rename all multicrit to multiobj (#282)

* rename all multicrit to multiobj

Author: jakob-r

DESCRIPTION

@@ -2,7 +2,7 @@ Package: mlrMBO
 Title: Model-Based Optimization for mlr
 Description: A framework for the (sequential) model-based parameter
     optimization. It offers methods to optimize numeric or discrete influence
-    parameters of non-linear black-box single- or multiobjective target
+    parameters of non-linear black-box single- or multi-objective target
     functions like an industrial simulator or a time-consuming algorithm using
     cheap surrogate models.
 Authors@R: c(

NAMESPACE

@@ -9,15 +9,15 @@ S3method(predictLearner,MultiFidWrapper)
 S3method(predictLearner,regr.kmforrester)
 S3method(print,MBOControl)
 S3method(print,MBOExampleRun)
-S3method(print,MBOExampleRunMultiCrit)
+S3method(print,MBOExampleRunMultiObj)
 S3method(print,MBOMultiObjResult)
 S3method(print,MBOResult)
 S3method(renderExampleRunPlot,MBOExampleRun)
-S3method(renderExampleRunPlot,MBOExampleRunMultiCrit)
+S3method(renderExampleRunPlot,MBOExampleRunMultiObj)
 S3method(trainLearner,MultiFidWrapper)
 S3method(trainLearner,regr.kmforrester)
 export(exampleRun)
-export(exampleRunMultiCrit)
+export(exampleRunMultiObj)
 export(getGlobalOpt)
 export(getSupportedInfillCritFunctions)
 export(getSupportedInfillOptFunctions)
@@ -32,8 +32,8 @@ export(plotEAF)
 export(plotExampleRun)
 export(renderExampleRunPlot)
 export(setMBOControlInfill)
-export(setMBOControlMultiCrit)
 export(setMBOControlMultiFid)
+export(setMBOControlMultiObj)
 export(setMBOControlMultiPoint)
 export(setMBOControlTermination)
 export(trafoLog)

R/OptProblem.R

@@ -75,9 +75,9 @@ setOptProblemAllPossibleWeights = function(opt.problem, all.possible.weights) {
 
 getOptProblemAllPossibleWeights = function(opt.problem) {
   control = getOptProblemControl(opt.problem)
-  if (is.null(opt.problem$all.possible.weights) && control$n.objectives > 1L && control$multicrit.method == "parego") {
+  if (is.null(opt.problem$all.possible.weights) && control$n.objectives > 1L && control$multiobj.method == "parego") {
     # calculate all possible weight vectors and save them
-    all.possible.weights = combWithSum(control$multicrit.parego.s, control$n.objectives) / control$multicrit.parego.s
+    all.possible.weights = combWithSum(control$multiobj.parego.s, control$n.objectives) / control$multiobj.parego.s
     # rearrange them a bit - we want to have the margin weights on top of the matrix
     # tricky: all margin weights have maximal variance
     vars = apply(all.possible.weights, 1, var)

R/OptState_getter.R

@@ -80,7 +80,7 @@ getOptStateShouldSave = function(opt.state) {
 }
 
 # @param unify [\code{logical(1)}]
-#   Defines if in the case of multicriterial optimization we shoud try to make
+#   Defines if in the case of multi-objective optimization we shoud try to make
 #  the output similar to the result of the normal optimization.
 getOptStateFinalPoints = function(opt.state, unify = FALSE) {
   opt.problem = getOptStateOptProblem(opt.state)

R/checkStuff.R

@@ -23,15 +23,15 @@ checkStuff = function(fun, par.set, design, learner, control) {
       getNumberOfObjectives(fun), control$n.objectives)
   }
 
-  # at the moment we do not support noisy multicriteria optimization
+  # at the moment we do not support noisy multi-objective optimization
   if (getNumberOfObjectives(fun) > 1L && isNoisy(fun)) {
     stopf("Optimization of noisy multi-objective functions not supported in the moment.")
   }
 
-  # final.method and final.evals have no effect on multicriteria optimization
+  # final.method and final.evals have no effect on multi-objective optimization
   if (getNumberOfObjectives(fun) > 1L &&
       (control$final.method != "best.true.y" || control$final.evals > 0L)) {
-    stop("Setting of final.method and final.evals for multicriteria optimization not supported at the moment.")
+    stop("Setting of final.method and final.evals for multi-objective optimization not supported at the moment.")
   }
 
   # general parameter and learner checks
@@ -50,7 +50,7 @@ checkStuff = function(fun, par.set, design, learner, control) {
   if (hasRequires(par.set) && !hasLearnerProperties(learner, "missings"))
     stopf("The 'par.set' has dependent parameters, which will lead to missing values in X-space during modeling, but learner '%s' does not support handling of missing values (property 'missing')!", learner$id)
 
-  # general infill stuff (relavant for single objective and parEGO)
+  # general infill stuff (relavant for single-objective and parEGO)
   if (control$infill.crit %in% c("se", "ei", "aei", "cb", "dib") && learner$predict.type != "se") {
     stopf("For infill criterion '%s' predict.type of learner %s must be set to 'se'!%s",
       control$infill.crit, learner$id,
@@ -90,7 +90,7 @@ checkStuff = function(fun, par.set, design, learner, control) {
   control$store.model.at = coalesce(control$store.model.at, control$iters + 1)
   control$resample.at = coalesce(control$resample.at, integer(0))
 
-  #  single objective
+  #  single-objective
   if (control$n.objectives == 1L) {
     if (control$propose.points == 1L) { # single point
     } else {                            # multi point
@@ -112,17 +112,17 @@ checkStuff = function(fun, par.set, design, learner, control) {
     }
   }
 
-  # multicrit stuff
+  # multi-objective stuff
   if (control$n.objectives > 1L) {
-    if (control$multicrit.method == "dib") {
+    if (control$multiobj.method == "dib") {
       if (control$infill.crit != "dib")
-        stopf("For multicrit 'dib' infil.crit must be set to 'dib'!")
+        stopf("For multi-objective 'dib' infil.crit must be set to 'dib'!")
     } else {
       if (control$infill.crit == "dib")
-        stopf("For infill.crit 'dib', multicrit method 'dib' is needed!")
+        stopf("For infill.crit 'dib', multi-objective method 'dib' is needed!")
     }
-    if (control$multicrit.method == "mspot" && control$infill.opt != "nsga2")
-      stopf("For multicrit 'mspot' infil.opt must be set to 'nsga2'!")
+    if (control$multiobj.method == "mspot" && control$infill.opt != "nsga2")
+      stopf("For multi-objective 'mspot' infil.opt must be set to 'nsga2'!")
   }
 
   # multifidelity stuff

R/doc_error_handling.R

@@ -6,7 +6,7 @@
 #'
 #' The target function could
 #' \itemize{
-#'   \item{1}{The target function returns NA(s) or NaN(s) (plural for the multicrit case).}
+#'   \item{1}{The target function returns NA(s) or NaN(s) (plural for the multi-objective case).}
 #'   \item{2}{The target function stops with an error.}
 #'   \item{3}{The target function does not return at all (infinite or very long execution time).}
 #'   \item{4}{The target function crashes the whole R process.}

R/doc_mbo_parallel.R

@@ -16,8 +16,8 @@
 #' }
 #' Details regarding the latter:
 #' \describe{
-#' \item{Singlecrit MBO with LCB multipoint}{Parallel optimization of LCBs for the lambda-values.}
-#' \item{Multicrit MBO with ParEGO}{Parallel optimization of scalarization functions.}
+#' \item{single-objective MBO with LCB multipoint}{Parallel optimization of LCBs for the lambda-values.}
+#' \item{Multi-objective MBO with ParEGO}{Parallel optimization of scalarization functions.}
 #' }
 #'
 #' @name mbo_parallel

R/evalProposedPoints.R

@@ -4,7 +4,7 @@
 # @param opt.state
 # @param prop: result of proposePoints
 # @return [\code{numeric} | \code{matrix}] Numeric vector of y-vals or matrix
-#   (for multi-criteria problems).
+#   (for multi-objective problems).
 #
 # gets the getExtras, converts point data.frame to a list, repairs points out-of-bounds
 # then call evalTargetFun

R/evalTargetFun.R

@@ -4,7 +4,7 @@
 # @param xs: list of list of points to evaluate
 # @param extras: list of extra stuff from getExtras, list of list of entries. same length as xs.
 # @return [\code{numeric} | \code{matrix}] Numeric vector of y-vals or matrix
-#   (for multi-criteria problems).
+#   (for multi-objective problems).
 #
 # Does this:
 # 1) trafo X points

R/exampleRun.R

@@ -55,7 +55,7 @@ exampleRun = function(fun, design = NULL, learner = NULL, control,
     global.opt = NA_real_
 
   if (control$n.objectives != 1L)
-    stopf("exampleRun can only be applied for single objective functions, but you have %i objectives! Use 'exampleRunMultiCrit'!",
+    stopf("exampleRun can only be applied for single-objective functions, but you have %i objectives! Use 'exampleRunMultiObj'!",
       control$n.objectives)
   if (n.params >= 3L)
     stopf("exampleRun can only be applied for functions with at most 2 dimensions, but you have %iD", n.params)