CODEWITHSUNDEEP
roptimizationhyperparametersmlrhyperopt
nelepi
nelepi

Reputation: 27

How to use mlrMBO with mlr for hyperparameter optimisation and tuning

Im trying to train ML algorithms (rf, adaboost, xgboost) in R on a dataset where the target is multiclass classification. For hyperparameter tuning I use the MLR package.

My goal of the code below is to tune the parameters mtry and nodesize, but keep ntrees constant at 128 (with mlrMBO). However, I get the error message below. How can I define this in the correct way?

rdesc <- makeResampleDesc("CV",stratify = T,iters=10L) 

traintask <- makeClassifTask(data = df_train, 
                             target = "more_than_X_perc_damage")
testtask <- makeClassifTask(data = df_test, 
                            target = "more_than_X_perc_damage")

lrn <- makeLearner("classif.randomForest",
                   predict.type = "prob")

# parameter space 
params_to_tune <- makeParamSet(makeIntegerParam("ntree", lower = 128, upper = 128),
                               makeNumericParam("mtry", lower = 0, upper = 1, trafo = function(x) ceiling(x*ncol(train_x))),
                               makeNumericParam("nodesize",lower = 0,upper = 1, trafo = function(x) ceiling(nrow(train_x)^x)))

ctrl = makeTuneControlMBO(mbo.control=mlrMBO::makeMBOControl())
tuned_params <- tuneParams(learner = lrn, 
                           task = traintask, 
                           control = ctrl, 
                           par.set = params_to_tune, 
                           resampling = rdesc, 
                           measure=acc)

rf_tuned_learner <- setHyperPars(learner = lrn,
                                 par.vals = tuned_params$x)

rf_tuned_model <- mlr::train(rf_tuned_learner, traintask)

# prediction performance
pred <- predict(rf_tuned_model, testtask)
performance(pred) 
calculateConfusionMatrix(pred)
stats <- confusionMatrix(pred$data$response,pred$data$truth)
acc_rf_tune <- stats$overall[1] # accuracy
print(acc_rf_tune)

Error in (function (fn, nvars, max = FALSE, pop.size = 1000, max.generations = 100, : Domains[,1] must be less than or equal to Domains[,2]

Thanks in advance!

Upvotes: 0

Views: 349

Answers (1)

Lars Kotthoff
Lars Kotthoff

Reputation: 109272

You can do this by not including the hyperparameter you want to keep constant in the ParamSet and instead setting it to the value you want when creating the learner.

Upvotes: 1

Related Questions