+++ /dev/null
-#' standardCV_core
-#'
-#' Cross-validation method, added here as an example.
-#' Parameters are described in ?agghoo and ?AgghooCV
-standardCV_core <- function(data, target, task, gmodel, params, loss, CV) {
- n <- nrow(data)
- shuffle_inds <- NULL
- if (CV$type == "vfold" && CV$shuffle)
- shuffle_inds <- sample(n, n)
- list_testinds <- list()
- for (v in seq_len(CV$V))
- list_testinds[[v]] <- get_testIndices(n, CV, v, shuffle_inds)
- gmodel <- agghoo::Model$new(data, target, task, gmodel, params)
- best_error <- Inf
- best_p <- NULL
- for (p in seq_len(gmodel$nmodels)) {
- error <- Reduce('+', lapply(seq_len(CV$V), function(v) {
- testIdx <- list_testinds[[v]]
- d <- splitTrainTest(data, target, testIdx)
- model_pred <- gmodel$get(d$dataTrain, d$targetTrain, p)
- prediction <- model_pred(d$dataTest)
- loss(prediction, d$targetTest)
- }) )
- if (error <= best_error) {
- if (error == best_error)
- best_p[[length(best_p)+1]] <- p
- else {
- best_p <- list(p)
- best_error <- error
- }
- }
- }
- chosenP <- best_p[[ sample(length(best_p), 1) ]]
- list(model=gmodel$get(data, target, chosenP), param=gmodel$getParam(chosenP))
-}
-
-#' CVvoting_core
-#'
-#' "voting" cross-validation method, added here as an example.
-#' Parameters are described in ?agghoo and ?AgghooCV
-CVvoting_core <- function(data, target, task, gmodel, params, loss, CV) {
- CV <- checkCV(CV)
- n <- nrow(data)
- shuffle_inds <- NULL
- if (CV$type == "vfold" && CV$shuffle)
- shuffle_inds <- sample(n, n)
- gmodel <- agghoo::Model$new(data, target, task, gmodel, params)
- bestP <- rep(0, gmodel$nmodels)
- for (v in seq_len(CV$V)) {
- test_indices <- get_testIndices(n, CV, v, shuffle_inds)
- d <- splitTrainTest(data, target, test_indices)
- best_p <- NULL
- best_error <- Inf
- for (p in seq_len(gmodel$nmodels)) {
- model_pred <- gmodel$get(d$dataTrain, d$targetTrain, p)
- prediction <- model_pred(d$dataTest)
- error <- loss(prediction, d$targetTest)
- if (error <= best_error) {
- if (error == best_error)
- best_p[[length(best_p)+1]] <- p
- else {
- best_p <- list(p)
- best_error <- error
- }
- }
- }
- for (p in best_p)
- bestP[p] <- bestP[p] + 1
- }
- # Choose a param at random in case of ex-aequos:
- maxP <- max(bestP)
- chosenP <- sample(which(bestP == maxP), 1)
- list(model=gmodel$get(data, target, chosenP), param=gmodel$getParam(chosenP))
-}
-
-#' standardCV_run
-#'
-#' Run and eval the standard cross-validation procedure.
-#' Parameters are rather explicit except "floss", which corresponds to the
-#' "final" loss function, applied to compute the error on testing dataset.
-standardCV_run <- function(
- dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
-) {
- args <- list(...)
- task <- checkTask(args$task, targetTrain)
- modPar <- checkModPar(args$gmodel, args$params)
- loss <- checkLoss(args$loss, task)
- CV <- checkCV(args$CV)
- s <- standardCV_core(
- dataTrain, targetTrain, task, modPar$gmodel, modPar$params, loss, CV)
- if (verbose)
- print(paste( "Parameter:", s$param ))
- p <- s$model(dataTest)
- err <- floss(p, targetTest)
- if (verbose)
- print(paste("error CV:", err))
- invisible(err)
-}
-
-#' CVvoting_run
-#'
-#' Run and eval the voting cross-validation procedure.
-#' Parameters are rather explicit except "floss", which corresponds to the
-#' "final" loss function, applied to compute the error on testing dataset.
-CVvoting_run <- function(
- dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
-) {
- args <- list(...)
- task <- checkTask(args$task, targetTrain)
- modPar <- checkModPar(args$gmodel, args$params)
- loss <- checkLoss(args$loss, task)
- CV <- checkCV(args$CV)
- s <- CVvoting_core(
- dataTrain, targetTrain, task, modPar$gmodel, modPar$params, loss, CV)
- if (verbose)
- print(paste( "Parameter:", s$param ))
- p <- s$model(dataTest)
- err <- floss(p, targetTest)
- if (verbose)
- print(paste("error CV:", err))
- invisible(err)
-}
-
-#' agghoo_run
-#'
-#' Run and eval the agghoo procedure.
-#' Parameters are rather explicit except "floss", which corresponds to the
-#' "final" loss function, applied to compute the error on testing dataset.
-agghoo_run <- function(
- dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
-) {
- args <- list(...)
- CV <- checkCV(args$CV)
- # Must remove CV arg, or agghoo will complain "error: unused arg"
- args$CV <- NULL
- a <- do.call(agghoo, c(list(data=dataTrain, target=targetTrain), args))
- a$fit(CV)
- if (verbose) {
- print("Parameters:")
- print(unlist(a$getParams()))
- }
- pa <- a$predict(dataTest)
- err <- floss(pa, targetTest)
- if (verbose)
- print(paste("error agghoo:", err))
- invisible(err)
-}
-
-#' compareTo
-#'
-#' Compare a list of learning methods (or run only one), on data/target.
-#'
-#' @param data Data matrix or data.frame
-#' @param target Target vector (generally)
-#' @param method_s Either a single function, or a list
-#' (examples: agghoo_run, standardCV_run)
-#' @param rseed Seed of the random generator (-1 means "random seed")
-#' @param floss Loss function to compute the error on testing dataset.
-#' @param verbose TRUE to request methods to be verbose.
-#' @param ... arguments passed to method_s function(s)
-#'
-#' @export
-compareTo <- function(
- data, target, method_s, rseed=-1, floss=NULL, verbose=TRUE, ...
-) {
- if (rseed >= 0)
- set.seed(rseed)
- n <- nrow(data)
- test_indices <- sample( n, round(n / ifelse(n >= 500, 10, 5)) )
- d <- splitTrainTest(data, target, test_indices)
-
- # Set error function to be used on model outputs (not in core method)
- task <- checkTask(list(...)$task, target)
- if (is.null(floss)) {
- floss <- function(y1, y2) {
- ifelse(task == "classification", mean(y1 != y2), mean(abs(y1 - y2)))
- }
- }
-
- # Run (and compare) all methods:
- runOne <- function(o) {
- o(d$dataTrain, d$dataTest, d$targetTrain, d$targetTest, floss, verbose, ...)
- }
- errors <- c()
- if (is.list(method_s))
- errors <- sapply(method_s, runOne)
- else if (is.function(method_s))
- errors <- runOne(method_s)
- invisible(errors)
-}
-
-#' compareMulti
-#'
-#' Run compareTo N times in parallel.
-#'
-#' @inheritParams compareTo
-#' @param N Number of calls to method(s)
-#' @param nc Number of cores. Set to parallel::detectCores() if undefined.
-#' Set it to any value <=1 to say "no parallelism".
-#' @param verbose TRUE to print task numbers and "Errors:" in the end.
-#'
-#' @export
-compareMulti <- function(
- data, target, method_s, N=100, nc=NA, floss=NULL, verbose=TRUE, ...
-) {
- base::require(parallel)
- if (is.na(nc))
- nc <- parallel::detectCores()
-
- # "One" comparison for each method in method_s (list)
- compareOne <- function(n) {
- if (verbose)
- print(n)
- compareTo(data, target, method_s, n, floss, verbose=FALSE, ...)
- }
-
- errors <- if (nc >= 2) {
- parallel::mclapply(1:N, compareOne, mc.cores = nc)
- } else {
- lapply(1:N, compareOne)
- }
- if (verbose)
- print("Errors:")
- Reduce('+', errors) / N
-}
-
-#' compareRange
-#'
-#' Run compareMulti on several values of the parameter V.
-#'
-#' @inheritParams compareMulti
-#' @param V_range Values of V to be tested.
-#'
-#' @export
-compareRange <- function(
- data, target, method_s, N=100, nc=NA, floss=NULL, V_range=c(10,15,20), ...
-) {
- args <- list(...)
- # Avoid warnings if V is left unspecified:
- CV <- suppressWarnings( checkCV(args$CV) )
- errors <- lapply(V_range, function(V) {
- args$CV$V <- V
- do.call(compareMulti, c(list(data=data, target=target, method_s=method_s,
- N=N, nc=nc, floss=floss, verbose=F), args))
- })
- print(paste(V_range, errors))
-}
projects / agghoo.git / blobdiff