Refactoring: separate standard CV from agghoo + some bug fixes

[agghoo.git] / R / agghoo.R

diff --git a/R/agghoo.R b/R/agghoo.R
index f3bc740..cac2cf1 100644 (file)
--- a/R/agghoo.R
+++ b/R/agghoo.R
@@ -1,9 +1,12 @@
 #' agghoo
 #'
-#' Run the agghoo procedure. (...)
+#' Run the agghoo procedure (or standard cross-validation).
+#' 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
@@ -14,11 +17,12 @@
 #' @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::AgghooCV.
+#'        loss(y1, y2) --> real number (error). Default: see R6::AgghooCV.
 #'
-#' @return An R6::AgghooCV object.
+#' @return
+#' An R6::AgghooCV object o. Then, call o$fit() and finally o$predict(newData)
 #'
 #' @examples
 #' # Regression:
@@ -27,15 +31,23 @@
 #' 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))
 #'
+#' @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 = NULL, gmodel = NULL, params = NULL, quality = NULL) {
+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 (!is.numeric(target) && !is.factor(target) && !is.character(target))
+  if (is.data.frame(target) || is.matrix(target)) {
+    if (nrow(target) != nrow(data) || ncol(target) == 1)
+      stop("target probability matrix does not match data size")
+  }
+  else if (!is.numeric(target) && !is.factor(target) && !is.character(target))
     stop("target: numeric, factor or character vector")
   if (!is.null(task))
     task = match.arg(task, c("classification", "regression"))
@@ -52,9 +64,9 @@ agghoo <- function(data, target, task = NULL, gmodel = NULL, params = NULL, qual
     stop("params must be provided when using a custom model")
   if (is.list(params) && is.null(gmodel))
     stop("model (or family) must be provided when using custom params")
-  if (!is.null(quality) && !is.function(quality))
+  if (!is.null(loss) && !is.function(loss))
     # No more checks here as well... TODO:?
-    stop("quality: function(y1, y2) --> Real")
+    stop("loss: function(y1, y2) --> Real")
 
   if (is.null(task)) {
     if (is.numeric(target))
@@ -65,34 +77,5 @@ agghoo <- function(data, target, task = NULL, gmodel = NULL, params = NULL, qual
   # Build Model object (= list of parameterized models)
   model <- Model$new(data, target, task, gmodel, params)
   # Return AgghooCV object, to run and predict
-  AgghooCV$new(data, target, task, model, quality)
-}
-
-#' 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 = NULL, rseed = -1) {
-  if (rseed >= 0)
-    set.seed(rseed)
-  if (is.null(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])))))
+  AgghooCV$new(data, target, task, model, loss)
 }