R/compareTo.R

   1 #' standardCV_core
   2 #'
   3 #' Cross-validation method, added here as an example.
   4 #' Parameters are described in ?agghoo and ?AgghooCV
   5 standardCV_core <- function(data, target, task, gmodel, params, loss, CV) {
   6   n <- nrow(data)
   7   shuffle_inds <- NULL
   8   if (CV$type == "vfold" && CV$shuffle)
   9     shuffle_inds <- sample(n, n)
  10   list_testinds <- list()
  11   for (v in seq_len(CV$V))
  12     list_testinds[[v]] <- get_testIndices(n, CV, v, shuffle_inds)
  13   gmodel <- agghoo::Model$new(data, target, task, gmodel, params)
  14   best_error <- Inf
  15   best_p <- NULL
  16   for (p in seq_len(gmodel$nmodels)) {
  17     error <- Reduce('+', lapply(seq_len(CV$V), function(v) {
  18       testIdx <- list_testinds[[v]]
  19       d <- splitTrainTest(data, target, testIdx)
  20       model_pred <- gmodel$get(d$dataTrain, d$targetTrain, p)
  21       prediction <- model_pred(d$dataTest)
  22       loss(prediction, d$targetTest)
  23     }) )
  24     if (error <= best_error) {
  25       if (error == best_error)
  26         best_p[[length(best_p)+1]] <- p
  27       else {
  28         best_p <- list(p)
  29         best_error <- error
  30       }
  31     }
  32   }
  33   chosenP <- best_p[[ sample(length(best_p), 1) ]]
  34   list(model=gmodel$get(data, target, chosenP), param=gmodel$getParam(chosenP))
  35 }
  36
  37 #' CVvoting_core
  38 #'
  39 #' "voting" cross-validation method, added here as an example.
  40 #' Parameters are described in ?agghoo and ?AgghooCV
  41 CVvoting_core <- function(data, target, task, gmodel, params, loss, CV) {
  42   CV <- checkCV(CV)
  43   n <- nrow(data)
  44   shuffle_inds <- NULL
  45   if (CV$type == "vfold" && CV$shuffle)
  46     shuffle_inds <- sample(n, n)
  47   gmodel <- agghoo::Model$new(data, target, task, gmodel, params)
  48   bestP <- rep(0, gmodel$nmodels)
  49   for (v in seq_len(CV$V)) {
  50     test_indices <- get_testIndices(n, CV, v, shuffle_inds)
  51     d <- splitTrainTest(data, target, test_indices)
  52     best_p <- NULL
  53     best_error <- Inf
  54     for (p in seq_len(gmodel$nmodels)) {
  55       model_pred <- gmodel$get(d$dataTrain, d$targetTrain, p)
  56       prediction <- model_pred(d$dataTest)
  57       error <- loss(prediction, d$targetTest)
  58       if (error <= best_error) {
  59         if (error == best_error)
  60           best_p[[length(best_p)+1]] <- p
  61         else {
  62           best_p <- list(p)
  63           best_error <- error
  64         }
  65       }
  66     }
  67     for (p in best_p)
  68       bestP[p] <- bestP[p] + 1
  69   }
  70   # Choose a param at random in case of ex-aequos:
  71   maxP <- max(bestP)
  72   chosenP <- sample(which(bestP == maxP), 1)
  73   list(model=gmodel$get(data, target, chosenP), param=gmodel$getParam(chosenP))
  74 }
  75
  76 #' standardCV_run
  77 #'
  78 #' Run and eval the standard cross-validation procedure.
  79 #' Parameters are rather explicit except "floss", which corresponds to the
  80 #' "final" loss function, applied to compute the error on testing dataset.
  81 standardCV_run <- function(
  82   dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
  83 ) {
  84   args <- list(...)
  85   task <- checkTask(args$task, targetTrain)
  86   modPar <- checkModPar(args$gmodel, args$params)
  87   loss <- checkLoss(args$loss, task)
  88   CV <- checkCV(args$CV)
  89   s <- standardCV_core(
  90     dataTrain, targetTrain, task, modPar$gmodel, modPar$params, loss, CV)
  91   if (verbose)
  92     print(paste( "Parameter:", s$param ))
  93   p <- s$model(dataTest)
  94   err <- floss(p, targetTest)
  95   if (verbose)
  96     print(paste("error CV:", err))
  97   invisible(err)
  98 }
  99
 100 #' CVvoting_run
 101 #'
 102 #' Run and eval the voting cross-validation procedure.
 103 #' Parameters are rather explicit except "floss", which corresponds to the
 104 #' "final" loss function, applied to compute the error on testing dataset.
 105 CVvoting_run <- function(
 106   dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
 107 ) {
 108   args <- list(...)
 109   task <- checkTask(args$task, targetTrain)
 110   modPar <- checkModPar(args$gmodel, args$params)
 111   loss <- checkLoss(args$loss, task)
 112   CV <- checkCV(args$CV)
 113   s <- CVvoting_core(
 114     dataTrain, targetTrain, task, modPar$gmodel, modPar$params, loss, CV)
 115   if (verbose)
 116     print(paste( "Parameter:", s$param ))
 117   p <- s$model(dataTest)
 118   err <- floss(p, targetTest)
 119   if (verbose)
 120     print(paste("error CV:", err))
 121   invisible(err)
 122 }
 123
 124 #' agghoo_run
 125 #'
 126 #' Run and eval the agghoo procedure.
 127 #' Parameters are rather explicit except "floss", which corresponds to the
 128 #' "final" loss function, applied to compute the error on testing dataset.
 129 agghoo_run <- function(
 130   dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
 131 ) {
 132   args <- list(...)
 133   CV <- checkCV(args$CV)
 134   # Must remove CV arg, or agghoo will complain "error: unused arg"
 135   args$CV <- NULL
 136   a <- do.call(agghoo, c(list(data=dataTrain, target=targetTrain), args))
 137   a$fit(CV)
 138   if (verbose) {
 139     print("Parameters:")
 140     print(unlist(a$getParams()))
 141   }
 142   pa <- a$predict(dataTest)
 143   err <- floss(pa, targetTest)
 144   if (verbose)
 145     print(paste("error agghoo:", err))
 146   invisible(err)
 147 }
 148
 149 #' compareTo
 150 #'
 151 #' Compare a list of learning methods (or run only one), on data/target.
 152 #'
 153 #' @param data Data matrix or data.frame
 154 #' @param target Target vector (generally)
 155 #' @param method_s Either a single function, or a list
 156 #'                 (examples: agghoo_run, standardCV_run)
 157 #' @param rseed Seed of the random generator (-1 means "random seed")
 158 #' @param floss Loss function to compute the error on testing dataset.
 159 #' @param verbose TRUE to request methods to be verbose.
 160 #' @param ... arguments passed to method_s function(s)
 161 #'
 162 #' @export
 163 compareTo <- function(
 164   data, target, method_s, rseed=-1, floss=NULL, verbose=TRUE, ...
 165 ) {
 166   if (rseed >= 0)
 167     set.seed(rseed)
 168   n <- nrow(data)
 169   test_indices <- sample( n, round(n / ifelse(n >= 500, 10, 5)) )
 170   d <- splitTrainTest(data, target, test_indices)
 171
 172   # Set error function to be used on model outputs (not in core method)
 173   task <- checkTask(list(...)$task, target)
 174   if (is.null(floss)) {
 175     floss <- function(y1, y2) {
 176       ifelse(task == "classification", mean(y1 != y2), mean(abs(y1 - y2)))
 177     }
 178   }
 179
 180   # Run (and compare) all methods:
 181   runOne <- function(o) {
 182     o(d$dataTrain, d$dataTest, d$targetTrain, d$targetTest, floss, verbose, ...)
 183   }
 184   errors <- c()
 185   if (is.list(method_s))
 186     errors <- sapply(method_s, runOne)
 187   else if (is.function(method_s))
 188     errors <- runOne(method_s)
 189   invisible(errors)
 190 }
 191
 192 #' compareMulti
 193 #'
 194 #' Run compareTo N times in parallel.
 195 #'
 196 #' @inheritParams compareTo
 197 #' @param N Number of calls to method(s)
 198 #' @param nc Number of cores. Set to parallel::detectCores() if undefined.
 199 #'           Set it to any value <=1 to say "no parallelism".
 200 #' @param verbose TRUE to print task numbers and "Errors:" in the end.
 201 #'
 202 #' @export
 203 compareMulti <- function(
 204   data, target, method_s, N=100, nc=NA, floss=NULL, verbose=TRUE, ...
 205 ) {
 206   base::require(parallel)
 207   if (is.na(nc))
 208     nc <- parallel::detectCores()
 209
 210   # "One" comparison for each method in method_s (list)
 211   compareOne <- function(n) {
 212     if (verbose)
 213       print(n)
 214     compareTo(data, target, method_s, n, floss, verbose=FALSE, ...)
 215   }
 216
 217   errors <- if (nc >= 2) {
 218     parallel::mclapply(1:N, compareOne, mc.cores = nc)
 219   } else {
 220     lapply(1:N, compareOne)
 221   }
 222   if (verbose)
 223     print("Errors:")
 224   Reduce('+', errors) / N
 225 }
 226
 227 #' compareRange
 228 #'
 229 #' Run compareMulti on several values of the parameter V.
 230 #'
 231 #' @inheritParams compareMulti
 232 #' @param V_range Values of V to be tested.
 233 #'
 234 #' @export
 235 compareRange <- function(
 236   data, target, method_s, N=100, nc=NA, floss=NULL, V_range=c(10,15,20), ...
 237 ) {
 238   args <- list(...)
 239   # Avoid warnings if V is left unspecified:
 240   CV <- suppressWarnings( checkCV(args$CV) )
 241   errors <- lapply(V_range, function(V) {
 242     args$CV$V <- V
 243     do.call(compareMulti, c(list(data=data, target=target, method_s=method_s,
 244                                  N=N, nc=nc, floss=floss, verbose=F), args))
 245   })
 246   print(paste(V_range, errors))
 247 }