os))
      os <- "linux"
  }
  tolower(os)
}