Allow bagging wrapper to work with no features in feature selection

[larskotthoff]

Example from https://stackoverflow.com/questions/44208492/mlr-package-r-feature-selection-sequential-forward-search-error-must-have-at-l:

d <- data.frame(a = rnorm(1000, mean = 1),
                    b = rnorm(1000, mean = 2),
                    c = rnorm(1000, mean = 3),
                    target = as.factor(rbinom(1000, 1, prob = 0.5)))

t <- makeClassifTask(data = d,
                     target = 'target',
                     positive = '1')

logreg.lrn <- makeLearner('classif.logreg')
logreg_bagged.lrn <- makeBaggingWrapper(logreg.lrn)

cntrl.sfs <- makeFeatSelControlSequential(method = "sfs",
                                          alpha = 0.01,
                                          max.features = 10,
                                          maxit = 3)

logreg_bagged_featsel.lrn <- makeFeatSelWrapper(logreg_bagged.lrn,
                                                resampling = makeResampleDesc('CV',
                                                                              iters = 3),
                                                measures = mmce,
                                                control = cntrl.sfs)

train(logreg_bagged_featsel.lrn, t)

gives

Assertion on '.newdata' failed: Must have at least 1 cols, but has 0 cols.

Should at least mention in documentation that this isn't supported.

[annette987]

I have another example of this. Using sequential forward selection with glmnet doesn't work as glmnet expects at least two variables in the matrix passed to it (see code and error below). So you cannot start with an empty model. Is it possible to modify the FeatSelWrapper so that it can start with one or more variables already in the model - i.e. variables that must be included ?

library(survival)
library(mlr)

data(veteran)
task_id = "MAS"
surv.task <- makeSurvTask(id = task_id, data = veteran, target = c("time", "status"))
ridge.lrn <- makeLearner(cl="surv.cvglmnet", id = "ridge", predict.type="response", alpha = 0, nfolds=5)

inner = makeResampleDesc("CV", iters=5, stratify=TRUE)	# Tuning
outer = makeResampleDesc("CV", iters=5, stratify=TRUE)	# Tuning

ctrl.sfs = makeFeatSelControlSequential(method="sfs", maxit=20L, max.features=20)
wrap.sfs.lrn = makeFeatSelWrapper(
  ridge.lrn,
  resampling = inner, 
  measures = list(cindex), 
  control = ctrl.sfs,
  show.info = FALSE
)

model_id = 'ridge.sfs.featsel'
learners = list(wrap.sfs.lrn)
bmr = benchmark(learners, surv.task, outer, list(cindex), show.info = TRUE, models=TRUE, keep.extract = TRUE)

Task: MAS, Learner: ridge.featsel
Resampling: cross-validation
Measures:             cindex    
Error in glmnet(x, y, weights = weights, offset = offset, lambda = lambda,  : 
  x should be a matrix with 2 or more columns

[pat-s]

Both issues are very niche and I'll label this as "wontfix".

The second one is even model specific and even more niche.
If this is really needed, I am happy to review a PR which solves this.

Otherwise, please file a request in {mlr3} to add support for this.