#' agghoo
#'
-#' Run the agghoo procedure. (...)
+#' Run the (core) agghoo procedure.
+#' Arguments specify the list of models, their parameters and the
+#' cross-validation settings, among others.
#'
#' @param data Data frame or matrix containing the data in lines.
-#' @param target The target values to predict. Generally a vector.
+#' @param target The target values to predict. Generally a vector,
+#' but possibly a matrix in the case of "soft classification".
#' @param task "classification" or "regression". Default:
#' regression if target is numerical, classification otherwise.
#' @param gmodel A "generic model", which is a function returning a predict
#' @param params A list of parameters. Often, one list cell is just a
#' numerical value, but in general it could be of any type.
#' Default: see R6::Model.
-#' @param quality A function assessing the quality of a prediction.
+#' @param loss A function assessing the error of a prediction.
#' Arguments are y1 and y2 (comparing a prediction to known values).
-#' Default: see R6::Agghoo.
+#' loss(y1, y2) --> real number (error). Default: see R6::AgghooCV.
#'
-#' @return An R6::Agghoo object.
+#' @return
+#' An R6::AgghooCV object o. Then, call o$fit() and finally o$predict(newData)
#'
#' @examples
#' # Regression:
#' pr <- a_reg$predict(iris[,-c(2,5)] + rnorm(450, sd=0.1))
#' # Classification
#' a_cla <- agghoo(iris[,-5], iris[,5])
-#' a_cla$fit(mode="standard")
+#' a_cla$fit()
#' pc <- a_cla$predict(iris[,-5] + rnorm(600, sd=0.1))
#'
+#' @seealso Function \code{\link{compareTo}}
+#'
+#' @references
+#' Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle. "Aggregated hold-out".
+#' Journal of Machine Learning Research 22(20):1--55, 2021.
+#'
#' @export
-agghoo <- function(data, target, task = NA, gmodel = NA, params = NA, quality = NA) {
+agghoo <- function(
+ data, target, task = NULL, gmodel = NULL, params = NULL, loss = NULL
+) {
# Args check:
- if (!is.data.frame(data) && !is.matrix(data))
- stop("data: data.frame or matrix")
- if (nrow(data) <= 1 || any(dim(data) == 0))
- stop("data: non-empty, >= 2 rows")
- if (!is.numeric(target) && !is.factor(target) && !is.character(target))
- stop("target: numeric, factor or character vector")
- if (!is.na(task))
- task = match.arg(task, c("classification", "regression"))
- if (is.character(gmodel))
- gmodel <- match.arg("knn", "ppr", "rf")
- else if (!is.na(gmodel) && !is.function(gmodel))
- # No further checks here: fingers crossed :)
- stop("gmodel: function(dataHO, targetHO, param) --> function(X) --> y")
- if (is.numeric(params) || is.character(params))
- params <- as.list(params)
- if (!is.na(params) && !is.list(params))
- stop("params: numerical, character, or list (passed to model)")
- if (!is.na(gmodel) && !is.character(model) && is.na(params))
- stop("params must be provided when using a custom model")
- if (is.na(gmodel) && !is.na(params))
- stop("model must be provided when using custom params")
- if (!is.na(quality) && !is.function(quality))
- # No more checks here as well... TODO:?
- stop("quality: function(y1, y2) --> Real")
+ checkDaTa(data, target)
+ task <- checkTask(task, target)
+ modPar <- checkModPar(gmodel, params)
+ loss <- checkLoss(loss, task)
- if (is.na(task)) {
- if (is.numeric(target))
- task = "regression"
- else
- task = "classification"
- }
# Build Model object (= list of parameterized models)
- model <- Model$new(data, target, task, gmodel, params)
- # Return Agghoo object, to run and predict
- Agghoo$new(data, target, task, model, quality)
-}
+ model <- Model$new(data, target, task, modPar$gmodel, modPar$params)
-#' compareToStandard
-#'
-#' Temporary function to compare agghoo to CV
-#' (TODO: extended, in another file, more tests - when faster code).
-#'
-#' @export
-compareToStandard <- function(df, t_idx, task = NA, rseed = -1) {
- if (rseed >= 0)
- set.seed(rseed)
- if (is.na(task))
- task <- ifelse(is.numeric(df[,t_idx]), "regression", "classification")
- n <- nrow(df)
- test_indices <- sample( n, round(n / ifelse(n >= 500, 10, 5)) )
- a <- agghoo(df[-test_indices,-t_idx], df[-test_indices,t_idx], task)
- a$fit(mode="agghoo") #default mode
- pa <- a$predict(df[test_indices,-t_idx])
- print(paste("error agghoo",
- ifelse(task == "classification",
- mean(p != df[test_indices,t_idx]),
- mean(abs(pa - df[test_indices,t_idx])))))
- # Compare with standard cross-validation:
- a$fit(mode="standard")
- ps <- a$predict(df[test_indices,-t_idx])
- print(paste("error CV",
- ifelse(task == "classification",
- mean(ps != df[test_indices,t_idx]),
- mean(abs(ps - df[test_indices,t_idx])))))
+ # Return AgghooCV object, to run and predict
+ AgghooCV$new(data, target, task, model, loss)
}
projects / agghoo.git / blobdiff