From: Benjamin Auder <benjamin.auder@somewhere>
Date: Thu, 24 Jun 2021 10:27:33 +0000 (+0200)
Subject: Add and debug CV-voting
X-Git-Url: https://git.auder.net/variants/Chakart/img/doc/pieces/cn.svg?a=commitdiff_plain;h=7733758e823f6e783f965f5c7f7f80a1d4e5df3a;p=agghoo.git

Add and debug CV-voting
---

diff --git a/DESCRIPTION b/DESCRIPTION
index 5d1842a..5e85d59 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -27,10 +27,10 @@ URL: https://git.auder.net/?p=agghoo.git
 License: MIT + file LICENSE
 RoxygenNote: 7.1.1
 Collate: 
+    'compareTo.R'
     'agghoo.R'
     'R6_AgghooCV.R'
     'R6_Model.R'
-    'A_NAMESPACE.R'
     'checks.R'
-    'compareTo.R'
     'utils.R'
+    'A_NAMESPACE.R'
diff --git a/NAMESPACE b/NAMESPACE
index 1a5f2a0..74d8bd5 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,6 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
 export(AgghooCV)
+export(CVvoting_run)
 export(Model)
 export(agghoo)
 export(agghoo_run)
diff --git a/R/A_NAMESPACE.R b/R/A_NAMESPACE.R
index 6747346..0466833 100644
--- a/R/A_NAMESPACE.R
+++ b/R/A_NAMESPACE.R
@@ -1,4 +1,7 @@
+#' @include utils.R
+#' @include checks.R
 #' @include R6_Model.R
 #' @include R6_AgghooCV.R
 #' @include agghoo.R
+#' @include compareTo.R
 NULL
diff --git a/R/compareTo.R b/R/compareTo.R
index e6bf2b2..28cb711 100644
--- a/R/compareTo.R
+++ b/R/compareTo.R
@@ -1,3 +1,39 @@
+#' 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
+  if (CV$type == "vfold" && CV$shuffle)
+    shuffle_inds <- sample(n, n)
+  list_testinds <- list()
+  for (v in seq_len(CV$V))
+    list_testinds[[v]] <- get_testIndices(n, CV, v, shuffle_inds)
+  gmodel <- agghoo::Model$new(data, target, task, gmodel, params)
+  best_error <- Inf
+  best_p <- NULL
+  for (p in seq_len(gmodel$nmodels)) {
+    error <- Reduce('+', lapply(seq_len(CV$V), function(v) {
+      testIdx <- list_testinds[[v]]
+      d <- splitTrainTest(data, target, testIdx)
+      model_pred <- gmodel$get(d$dataTrain, d$targetTrain, p)
+      prediction <- model_pred(d$dataTest)
+      loss(prediction, d$targetTest)
+    }) )
+    if (error <= best_error) {
+      if (error == best_error)
+        best_p[[length(best_p)+1]] <- p
+      else {
+        best_p <- list(p)
+        best_error <- error
+      }
+    }
+  }
+  chosenP <- best_p[[ sample(length(best_p), 1) ]]
+  list(model=gmodel$get(data, target, chosenP), param=gmodel$getParam(chosenP))
+}
+
 #' CVvoting_core
 #'
 #' "voting" cross-validation method, added here as an example.
@@ -8,8 +44,8 @@ CVvoting_core <- function(data, target, task, gmodel, params, loss, CV) {
   shuffle_inds <- NULL
   if (CV$type == "vfold" && CV$shuffle)
     shuffle_inds <- sample(n, n)
-  bestP <- rep(0, gmodel$nmodels)
   gmodel <- agghoo::Model$new(data, target, task, gmodel, params)
+  bestP <- rep(0, gmodel$nmodels)
   for (v in seq_len(CV$V)) {
     test_indices <- get_testIndices(n, CV, v, shuffle_inds)
     d <- splitTrainTest(data, target, test_indices)
@@ -37,42 +73,6 @@ CVvoting_core <- function(data, target, task, gmodel, params, loss, CV) {
   list(model=gmodel$get(data, target, chosenP), param=gmodel$getParam(chosenP))
 }
 
-#' 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
-  if (CV$type == "vfold" && CV$shuffle)
-    shuffle_inds <- sample(n, n)
-  list_testinds <- list()
-  for (v in seq_len(CV$V))
-    list_testinds[[v]] <- get_testIndices(n, CV, v, shuffle_inds)
-  gmodel <- agghoo::Model$new(data, target, task, gmodel, params)
-  best_error <- Inf
-  best_p <- NULL
-  for (p in seq_len(gmodel$nmodels)) {
-    error <- Reduce('+', lapply(seq_len(CV$V), function(v) {
-      testIdx <- list_testinds[[v]]
-      d <- splitTrainTest(data, target, testIdx)
-      model_pred <- gmodel$get(d$dataTrain, d$targetTrain, p)
-      prediction <- model_pred(d$dataTest)
-      loss(prediction, d$targetTest)
-    }) )
-    if (error <= best_error) {
-      if (error == best_error)
-        best_p[[length(best_p)+1]] <- p
-      else {
-        best_p <- list(p)
-        best_error <- error
-      }
-    }
-  }
-  chosenP <- best_p[[ sample(length(best_p), 1) ]]
-  list(model=gmodel$get(data, target, chosenP), param=gmodel$getParam(chosenP))
-}
-
 #' standardCV_run
 #'
 #' Run and eval the standard cross-validation procedure.
@@ -99,6 +99,32 @@ standardCV_run <- function(
   invisible(err)
 }
 
+#' CVvoting_run
+#'
+#' Run and eval the voting 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
+CVvoting_run <- function(
+  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$CV)
+  s <- CVvoting_core(
+    dataTrain, targetTrain, task, modPar$gmodel, modPar$params, loss, CV)
+  if (verbose)
+    print(paste( "Parameter:", s$param ))
+  p <- s$model(dataTest)
+  err <- floss(p, targetTest)
+  if (verbose)
+    print(paste("error CV:", err))
+  invisible(err)
+}
+
 #' agghoo_run
 #'
 #' Run and eval the agghoo procedure.
diff --git a/man/CVvoting_run.Rd b/man/CVvoting_run.Rd
new file mode 100644
index 0000000..9aad2fe
--- /dev/null
+++ b/man/CVvoting_run.Rd
@@ -0,0 +1,13 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/compareTo.R
+\name{CVvoting_run}
+\alias{CVvoting_run}
+\title{CVvoting_run}
+\usage{
+CVvoting_run(dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...)
+}
+\description{
+Run and eval the voting cross-validation procedure.
+Parameters are rather explicit except "floss", which corresponds to the
+"final" loss function, applied to compute the error on testing dataset.
+}