R/classification.R

In elpidiofilho/easyFit: Routines for fit classification and regression models in easy way

# Classification
#'
#' @title classification
#' @description   This function performs the training of the chosen classifier
#' @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 classifier Choice of classifier to be used to train model. Uses  algortims names from Caret package.
#' @param nfolds     Number of folds to be build in crossvalidation
#' @param index index
#' @param repeats repeats
#' @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 metric metric used to evaluate model fit. For numeric outcome ("RMSE", "Rsquared)
#' @param seeds  seeds seeds
#' @param verbose verbose
#' @keywords Train kappa
#' @importFrom parallel makePSOCKcluster stopCluster
#' @importFrom doParallel registerDoParallel
#' @importFrom caret trainControl train
#' @details details
#' @author Elpidio Filho, \email{elpidio@ufv.br}
#' @examples
#' \dontrun{
#' kappa_cv_evaluation(train,"rf",10,6)
#' }
#' @export

classification <- function(df.train,
                           formula = NULL,
                           preprocess = NULL,
                           classifier = "rf",
                           nfolds = 10,
                           repeats = 1,
                           index = NULL,
                           cpu_cores = 4,
                           tune_length = 5,
                           metric = "Kappa",
                           seeds = NULL,
                           verbose = FALSE) {
  cl <- NULL
  if (nfolds == 0) {
    method <- "none"
    tune_length <- 1
  }
  if (nfolds == nrow(df.train)) {
    method <- "LOOCV"
  } else {
    method <- "CV"
  }
  #if (repeats > 1) method <- "repeatedcv"
  inicio <- Sys.time()
  tc <- caret::trainControl(method = method,number = nfolds,
                            index = index,  seeds = seeds )

  if (cpu_cores > 0) {
    cl <- parallel::makePSOCKcluster(cpu_cores)
    doParallel::registerDoParallel(cl)
    on.exit(stopCluster(cl))
  }

  #set.seed(313)
  if (is.null(formula == FALSE)) {
    fit <- tryCatch({
      suppressMessages(caret::train(
        formula, data = df.train, method = classifier,
        metric = metric,
        trControl = tc,
        tuneLength = tune_length,
        preProcess = preprocess
      ))},
      error = function(e) NULL)
  } else {
    fit <- tryCatch({
      suppressMessages(caret::train(
        x = df.train[, -1],
        y = df.train[, 1],
        method = classifier,
        metric = metric,
        trControl = tc,
        tuneLength = tune_length,
        preProcess = preprocess
      ))},
      error = function(e) NULL)

  }
  #if (!is.null(cl)) {
  #  parallel::stopCluster(cl)
  #}
  if (verbose == TRUE & is.null(fit) == FALSE) {
    print(paste("Classification variable ", names(df.train)[1]))
    print(paste("time elapsed : ", hms_span(inicio, Sys.time())))
    print(caret::getTrainPerf(fit))
  }
  return(fit)
}