# 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 search search option "grid" or "random"
#' @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 foreach registerDoSEQ
#' @importFrom caret trainControl train
#' @details details
#' @author Elpidio Filho, \email{elpidio@ufv.br}
#' @examples
#' \dontrun{
#' library(dplyr)
#' library(labgeo)
#'
#' data("iris")
#' d = iris %>% select(Species, everything())
#' vt = train_test(df = d, p = 0.75,seed = 313)
#' train = vt$train
#' test = vt$test
#' fit = classification(df.train = train, preprocess = c('center', 'scale'),
#' classifier = 'rf', nfolds = 5, cpu_cores = 0,
#' metric = 'Kappa', tune_length = 3,
#' verbose = T )
#' pred = predict(fit, test)
#' obs = test[,1]
#' plot_confusion_matrix(obs, pred)
#' }
#' @export
classification <- function(df.train,
formula = NULL,
preprocess = NULL,
classifier = "rf",
rsample = "cv",
nfolds = 10,
repeats = NA,
index = NULL,
cpu_cores = 4,
tune_length = 5,
search = "grid",
metric = "Kappa",
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,verboseIter = FALSE,
index = index, seeds = seeds, search = search
)
switch(rsample,
"cv" = {
tc <- caret::trainControl(
method = rsample, number = nfolds, verboseIter = FALSE,
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,verboseIter = FALSE,
repeats = repeats,
index = index, seeds = seeds, search = search, verboseIter =
)
},
"none" = {
tc <- caret::trainControl(method = rsample, verboseIter = FALSE)
}
)
#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 = classifier, metric = metric,
trControl = tc, tuneLength = tune_length, verbose = FALSE,
preProcess = preprocess
)
},
error = function(e) {
print(" ")
print(e)
NULL
}
)
} else {
fit <- tryCatch({
caret::train(
form = formula, data = df.train, method = classifier,
metric = metric, trControl = tc, tuneLength = tune_length, verbose = FALSE,
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)
}
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