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# get one el from each row of a matrix, given indices or col names (factors for colnames are converted to characters)
getRowEls = function(mat, inds) {
if (is.factor(inds)) {
inds = as.character(inds)
}
if (is.character(inds)) {
inds = match(inds, colnames(mat))
}
inds = cbind(seq_row(mat), inds)
mat[inds]
}
# get one el from each col of a matrix, given indices or row names
getColEls = function(mat, inds) {
getRowEls(t(mat), inds)
}
# Do fuzzy string matching between input and a set of valid inputs
# and return the most similar valid inputs.
getNameProposals = function(input, possible.inputs, nproposals = 3L) {
assertString(input)
assertCharacter(possible.inputs)
assertInt(nproposals, lower = 1L)
# compute the approximate string distance (using the generalized Levenshtein / edit distance)
# and get the nproposals most similar valid inputs.
indices = order(adist(input, possible.inputs))[1:nproposals]
possibles = na.omit(possible.inputs[indices])
return(possibles)
}
# shorter way of printing debug dumps
#' @export
print.mlr.dump = function(x, ...) {
cat("<debug dump>\n")
invisible(NULL)
}
# applys the appropriate getPrediction* helper function
getPrediction = function(object, newdata, ...) {
pred = do.call("predict", c(list("object" = object, "newdata" = newdata), list(...)))
point = switch(object$task.desc$type,
"regr" = getPredictionResponse(pred),
"surv" = getPredictionResponse(pred),
"classif" = if (object$learner$predict.type == "response") {
getPredictionResponse(pred)
} else {
getPredictionProbabilities(pred)
}
)
if (object$learner$predict.type == "se") {
cbind("preds" = point, "se" = getPredictionSE(pred))
} else {
point
}
}
# replacement for purrr::imap()
imap = function(.x, .f) {
Map(.f, .x = .x, .y = seq_along(.x))
}
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