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workflows.R
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workflows.R
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### In this file, there are a lot of functions from packages that are
### referenced using `getFromNamespace`. For some
### reason, with _some_ parallel processing technologies and foreach,
### functions inside of caret cannot be found despite using the
### ".packages" argument or even calling the caret package via library().
getOper <- function(x) if(x) `%dopar%` else `%do%`
getTrainOper <- function(x) if(x) `%dopar%` else `%do%`
#' @rdname caret-internal
#' @export
#' @keywords internal
progress <- function(x, names, iter, start = TRUE)
{
text <- paste(ifelse(start, "+ ", "- "),
names[iter], ": ",
paste(colnames(x), x, sep = "=", collapse = ", "),
sep = "")
cat(text, "\n")
}
#' @rdname caret-internal
#' @importFrom stats sd
#' @export
MeanSD <- function(x, exclude = NULL)
{
if(!is.null(exclude)) x <- x[, !(colnames(x) %in% exclude), drop = FALSE]
out <- c(colMeans(x, na.rm = TRUE), sapply(x, sd, na.rm = TRUE))
names(out)[-(1:ncol(x))] <- paste(names(out)[-(1:ncol(x))], "SD", sep = "")
out
}
#' @rdname caret-internal
#' @export
expandParameters <- function(fixed, seq)
{
if(is.null(seq)) return(fixed)
isSeq <- names(fixed) %in% names(seq)
out <- fixed
for(i in 1:nrow(seq))
{
tmp <- fixed
tmp[,isSeq] <- seq[i,]
out <- rbind(out, tmp)
}
out
}
#' @importFrom utils head
#' @importFrom stats complete.cases
#' @import foreach
nominalTrainWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev, testing = FALSE, ...)
{
loadNamespace("caret")
ppp <- list(options = ppOpts)
ppp <- c(ppp, ctrl$preProcOptions)
printed <- format(info$loop, digits = 4)
colnames(printed) <- gsub("^\\.", "", colnames(printed))
## For 632 estimator, add an element to the index of zeros to trick it into
## fitting and predicting the full data set.
resampleIndex <- ctrl$index
if(ctrl$method %in% c("boot632", "optimism_boot", "boot_all"))
{
resampleIndex <- c(list("AllData" = rep(0, nrow(x))), resampleIndex)
ctrl$indexOut <- c(list("AllData" = rep(0, nrow(x))), ctrl$indexOut)
if(!is.null(ctrl$indexExtra)) ctrl$indexExtra <- c(list("AllData" = NULL), ctrl$indexExtra)
}
`%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1)
keep_pred <- isTRUE(ctrl$savePredictions) || ctrl$savePredictions %in% c("all", "final")
pkgs <- c("methods", "caret")
if(!is.null(method$library)) pkgs <- c(pkgs, method$library)
result <- foreach(iter = seq(along = resampleIndex), .combine = "c", .verbose = FALSE, .packages = pkgs, .errorhandling = "stop") %:%
foreach(parm = 1:nrow(info$loop), .combine = "c", .verbose = FALSE, .packages = pkgs, .errorhandling = "stop") %op%
{
if(!(length(ctrl$seeds) == 1 && is.na(ctrl$seeds))) set.seed(ctrl$seeds[[iter]][parm])
loadNamespace("caret")
if(ctrl$verboseIter) progress(printed[parm,,drop = FALSE],
names(resampleIndex), iter)
if(names(resampleIndex)[iter] != "AllData") {
modelIndex <- resampleIndex[[iter]]
holdoutIndex <- ctrl$indexOut[[iter]]
} else {
modelIndex <- 1:nrow(x)
holdoutIndex <- modelIndex
}
extraIndex <- if(is.null(ctrl$indexExtra)) NULL else ctrl$indexExtra[[iter]]
if(testing) cat("pre-model\n")
if(!is.null(info$submodels[[parm]]) && nrow(info$submodels[[parm]]) > 0) {
submod <- info$submodels[[parm]]
} else submod <- NULL
is_regression <- is.null(lev)
mod <- try(
createModel(x = subset_x(x, modelIndex),
y = y[modelIndex],
wts = wts[modelIndex],
method = method,
tuneValue = info$loop[parm,,drop = FALSE],
obsLevels = lev,
pp = ppp,
classProbs = ctrl$classProbs,
sampling = ctrl$sampling,
...),
silent = TRUE)
if(testing) print(mod)
predictedExtra <- NULL
if(!model_failed(mod)) {
predicted <- try(
predictionFunction(method = method,
modelFit = mod$fit,
newdata = subset_x(x, holdoutIndex),
preProc = mod$preProc,
param = submod),
silent = TRUE)
if(pred_failed(predicted)) {
fail_warning(settings = printed[parm,,drop = FALSE],
msg = predicted,
where = "predictions",
iter = names(resampleIndex)[iter],
verb = ctrl$verboseIter)
predicted <- fill_failed_pred(index = holdoutIndex, lev = lev, submod)
} else if(!is.null(extraIndex)) {
predictedExtra <- lapply(extraIndex, function(idx) {
predictionFunction(method = method,
modelFit = mod$fit,
newdata = subset_x(x, idx),
preProc = mod$preProc,
param = submod)
})
}
} else {
fail_warning(settings = printed[parm,,drop = FALSE],
msg = mod,
iter = names(resampleIndex)[iter],
verb = ctrl$verboseIter)
## setup a dummy results with NA values for all predictions
predicted <- fill_failed_pred(index = holdoutIndex, lev = lev, submod)
}
if(testing) print(head(predicted))
probValuesExtra <- NULL
if(ctrl$classProbs) {
if(!model_failed(mod)) {
probValues <- probFunction(method = method,
modelFit = mod$fit,
newdata = subset_x(x, holdoutIndex),
preProc = mod$preProc,
param = submod)
if (!is.null(extraIndex))
probValuesExtra <- lapply(extraIndex, function(index) {
probFunction(method = method,
modelFit = mod$fit,
newdata = subset_x(x, index),
preProc = mod$preProc,
param = submod)
})
} else {
probValues <- fill_failed_prob(holdoutIndex, lev, submod)
}
if(testing) print(head(probValues))
}
if(is.null(probValuesExtra)) {
probValuesExtra <- as.data.frame(matrix(NA, nrow = nrow(x), ncol = length(lev)))
colnames(probValuesExtra) <- lev
if(!is.null(submod)) {
probValuesExtra <- rep(list(probValuesExtra), nrow(info$submodels[[parm]]) + 1L)
}
probValuesExtra <- rep(list(probValuesExtra), 2L)
}
##################################
predicted <- trim_values(predicted, ctrl, is_regression)
##################################
if(!is.null(submod)) {
## merge the fixed and seq parameter values together
allParam <- expandParameters(info$loop[parm,,drop = FALSE], info$submodels[[parm]])
allParam <- allParam[complete.cases(allParam),, drop = FALSE]
## collate the predicitons across all the sub-models
predicted <- lapply(predicted,
function(x, y, wts, lv, rows) {
x <- outcome_conversion(x, lv = lev)
out <- data.frame(pred = x, obs = y, stringsAsFactors = FALSE)
if(!is.null(wts)) out$weights <- wts
out$rowIndex <- rows
out
},
y = y[holdoutIndex],
wts = wts[holdoutIndex],
lv = lev,
rows = holdoutIndex)
if(!is.null(predictedExtra))
predictedExtra <- mapply(predictedExtra, extraIndex,
SIMPLIFY = FALSE, USE.NAMES = FALSE,
FUN = function(pred, rows) {
lapply(pred, function(x) {
y <- y[rows]
wts <- wts[rows]
x <- outcome_conversion(x, lv = lev)
out <- data.frame(pred = x, obs = y, stringsAsFactors = FALSE)
if(!is.null(wts)) out$weights <- wts
out$rowIndex <- rows
out
})
})
if(testing) print(head(predicted))
## same for the class probabilities
if(ctrl$classProbs) {
predicted <- mapply(cbind, predicted, probValues, SIMPLIFY = FALSE)
if (!is.null(predictedExtra))
predictedExtra <- mapply(predictedExtra, probValuesExtra,
SIMPLIFY = FALSE,
FUN = function(predEx, probEx) {
mapply(cbind, predEx, probEx, SIMPLIFY = FALSE)
})
}
if(keep_pred || (ctrl$method == "boot_all" && names(resampleIndex)[iter] == "AllData"))
{
tmpPred <- predicted
for(modIndex in seq(along = tmpPred)) {
tmpPred[[modIndex]] <- merge(tmpPred[[modIndex]],
allParam[modIndex,,drop = FALSE],
all = TRUE)
}
tmpPred <- rbind.fill(tmpPred)
tmpPred$Resample <- names(resampleIndex)[iter]
} else tmpPred <- NULL
## get the performance for this resample for each sub-model
thisResample <- lapply(predicted,
ctrl$summaryFunction,
lev = lev,
model = method)
if(testing) print(head(thisResample))
if(!is.null(predictedExtra)) {
thisResampleExtra <- lapply(predictedExtra, function(predicted) {
lapply(predicted,
ctrl$summaryFunction,
lev = lev,
model = method)
})
thisResampleExtra[[1L]] <- lapply(thisResampleExtra[[1L]], function(res) {
names(res) <- paste0(names(res), "Orig")
res
})
thisResampleExtra[[2L]] <- lapply(thisResampleExtra[[2L]], function(res) {
names(res) <- paste0(names(res), "Boot")
res
})
thisResampleExtra <- do.call(cbind, lapply(thisResampleExtra, function(x) do.call(rbind, x)))
thisResampleExtra <- cbind(allParam, thisResampleExtra)
} else thisResampleExtra <- NULL
## for classification, add the cell counts
if(length(lev) > 1 && length(lev) <= 50) {
cells <- lapply(predicted,
function(x) flatTable(x$pred, x$obs))
for(ind in seq(along = cells)) thisResample[[ind]] <- c(thisResample[[ind]], cells[[ind]])
}
thisResample <- do.call("rbind", thisResample)
thisResample <- cbind(allParam, thisResample)
} else {
if(is.factor(y)) predicted <- outcome_conversion(predicted, lv = lev)
tmp <- data.frame(pred = predicted,
obs = y[holdoutIndex],
stringsAsFactors = FALSE)
## Sometimes the code above does not coerce the first
## columnn to be named "pred" so force it
names(tmp)[1] <- "pred"
if(!is.null(wts)) tmp$weights <- wts[holdoutIndex]
if(ctrl$classProbs) tmp <- cbind(tmp, probValues)
tmp$rowIndex <- holdoutIndex
if(keep_pred) {
tmpPred <- tmp
tmpPred$rowIndex <- holdoutIndex
tmpPred <- merge(tmpPred, info$loop[parm,,drop = FALSE],
all = TRUE)
tmpPred$Resample <- names(resampleIndex)[iter]
} else tmpPred <- NULL
##################################
thisResample <- ctrl$summaryFunction(tmp,
lev = lev,
model = method)
## if classification, get the confusion matrix
if(length(lev) > 1 && length(lev) <= 50)
thisResample <- c(thisResample, flatTable(tmp$pred, tmp$obs))
thisResample <- as.data.frame(t(thisResample))
thisResample <- cbind(thisResample, info$loop[parm,,drop = FALSE])
## for optimism bootstrap
if(!is.null(predictedExtra)) {
thisResampleExtra <- mapply(predictedExtra, extraIndex, probValuesExtra,
SIMPLIFY = FALSE, USE.NAMES = FALSE,
FUN = function(predicted, holdoutIndex, probValues) {
if(is.factor(y)) predicted <- outcome_conversion(predicted, lv = lev)
tmp <- data.frame(pred = predicted,
obs = y[holdoutIndex],
stringsAsFactors = FALSE)
## Sometimes the code above does not coerce the first
## columnn to be named "pred" so force it
names(tmp)[1] <- "pred"
if(!is.null(wts)) tmp$weights <- wts[holdoutIndex]
if(ctrl$classProbs) tmp <- cbind(tmp, probValues)
tmp$rowIndex <- holdoutIndex
ctrl$summaryFunction(tmp, lev = lev, model = method)
})
names(thisResampleExtra[[1L]]) <- paste0(names(thisResampleExtra[[1L]]), "Orig")
names(thisResampleExtra[[2L]]) <- paste0(names(thisResampleExtra[[2L]]), "Boot")
thisResampleExtra <- unlist(thisResampleExtra, recursive = FALSE)
thisResampleExtra <- cbind(as.data.frame(t(thisResampleExtra)), info$loop[parm, , drop = FALSE])
} else thisResampleExtra <- NULL
}
thisResample$Resample <- names(resampleIndex)[iter]
if(ctrl$verboseIter) progress(printed[parm,,drop = FALSE],
names(resampleIndex), iter, FALSE)
if(testing) print(thisResample)
list(resamples = thisResample, pred = tmpPred, resamplesExtra = thisResampleExtra)
}
resamples <- rbind.fill(result[names(result) == "resamples"])
pred <- rbind.fill(result[names(result) == "pred"])
resamplesExtra <- rbind.fill(result[names(result) == "resamplesExtra"])
if(ctrl$method %in% c("boot632", "optimism_boot", "boot_all")) {
perfNames <- names(resamples)
perfNames <- perfNames[!(perfNames %in% c("Resample", as.character(method$parameters$parameter)))]
perfNames <- perfNames[!grepl("^\\.cell[0-9]", perfNames)]
apparent <- subset(resamples, Resample == "AllData")
apparent <- apparent[,!grepl("^\\.cell|Resample", colnames(apparent)),drop = FALSE]
names(apparent)[which(names(apparent) %in% perfNames)] <- paste(names(apparent)[which(names(apparent) %in% perfNames)],
"Apparent", sep = "")
names(apparent) <- gsub("^\\.", "", names(apparent))
if(any(!complete.cases(apparent[,!grepl("^cell|Resample", colnames(apparent)),drop = FALSE]))) {
warning("There were missing values in the apparent performance measures.")
}
resamples <- subset(resamples, Resample != "AllData")
if(!is.null(pred)) {
predHat <- subset(pred, Resample == "AllData")
pred <- subset(pred, Resample != "AllData")
}
}
names(resamples) <- gsub("^\\.", "", names(resamples))
if(any(!complete.cases(resamples[,!grepl("^cell|Resample", colnames(resamples)),drop = FALSE]))) {
warning("There were missing values in resampled performance measures.")
}
out <- ddply(resamples[,!grepl("^cell|Resample", colnames(resamples)),drop = FALSE],
gsub("^\\.", "", colnames(info$loop)),
MeanSD,
exclude = gsub("^\\.", "", colnames(info$loop)))
if(ctrl$method %in% c("boot632", "boot_all")) {
out <- merge(out, apparent)
const <- 1 - exp(-1)
sapply(perfNames, function(perfName) {
perfOut <- if(ctrl$method == "boot_all") paste0(perfName, "_632") else perfName
out[, perfOut] <<- (const * out[, perfName]) + ((1-const) * out[, paste(perfName, "Apparent", sep = "")])
NULL
})
}
if(ctrl$method %in% c("optimism_boot", "boot_all")) {
out <- merge(out, apparent)
out <- merge(out, ddply(resamplesExtra[, !grepl("Resample", colnames(resamplesExtra)), drop = FALSE],
colnames(info$loop),
function(df, exclude) {
colMeans(df[, setdiff(colnames(df), exclude), drop = FALSE])
},
exclude = colnames(info$loop)))
sapply(perfNames, function(perfName) {
optimism <- out[ , paste0(perfName, "Orig")] - out[ , paste0(perfName, "Boot")]
final_estimate <- out[ , paste0(perfName, "Apparent")] + optimism
## Remove unnecessary values
out[ , paste0(perfName, "Orig")] <<- NULL
out[ , paste0(perfName, "Boot")] <<- NULL
perfOut <- if(ctrl$method == "boot_all") paste0(perfName, "_OptBoot") else perfName
## Update estimates
out[ , paste0(perfName, "Optimism")] <<- optimism
out[ , perfOut] <<- final_estimate
NULL
})
}
list(performance = out, resamples = resamples, predictions = if(keep_pred) pred else NULL)
}
#' @import foreach
looTrainWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev, testing = FALSE, ...)
{
loadNamespace("caret")
ppp <- list(options = ppOpts)
ppp <- c(ppp, ctrl$preProcOptions)
printed <- format(info$loop)
colnames(printed) <- gsub("^\\.", "", colnames(printed))
`%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1)
pkgs <- c("methods", "caret")
if(!is.null(method$library)) pkgs <- c(pkgs, method$library)
result <- foreach(iter = seq(along = ctrl$index), .combine = "rbind", .verbose = FALSE, .packages = pkgs, .errorhandling = "stop") %:%
foreach(parm = 1:nrow(info$loop), .combine = "rbind", .verbose = FALSE, .packages = pkgs, .errorhandling = "stop") %op% {
if(!(length(ctrl$seeds) == 1 && is.na(ctrl$seeds))) set.seed(ctrl$seeds[[iter]][parm])
if(testing) cat("after loops\n")
loadNamespace("caret")
if(ctrl$verboseIter) progress(printed[parm,,drop = FALSE],
names(ctrl$index), iter, TRUE)
if(is.null(info$submodels[[parm]]) || nrow(info$submodels[[parm]]) > 0) {
submod <- info$submodels[[parm]]
} else submod <- NULL
is_regression <- is.null(lev)
mod <- try(
createModel(x = subset_x(x, ctrl$index[[iter]]),
y = y[ctrl$index[[iter]] ],
wts = wts[ctrl$index[[iter]] ],
method = method,
tuneValue = info$loop[parm,,drop = FALSE],
obsLevels = lev,
pp = ppp,
classProbs = ctrl$classProbs,
sampling = ctrl$sampling,
...),
silent = TRUE)
holdoutIndex <- ctrl$indexOut[[iter]]
if(!model_failed(mod)) {
predicted <- try(
predictionFunction(method = method,
modelFit = mod$fit,
newdata = subset_x(x, -ctrl$index[[iter]]),
preProc = mod$preProc,
param = submod),
silent = TRUE)
if(pred_failed(predicted)) {
fail_warning(settings = printed[parm,,drop = FALSE],
msg = predicted,
where = "predictions",
iter = names(ctrl$index)[iter],
verb = ctrl$verboseIter)
predicted <- fill_failed_pred(index = holdoutIndex, lev = lev, submod)
}
} else {
fail_warning(settings = printed[parm,,drop = FALSE],
msg = mod,
iter = names(ctrl$index)[iter],
verb = ctrl$verboseIter)
predicted <- fill_failed_pred(index = holdoutIndex, lev = lev, submod)
}
if(testing) print(head(predicted))
if(ctrl$classProbs) {
if(!model_failed(mod)) {
probValues <- probFunction(method = method,
modelFit = mod$fit,
newdata = subset_x(x, holdoutIndex),
preProc = mod$preProc,
param = submod)
} else {
probValues <- fill_failed_prob(holdoutIndex, lev, submod)
}
if(testing) print(head(probValues))
}
predicted <- trim_values(predicted, ctrl, is_regression)
##################################
if(!is.null(info$submodels)) {
## collate the predictions across all the sub-models
predicted <- lapply(predicted,
function(x, y, wts, lv, rows) {
x <- outcome_conversion(x, lv = lev)
out <- data.frame(pred = x, obs = y, stringsAsFactors = FALSE)
if(!is.null(wts)) out$weights <- wts
out$rowIndex <- rows
out
},
y = y[holdoutIndex],
wts = wts[holdoutIndex],
lv = lev,
rows = seq(along = y)[holdoutIndex])
if(testing) print(head(predicted))
## same for the class probabilities
if(ctrl$classProbs)
for(k in seq(along = predicted))
predicted[[k]] <- cbind(predicted[[k]], probValues[[k]])
predicted <- do.call("rbind", predicted)
allParam <- expandParameters(info$loop[parm,,drop = FALSE], submod)
rownames(predicted) <- NULL
predicted <- cbind(predicted, allParam)
## if saveDetails then save and export 'predicted'
} else {
predicted <- outcome_conversion(predicted, lv = lev)
predicted <- data.frame(pred = predicted,
obs = y[holdoutIndex],
stringsAsFactors = FALSE)
if(!is.null(wts)) predicted$weights <- wts[holdoutIndex]
if(ctrl$classProbs) predicted <- cbind(predicted, probValues)
predicted$rowIndex <- seq(along = y)[holdoutIndex]
predicted <- cbind(predicted, info$loop[parm,,drop = FALSE])
}
if(ctrl$verboseIter) progress(printed[parm,,drop = FALSE],
names(ctrl$index), iter, FALSE)
predicted
}
names(result) <- gsub("^\\.", "", names(result))
out <- ddply(result,
as.character(method$parameter$parameter),
ctrl$summaryFunction,
lev = lev,
model = method)
list(performance = out, predictions = result)
}
#' @import foreach
oobTrainWorkflow <- function(x, y, wts, info, method, ppOpts, ctrl, lev, testing = FALSE, ...)
{
loadNamespace("caret")
ppp <- list(options = ppOpts)
ppp <- c(ppp, ctrl$preProcOptions)
printed <- format(info$loop)
colnames(printed) <- gsub("^\\.", "", colnames(printed))
`%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1)
pkgs <- c("methods", "caret")
if(!is.null(method$library)) pkgs <- c(pkgs, method$library)
result <- foreach(parm = 1:nrow(info$loop), .packages = pkgs, .combine = "rbind") %op%
{
loadNamespace("caret")
if(ctrl$verboseIter) progress(printed[parm,,drop = FALSE], "", 1, TRUE)
if(!(length(ctrl$seeds) == 1 && is.na(ctrl$seeds))) set.seed(ctrl$seeds[[1L]][parm])
mod <- createModel(x = x,
y = y,
wts = wts,
method = method,
tuneValue = info$loop[parm,,drop = FALSE],
obsLevels = lev,
pp = ppp,
classProbs = ctrl$classProbs,
sampling = ctrl$sampling,
...)
out <- method$oob(mod$fit)
if(ctrl$verboseIter) progress(printed[parm,,drop = FALSE], "", 1, FALSE)
cbind(as.data.frame(t(out)), info$loop[parm,,drop = FALSE])
}
names(result) <- gsub("^\\.", "", names(result))
result
}
################################################################################################
#' @import foreach
nominalSbfWorkflow <- function(x, y, ppOpts, ctrl, lev, ...)
{
loadNamespace("caret")
ppp <- list(options = ppOpts)
ppp <- c(ppp, ctrl$preProcOptions)
resampleIndex <- ctrl$index
if(ctrl$method %in% c("boot632")){
resampleIndex <- c(list("AllData" = rep(0, nrow(x))), resampleIndex)
ctrl$indexOut <- c(list("AllData" = rep(0, nrow(x))), ctrl$indexOut)
}
`%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1)
result <- foreach(iter = seq(along = resampleIndex), .combine = "c", .verbose = FALSE, .packages = c("methods", "caret"), .errorhandling = "stop") %op%
{
if(!(length(ctrl$seeds) == 1 && is.na(ctrl$seeds))) set.seed(ctrl$seeds[iter])
loadNamespace("caret")
if(names(resampleIndex)[iter] != "AllData") {
modelIndex <- resampleIndex[[iter]]
holdoutIndex <- ctrl$indexOut[[iter]]
} else {
modelIndex <- 1:nrow(x)
holdoutIndex <- modelIndex
}
sbfResults <- sbfIter(subset_x(x, modelIndex),
y[modelIndex],
subset_x(x, holdoutIndex),
y[holdoutIndex],
ctrl,
...)
if(ctrl$saveDetails)
{
tmpPred <- sbfResults$pred
tmpPred$Resample <- names(resampleIndex)[iter]
tmpPred$rowIndex <- seq(along = y)[unique(holdoutIndex)]
} else tmpPred <- NULL
resamples <- ctrl$functions$summary(sbfResults$pred, lev = lev)
if(is.factor(y) && length(lev) <= 50) resamples <- c(resamples, flatTable(sbfResults$pred$pred, sbfResults$pred$obs))
resamples <- data.frame(t(resamples))
resamples$Resample <- names(resampleIndex)[iter]
list(resamples = resamples, selectedVars = sbfResults$variables, pred = tmpPred)
}
resamples <- rbind.fill(result[names(result) == "resamples"])
pred <- if(ctrl$saveDetails) rbind.fill(result[names(result) == "pred"]) else NULL
performance <- MeanSD(resamples[,!grepl("Resample", colnames(resamples)),drop = FALSE])
if(ctrl$method %in% c("boot632"))
{
modelIndex <- 1:nrow(x)
holdoutIndex <- modelIndex
appResults <- sbfIter(subset_x(x, modelIndex),
y[modelIndex],
subset_x(x, holdoutIndex),
y[holdoutIndex],
ctrl,
...)
apparent <- ctrl$functions$summary(appResults$pred, lev = lev)
perfNames <- names(apparent)
perfNames <- perfNames[perfNames != "Resample"]
const <- 1-exp(-1)
for(p in seq(along = perfNames))
performance[perfNames[p]] <- (const * performance[perfNames[p]]) + ((1-const) * apparent[perfNames[p]])
}
list(performance = performance, everything = result, predictions = if(ctrl$saveDetails) pred else NULL)
}
#' @import foreach
looSbfWorkflow <- function(x, y, ppOpts, ctrl, lev, ...)
{
loadNamespace("caret")
ppp <- list(options = ppOpts)
ppp <- c(ppp, ctrl$preProcOptions)
resampleIndex <- ctrl$index
vars <- vector(mode = "list", length = length(y))
`%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1)
result <- foreach(iter = seq(along = resampleIndex), .combine = "c", .verbose = FALSE, .packages = c("methods", "caret"), .errorhandling = "stop") %op%
{
if(!(length(ctrl$seeds) == 1 && is.na(ctrl$seeds))) set.seed(ctrl$seeds[iter])
loadNamespace("caret")
modelIndex <- resampleIndex[[iter]]
holdoutIndex <- -unique(resampleIndex[[iter]])
sbfResults <- sbfIter(subset_x(x, modelIndex),
y[modelIndex],
subset_x(x, holdoutIndex),
y[holdoutIndex],
ctrl,
...)
sbfResults
}
resamples <- do.call("rbind", result[names(result) == "pred"])
performance <- ctrl$functions$summary(resamples, lev = lev)
list(performance = performance, everything = result, predictions = if(ctrl$saveDetails) resamples else NULL)
}
################################################################################################
#' @import foreach
nominalRfeWorkflow <- function(x, y, sizes, ppOpts, ctrl, lev, ...)
{
loadNamespace("caret")
ppp <- list(options = ppOpts)
ppp <- c(ppp, ctrl$preProcOptions)
resampleIndex <- ctrl$index
if(ctrl$method %in% c("boot632")) {
resampleIndex <- c(list("AllData" = rep(0, nrow(x))), resampleIndex)
ctrl$indexOut <- c(list("AllData" = rep(0, nrow(x))), ctrl$indexOut)
}
`%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1)
result <- foreach(iter = seq(along = resampleIndex), .combine = "c", .verbose = FALSE, .packages = c("methods", "caret", "plyr"), .errorhandling = "stop") %op%
{
loadNamespace("caret")
if(names(resampleIndex)[iter] != "AllData") {
modelIndex <- resampleIndex[[iter]]
holdoutIndex <- ctrl$indexOut[[iter]]
} else {
modelIndex <- 1:nrow(x)
holdoutIndex <- modelIndex
}
seeds <- if(!(length(ctrl$seeds) == 1 && is.na(ctrl$seeds))) ctrl$seeds[[iter]] else NA
rfeResults <- rfeIter(subset_x(x, modelIndex),
y[modelIndex],
subset_x(x, holdoutIndex),
y[holdoutIndex],
sizes,
ctrl,
label = names(resampleIndex)[iter],
seeds = seeds,
...)
resamples <- ddply(rfeResults$pred, .(Variables), ctrl$functions$summary, lev = lev)
if(ctrl$saveDetails)
{
rfeResults$pred$Resample <- names(resampleIndex)[iter]
## If the user did not have nrow(x) in 'sizes', rfeIter added it.
## So, we need to find out how many set of predictions there are:
nReps <- length(table(rfeResults$pred$Variables))
rfeResults$pred$rowIndex <- rep(seq(along = y)[unique(holdoutIndex)], nReps)
}
if(is.factor(y) && length(lev) <= 50) {
cells <- ddply(rfeResults$pred, .(Variables), function(x) flatTable(x$pred, x$obs))
resamples <- merge(resamples, cells)
}
resamples$Resample <- names(resampleIndex)[iter]
vars <- do.call("rbind", rfeResults$finalVariables)
vars$Resample <- names(resampleIndex)[iter]
list(resamples = resamples, selectedVars = vars, predictions = if(ctrl$saveDetails) rfeResults$pred else NULL)
}
resamples <- do.call("rbind", result[names(result) == "resamples"])
rownames(resamples) <- NULL
if(ctrl$method %in% c("boot632"))
{
perfNames <- names(resamples)
perfNames <- perfNames[!(perfNames %in% c("Resample", "Variables"))]
perfNames <- perfNames[!grepl("^cell[0-9]", perfNames)]
apparent <- subset(resamples, Resample == "AllData")
apparent <- apparent[,!grepl("^\\.cell|Resample", colnames(apparent)),drop = FALSE]
names(apparent)[which(names(apparent) %in% perfNames)] <- paste(names(apparent)[which(names(apparent) %in% perfNames)],
"Apparent", sep = "")
names(apparent) <- gsub("^\\.", "", names(apparent))
resamples <- subset(resamples, Resample != "AllData")
}
externPerf <- ddply(resamples[,!grepl("\\.cell|Resample", colnames(resamples)),drop = FALSE],
.(Variables),
MeanSD,
exclude = "Variables")
if(ctrl$method %in% c("boot632"))
{
externPerf <- merge(externPerf, apparent)
for(p in seq(along = perfNames))
{
const <- 1-exp(-1)
externPerf[, perfNames[p]] <- (const * externPerf[, perfNames[p]]) + ((1-const) * externPerf[, paste(perfNames[p],"Apparent", sep = "")])
}
externPerf <- externPerf[, !(names(externPerf) %in% paste(perfNames,"Apparent", sep = ""))]
}
list(performance = externPerf, everything = result)
}
#' @import foreach
looRfeWorkflow <- function(x, y, sizes, ppOpts, ctrl, lev, ...)
{
loadNamespace("caret")
ppp <- list(options = ppOpts)
ppp <- c(ppp, ctrl$preProcOptions)
resampleIndex <- ctrl$index
`%op%` <- getOper(ctrl$allowParallel && getDoParWorkers() > 1)
result <- foreach(iter = seq(along = resampleIndex), .combine = "c", .verbose = FALSE, .packages = c("methods", "caret"), .errorhandling = "stop") %op%
{
loadNamespace("caret")
modelIndex <- resampleIndex[[iter]]
holdoutIndex <- -unique(resampleIndex[[iter]])
seeds <- if(!(length(ctrl$seeds) == 1 && is.na(ctrl$seeds))) ctrl$seeds[[iter]] else NA
rfeResults <- rfeIter(subset_x(x, modelIndex),
y[modelIndex],
subset_x(x, holdoutIndex),
y[holdoutIndex],
sizes,
ctrl,
seeds = seeds,
...)
rfeResults
}
preds <- do.call("rbind", result[names(result) == "pred"])
resamples <- ddply(preds, .(Variables), ctrl$functions$summary, lev = lev)
list(performance = resamples, everything = result)
}