updating mlrmbo to handle small runs

Author: pbalapra

workflows/nt3_mlrMBO/R/mlrMBO3.R

@@ -61,7 +61,7 @@ main_function <- function(max.budget = 110, max.iterations = 10, design.size=10,
   surr.rf = makeLearner("regr.randomForest", predict.type = "se")
   ctrl = makeMBOControl(n.objectives = 1, propose.points = min(20, propose.points), 
        	  			       impute.y.fun = function(x, y, opt.path, ...) .Machine$integer.max * 0.1 )
-  ctrl = setMBOControlInfill(ctrl, crit=makeMBOInfillCritCB(), interleave.random.points=min(0,propose.points-20))
+  ctrl = setMBOControlInfill(ctrl, crit=makeMBOInfillCritCB(), interleave.random.points=max(0,propose.points-20))
   ctrl = setMBOControlMultiPoint(ctrl, method = "cb")
   ctrl = setMBOControlTermination(ctrl, max.evals = max.budget)
   ctrl = setMBOControlTermination(ctrl, iters = max.iterations)

workflows/p2b1_mlrMBO/R/mlrMBO3.R

@@ -57,10 +57,15 @@ simple.obj.fun = function(x){}
 
 main_function <- function(max.budget = 110, max.iterations = 10, design.size=10, propose.points=10){
 
+
   surr.rf = makeLearner("regr.randomForest", predict.type = "se")
-  ctrl = makeMBOControl(n.objectives = 1, propose.points = propose.points)
+  ctrl = makeMBOControl(n.objectives = 1, propose.points = min(20, propose.points), 
+       	   			       	         impute.y.fun = function(x, y, opt.path, ...) .Machine$integer.max * 0.1 )
+  ctrl = setMBOControlInfill(ctrl, crit=makeMBOInfillCritCB(), interleave.random.points=max(0,propose.points-20))
+  ctrl = setMBOControlMultiPoint(ctrl, method = "cb")
   ctrl = setMBOControlTermination(ctrl, max.evals = max.budget)
   ctrl = setMBOControlTermination(ctrl, iters = max.iterations)
+
   # ctrl = setMBOControlInfill(ctrl, crit =makeMBOInfillCritCB(), opt.focussearch.points = 500)
   design = generateDesign(n = design.size, par.set = getParamSet(obj.fun))
   configureMlr(show.info = FALSE, show.learner.output = FALSE, on.learner.warning = "quiet")

workflows/p3b1_mlrMBO/R/mlrMBO3.R

@@ -58,9 +58,13 @@ simple.obj.fun = function(x){}
 main_function <- function(max.budget = 110, max.iterations = 10, design.size=10, propose.points=10){
 
   surr.rf = makeLearner("regr.randomForest", predict.type = "se")
-  ctrl = makeMBOControl(n.objectives = 1, propose.points = propose.points)
+  ctrl = makeMBOControl(n.objectives = 1, propose.points = min(20, propose.points), 
+       	   			       	         impute.y.fun = function(x, y, opt.path, ...) .Machine$integer.max * 0.1 )
+  ctrl = setMBOControlInfill(ctrl, crit=makeMBOInfillCritCB(), interleave.random.points=max(0,propose.points-20))
+  ctrl = setMBOControlMultiPoint(ctrl, method = "cb")
   ctrl = setMBOControlTermination(ctrl, max.evals = max.budget)
   ctrl = setMBOControlTermination(ctrl, iters = max.iterations)
+
   # ctrl = setMBOControlInfill(ctrl, crit =makeMBOInfillCritCB(), opt.focussearch.points = 500)
   design = generateDesign(n = design.size, par.set = getParamSet(obj.fun))
   configureMlr(show.info = FALSE, show.learner.output = FALSE, on.learner.warning = "quiet")