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I am using mlr3 for a simple classification model. But I encounter errors with several different models which mlr3 gives access to. Here I provide one reprex to illustrate the problem:

library(data.table)
library(mlr3extralearners)
library(mlr3)
library(mlr3learners)
library(mlr3tuning)
library(mlr3pipelines)
library(mlr3filters)

#Make example data
DT = data.table(target = c(0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),pred = c(0.05767878,0.05761652,0.06508700,0.06531820,0.07050699,0.07098812,0.07150984,0.07845767,0.07891081,0.07873572,0.08035471,0.08039300,0.08040480,0.08040480,0.08472619,0.08489135,0.08517742,0.08612768,0.08728675,0.08790671,0.08913434,0.08911522,0.09036788,0.09147726,0.09154964,0.09236259,0.09299088,0.09499589,0.09748171,0.09756818,0.09756818,0.09861013,0.10193147,0.10211796,0.10277547,0.10379659,0.10393602,0.10397469,0.10364373,0.10368016,0.10362235,0.10387504,0.10385431,0.10387288,0.10423139,0.10483475,0.10570517,0.10573617,0.10569312,0.10572714,0.10597040,0.10573924,0.10551367,0.10573499,0.10602269,0.10765947,0.10721005,0.10703524,0.10824609,0.10933141,0.10936178,0.10957693,0.10874663,0.10875077))
DT[, target := as.factor(target)] #Target Variable as factor is required

task <- TaskClassif$new(id='pizza', backend = DT, target = "target", positive = '1') 
#Select an algo and a filter
randF = lrn("classif.randomForest", predict_type = "prob")#
filter1 = mlr_pipeops$get("filter", filter = mlr3filters::FilterVariance$new(),param_vals = list(filter.cutoff = 0.05))
#Construct a simple graph
graph = filter1 %>>% 
  PipeOpLearner$new(lrn("classif.randomForest"), id = "randF")
#graph$plot()
#Construct a learner and train it
learner = GraphLearner$new(graph)
learner$train(task)

This give the error: 'Error in reformulate(attributes(Terms)$term.labels) : 'termlabels' must be a character vector of length at least one'

I have the impression, that the task- object of mlr3 somehow doesnt interact well with the graph. The error then comes from the randomForest classifier, but to me it seems like the data was not properly handed over to it. But thats just a theory of mine. I may alter the question if its not clear enough.

Pizza-Fan
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  • It looks like your filter is removing the only feature. You don't need feature filtering anyway with just a single feature. – Lars Kotthoff Aug 08 '21 at 18:42
  • @LarsKotthoff, you saved me. Maybe this would be a nice feature for mlr3 having a suggestion in the error message, that maybe predictors got filtered out. – Pizza-Fan Aug 09 '21 at 17:33

1 Answers1

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Your filter is removing the only feature, and feature filtering is not necessary if there is only a single feature.

Lars Kotthoff
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