I am creating custom learners, in particular, I am trying to use h2o machine learning algorithms as part of mlr. The "hidden" parameter of the h2o.deeplearning function is the integer vector that I want to configure. I defined the "hidden" parameter as follows:
makeRLearner.classif.h2o_dl = function() { makeRLearnerClassif( cl = "classif.h2o_dl", package = "h2o", par.set = makeParamSet( makeDiscreteLearnerParam(id = "activation", values = c("Rectifier", "Tanh", "TanhWithDropout", "RectifierWithDropout", "Maxout", "MaxoutWithDropout")), makeNumericLearnerParam(id = "epochs", default = 10, lower = 1), makeNumericLearnerParam(id = "rate", default = 0.005, lower = 0, upper = 1), makeIntegerVectorLearnerParam(id = "hidden", default = c(100,100)), makeDiscreteLearnerParam(id = "loss", values = c("Automatic", "CrossEntropy", "Quadratic", "Absolute", "Huber")) ), properties = c("twoclass", "multiclass", "numerics", "factors", "prob","missings"), name = "Deep Learning Neural Network with h2o", short.name = "h2o_deeplearning_classif", note = "tbd" ) } trainLearner.classif.h2o_dl = function(.learner, .task,.subset,.weights=NULL, ...) { f = getTaskFormula(.task) data = getTaskData(.task, .subset) data_h2o <- as.h2o(data, destination_frame = paste0( "train_", format(Sys.time(), "%m%d%y_%H%M%S"))) h2o::h2o.deeplearning(x = getTaskFeatureNames(.task), y = setdiff(names(getTaskData(.task)), getTaskFeatureNames(.task)), training_frame = data_h2o, ...) } predictLearner.classif.h2o_dl = function(.learner, .model, .newdata, predict.method = "plug-in", ...) { data <- as.h2o(.newdata, destination_frame = paste0("pred_", format(Sys.time(), "%m%d%y_%H%M%S"))) p = predict(.model$learner.model, newdata = data, method = predict.method, ...) if (.learner$predict.type == "response") return(as.data.frame(p)[,1]) else return(as.matrix(as.numeric(p))[,-1]) }
I tried to set the hidden option through a grid search using the makeDiscreteParam function:
library(mlr) library(h2o) h2o.init() lrn.h2o <- makeLearner("classif.h2o_dl") n <- getTaskSize(sonar.task) train.set = seq(1, n, by = 2) test.set = seq(2, n, by = 2) mod.h2o = train(lrn.h2o, sonar.task, subset = train.set) pred.h2o <- predict(mod.h2o,task= sonar.task, subset = train.set) ctrl = makeTuneControlGrid() rdesc = makeResampleDesc("CV", iters = 3L) ps = makeParamSet( makeDiscreteParam("hidden", values = list(c(10,10),c(100,100))), makeDiscreteParam("rate", values = c(0.1,0.5)) ) res = tuneParams("classif.h2o_dl", task = sonar.task, resampling = rdesc,par.set = ps,control = ctrl)
resulting in a warning message
Warning messages: 1: In checkValuesForDiscreteParam(id, values) : number of items to replace is not a multiple of replacement length 2: In checkValuesForDiscreteParam(id, values) : number of items to replace is not a multiple of replacement length
and ps looks like this:
ps Type len Def Constr Req Tunable Trafo hidden discrete - - 10,100 - TRUE - rate discrete - - 0.1,0.5 - TRUE -
which does not lead to the setting of the hidden parameter as a vector. I also tried using another special design function (e.g. makeNumericVectorParam ), which didn't work either. Does anyone have experience setting up (integer) vectors in mlr and can give me a hint?