X-Git-Url: https://git.auder.net/?p=agghoo.git;a=blobdiff_plain;f=agghoo.Rcheck%2F00_pkg_src%2Fagghoo%2FR%2FcompareTo.R;fp=agghoo.Rcheck%2F00_pkg_src%2Fagghoo%2FR%2FcompareTo.R;h=fe5b24d4be8a526bbbb4149d713490403f948cb3;hp=0000000000000000000000000000000000000000;hb=97f16440280a40a49c4898a75942e374880bfca3;hpb=3b8affec63125c3816a3d15f0f49776dc14867a2

diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/R/compareTo.R b/agghoo.Rcheck/00_pkg_src/agghoo/R/compareTo.R
new file mode 100644
index 0000000..fe5b24d
--- /dev/null
+++ b/agghoo.Rcheck/00_pkg_src/agghoo/R/compareTo.R
@@ -0,0 +1,247 @@
+#' 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$getParam(chosenP))
+}
+
+#' CVvoting_core
+#'
+#' "voting" cross-validation method, added here as an example.
+#' Parameters are described in ?agghoo and ?AgghooCV
+CVvoting_core <- function(data, target, task, gmodel, params, loss, CV) {
+  CV <- checkCV(CV)
+  n <- nrow(data)
+  shuffle_inds <- NULL
+  if (CV$type == "vfold" && CV$shuffle)
+    shuffle_inds <- sample(n, n)
+  gmodel <- agghoo::Model$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)
+    best_p <- NULL
+    best_error <- Inf
+    for (p in seq_len(gmodel$nmodels)) {
+      model_pred <- gmodel$get(d$dataTrain, d$targetTrain, p)
+      prediction <- model_pred(d$dataTest)
+      error <- loss(prediction, d$targetTest)
+      if (error <= best_error) {
+        if (error == best_error)
+          best_p[[length(best_p)+1]] <- p
+        else {
+          best_p <- list(p)
+          best_error <- error
+        }
+      }
+    }
+    for (p in best_p)
+      bestP[p] <- bestP[p] + 1
+  }
+  # Choose a param at random in case of ex-aequos:
+  maxP <- max(bestP)
+  chosenP <- sample(which(bestP == maxP), 1)
+  list(model=gmodel$get(data, target, chosenP), param=gmodel$getParam(chosenP))
+}
+
+#' 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.
+standardCV_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$CV)
+  s <- standardCV_core(
+    dataTrain, targetTrain, task, modPar$gmodel, modPar$params, loss, CV)
+  if (verbose)
+    print(paste( "Parameter:", s$param ))
+  p <- s$model(dataTest)
+  err <- floss(p, targetTest)
+  if (verbose)
+    print(paste("error CV:", err))
+  invisible(err)
+}
+
+#' CVvoting_run
+#'
+#' Run and eval the voting cross-validation procedure.
+#' Parameters are rather explicit except "floss", which corresponds to the
+#' "final" loss function, applied to compute the error on testing dataset.
+CVvoting_run <- function(
+  dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
+) {
+  args <- list(...)
+  task <- checkTask(args$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.
+#' Parameters are rather explicit except "floss", which corresponds to the
+#' "final" loss function, applied to compute the error on testing dataset.
+agghoo_run <- function(
+  dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
+) {
+  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$fit(CV)
+  if (verbose) {
+    print("Parameters:")
+    print(unlist(a$getParams()))
+  }
+  pa <- a$predict(dataTest)
+  err <- floss(pa, targetTest)
+  if (verbose)
+    print(paste("error agghoo:", err))
+  invisible(err)
+}
+
+#' compareTo
+#'
+#' Compare a list of learning methods (or run only one), on data/target.
+#'
+#' @param data Data matrix or data.frame
+#' @param target Target vector (generally)
+#' @param method_s Either a single function, or a list
+#'                 (examples: agghoo_run, standardCV_run)
+#' @param rseed Seed of the random generator (-1 means "random seed")
+#' @param floss Loss function to compute the error on testing dataset.
+#' @param verbose TRUE to request methods to be verbose.
+#' @param ... arguments passed to method_s function(s)
+#'
+#' @export
+compareTo <- function(
+  data, target, method_s, rseed=-1, floss=NULL, verbose=TRUE, ...
+) {
+  if (rseed >= 0)
+    set.seed(rseed)
+  n <- nrow(data)
+  test_indices <- sample( n, round(n / ifelse(n >= 500, 10, 5)) )
+  d <- splitTrainTest(data, target, test_indices)
+
+  # Set error function to be used on model outputs (not in core method)
+  task <- checkTask(list(...)$task, target)
+  if (is.null(floss)) {
+    floss <- function(y1, y2) {
+      ifelse(task == "classification", mean(y1 != y2), mean(abs(y1 - y2)))
+    }
+  }
+
+  # Run (and compare) all methods:
+  runOne <- function(o) {
+    o(d$dataTrain, d$dataTest, d$targetTrain, d$targetTest, floss, verbose, ...)
+  }
+  errors <- c()
+  if (is.list(method_s))
+    errors <- sapply(method_s, runOne)
+  else if (is.function(method_s))
+    errors <- runOne(method_s)
+  invisible(errors)
+}
+
+#' 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, verbose=TRUE, ...
+) {
+  base::require(parallel)
+  if (is.na(nc))
+    nc <- parallel::detectCores()
+
+  # "One" comparison for each method in method_s (list)
+  compareOne <- function(n) {
+    if (verbose)
+      print(n)
+    compareTo(data, target, method_s, n, floss, verbose=FALSE, ...)
+  }
+
+  errors <- if (nc >= 2) {
+    parallel::mclapply(1:N, compareOne, mc.cores = nc)
+  } else {
+    lapply(1:N, compareOne)
+  }
+  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))
+}