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#' @title FTRL-Proximal Linear Model Cross Validation
#'
#' @description
#' An advanced interface for FTRL-Proximal online learning model cross validation.
#'
#' @param x a transposed \code{dgCMatrix}.
#' @param y a vector containing labels.
#' @param family link function to be used in the model. "gaussian", "binomial" and "poisson" are avaliable.
#' @param params a list of parameters of FTRL-Proximal Algorithm.
#' \itemize{
#' \item \code{alpha} alpha in the per-coordinate learning rate
#' \item \code{beta} beta in the per-coordinate learning rate
#' \item \code{l1} L1 regularization parameter
#' \item \code{l2} L2 regularization parameter
#' }
#' @param epoch The number of iterations over training data to train the model.
#' @param folds \code{list} provides a possibility of using a list of pre-defined CV folds (each element must be a vector of fold's indices).
#' @param eval a evaluation metrics computing function, the first argument shoule be prediction, the second argument shoule be label.
#' @return a list with the following elements is returned:
#' \itemize{
#' \item \code{dt} a data.table with each mean and standard deviation stat for training set and test set
#' \item \code{pred} a numerical vector with predictions for each CV-fold for the model having been trained on the data in all other folds.
#' }
#' @references
#' H. B. McMahan, G. Holt, D. Sculley, et al. "Ad click prediction: a view from the trenches".
#' In: _The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,
#' KDD 2013, Chicago, IL, USA, August 11-14, 2013_. Ed. by I. S.Dhillon, Y. Koren, R. Ghani,
#' T. E. Senator, P. Bradley, R. Parekh, J. He, R. L. Grossman and R. Uthurusamy. ACM, 2013, pp. 1222-1230.
#' DOI: 10.1145/2487575.2488200. <URL: \url{http://doi.acm.org/10.1145/2487575.2488200}>.
#' @examples
#' library(FeatureHashing)
#' library(data.table)
#' library(rBayesianOptimization)
#' library(MLmetrics)
#' data(ipinyou)
#' m.train <- FTRLProx_Hashing(~ 0 + ., ipinyou.train[,-"IsClick", with = FALSE],
#' hash.size = 2^13, signed.hash = FALSE, verbose = TRUE)
#' ftrl_model_cv <- FTRLProx_cv(x = m.train, y = as.numeric(ipinyou.train$IsClick),
#' family = "binomial",
#' params = list(alpha = 0.01, beta = 0.1, l1 = 1.0, l2 = 1.0),
#' epoch = 10,
#' folds = KFold(as.numeric(ipinyou.train$IsClick), nfolds = 5),
#' eval = AUC)
#' @importFrom magrittr %>%
#' @importFrom foreach %do%
#' @importFrom stats sd
#' @export
FTRLProx_cv <- function(x, y, family = c("gaussian", "binomial", "poisson"),
params = list(alpha = 0.1, beta = 1.0, l1 = 1.0, l2 = 1.0), epoch = 1,
folds, eval) {
Perf_Pred_List <- foreach::foreach(i = seq_along(folds)) %do% {
FTRLProx <- FTRLProx_validate(x = slice(x, -folds[[i]]), y = y[-folds[[i]]], family = family,
params = params, epoch = epoch,
val_x = slice(x, folds[[i]]), val_y = y[folds[[i]]], eval = eval,
verbose = FALSE)
Pred <- FTRLProx_predict(FTRLProx, newx = slice(x, folds[[i]]))
Perf_Train <- FTRLProx$eval_train
Perf_Val <- FTRLProx$eval_val
list(Pred = Pred, Perf_Train = Perf_Train, Perf_Val = Perf_Val)
}
ID_CV <- foreach::foreach(i = seq_along(folds), .combine = "c") %do% {
folds[[i]]
}
Pred_CV <- foreach::foreach(i = seq_along(Perf_Pred_List), .combine = "c") %do% {
Perf_Pred_List[[i]]$Pred
} %>% magrittr::extract(., order(ID_CV))
Perf_Train_CV <- foreach::foreach(i = seq_along(Perf_Pred_List), .combine = "cbind") %do% {
Perf_Pred_List[[i]]$Perf_Train
}
Perf_Val_CV <- foreach::foreach(i = seq_along(Perf_Pred_List), .combine = "cbind") %do% {
Perf_Pred_List[[i]]$Perf_Val
}
Perf_CV = data.table::data.table(Train_Mean = apply(Perf_Train_CV, 1, mean),
Train_SD = apply(Perf_Train_CV, 1, sd),
Validation_Mean = apply(Perf_Val_CV, 1, mean),
Validation_SD = apply(Perf_Val_CV, 1, sd))
list(dt = Perf_CV,
pred = Pred_CV)
}
utils::globalVariables(c("i", "."))
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