X-Git-Url: https://git.auder.net/?p=agghoo.git;a=blobdiff_plain;f=R%2FcompareTo.R;fp=R%2FcompareTo.R;h=536d2eec8526504e0c4f7fcd883e54badea58f49;hp=00e90a9ea7ddaac9ea3a24326ceb21d5eb0e01d0;hb=17ea2f13e0c32c107db20677750bd7a98bb7e0f8;hpb=afa676609daba103e43d6d4654560ca4c1c9b38b

diff --git a/R/compareTo.R b/R/compareTo.R
index 00e90a9..536d2ee 100644
--- a/R/compareTo.R
+++ b/R/compareTo.R
@@ -1,3 +1,7 @@
+#' 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
@@ -28,17 +32,24 @@ standardCV_core <- function(data, target, task, gmodel, params, loss, CV) {
       }
     }
   }
-#browser()
   best_model[[ sample(length(best_model), 1) ]]
 }
 
+#' 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.
+#'
+#' @export
 standardCV_run <- function(
-  dataTrain, dataTest, targetTrain, targetTest, CV, floss, verbose, ...
+  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)
@@ -50,10 +61,21 @@ standardCV_run <- function(
   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.
+#'
+#' @export
 agghoo_run <- function(
-  dataTrain, dataTest, targetTrain, targetTest, CV, floss, verbose, ...
+  dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
 ) {
-  a <- agghoo(dataTrain, targetTrain, ...)
+  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:")
@@ -66,7 +88,20 @@ agghoo_run <- function(
   invisible(err)
 }
 
-# ... arguments passed to method_s (agghoo, standard CV or else)
+#' 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, ...
 ) {
@@ -75,7 +110,6 @@ compareTo <- function(
   n <- nrow(data)
   test_indices <- sample( n, round(n / ifelse(n >= 500, 10, 5)) )
   d <- splitTrainTest(data, target, test_indices)
-  CV <- checkCV(list(...)$CV)
 
   # Set error function to be used on model outputs (not in core method)
   task <- checkTask(list(...)$task, target)
@@ -87,8 +121,7 @@ compareTo <- function(
 
   # Run (and compare) all methods:
   runOne <- function(o) {
-    o(d$dataTrain, d$dataTest, d$targetTrain, d$targetTest,
-      CV, floss, verbose, ...)
+    o(d$dataTrain, d$dataTest, d$targetTrain, d$targetTest, floss, verbose, ...)
   }
   errors <- c()
   if (is.list(method_s))
@@ -98,10 +131,19 @@ compareTo <- function(
   invisible(errors)
 }
 
-# Run compareTo N times in parallel
-# ... : additional args to be passed to method_s
+#' 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, ...
+  data, target, method_s, N=100, nc=NA, floss=NULL, verbose=TRUE, ...
 ) {
   require(parallel)
   if (is.na(nc))
@@ -109,7 +151,8 @@ compareMulti <- function(
 
   # "One" comparison for each method in method_s (list)
   compareOne <- function(n) {
-    print(n)
+    if (verbose)
+      print(n)
     compareTo(data, target, method_s, n, floss, verbose=FALSE, ...)
   }
 
@@ -118,6 +161,29 @@ compareMulti <- function(
   } else {
     lapply(1:N, compareOne)
   }
-  print("Errors:")
+  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))
+}