#' @title Write results.
#'
#' @description Append results to a log file.
#'
#' @param df List of the dataset meta-features
#' @param algorithmName String of the name of selected classifier algorithm.
#' @param bestParams String of the best parameters configuration found.
#' @param perf String of the performance value obtained using the selected algorithm and parameter configuration.
#' @param nModels Integer representing the number of classifier algorithms that you want to select based on Meta-Learning and start to tune using Bayesian Optimization.
#' @param metric Metric to be used in evaluation:
#' \itemize{
#' \item "acc" - Accuracy,
#' \item "fscore" - Micro-Average of F-Score of each label,
#' \item "recall" - Micro-Average of Recall of each label,
#' \item "precision" - Micro-Average of Precision of each label.
#' }
#'
#' @return None
#'
#' @examples sendToTmp(\code{df}, 'knn', '1', '0.9').
#'
#' @noRd
#'
#' @keywords internal
sendToTmp <- function(df, algorithmName, bestParams, perf, nModels, metric = 'acc') {
df$params <- sprintf("%s", paste( unlist(bestParams), collapse='#'))
df$performance <- perf
df$classifierAlgorithm <- sprintf("%s", algorithmName)
query <- sprintf("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s",
df$datasetRatio, df$featuresKurtStdDev, df$featuresKurtMean, df$featuresKurtMax, df$featuresKurtMin, df$featuresSkewStdDev,
df$featuresSkewMean, df$featuresSkewMax, df$featuresSkewMin, df$symbolsStdDev, df$symbolsSum, df$symbolsMean, df$classProbStdDev,
df$classProbMean, df$classProbMax, df$classProbMin, df$classEntropy, df$ratioNumToCat, df$nCatFeatures, df$nNumFeatures,
df$nInstances, df$nFeatures, df$nClasses, df$lognFeatures, df$lognInstances, df$classifierAlgorithm, df$params, df$maxTime, metric,
df$performance, nModels)
return(query)
#write(query, file=system.file("extdata", "tmp", package = "SmartML", mustWork = TRUE),append=TRUE)
}
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