support mlr3 #13

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1. simonpcouch commented on Jul 18, 2022

   

   simonpcouch

   on Jul 18, 2022

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   After spending more time with this, I think our best approach here will to wait for more machinery from mlr3 folks before implementing anything more principled. Prediction in a new session works fine, so this shouldn't be a pain point!

   library(mlr3)
   
   task <- tsk("mtcars")
   fit <- lrn("regr.rpart")
     
   # train a model of this learner for a subset of the task
   fit$train(task, row_ids = 1:26)
   
   callr::r(
     function(fit) {
       library(mlr3)
       
       predict(fit, mtcars[27:32,])
     },
     args = list(fit = fit)
   )
   #> [1] 27.71429 16.87368 16.87368 16.87368 16.87368 16.87368

   ^{Created on 2022-07-18 by the reprex package (v2.0.1)}
2. simonpcouch

   closed this as completedon Jul 18, 2022
3. simonpcouch commented on Jul 19, 2022

   

   simonpcouch

   on Jul 19, 2022

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   Look for the fitted model object slot, these should wrap like caret or parsnip.
4. simonpcouch

   reopened this on Jul 19, 2022
5. sebffischer commented on Jan 24, 2023

   

   sebffischer

   on Jan 24, 2023

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   I have started a discussion in mlr3 about serialization here: mlr-org/mlr3#891
   If we implement something like this, adding a bundle method for mlr3 Learners should be straightforward! :)

   👍React with 👍2Reacted by Julia Silge and Simon P. Couch

   🎉React with 🎉1Reacted by Lukas Burk
6. juliasilge

   added

   featurea feature request or enhancementa feature request or enhancement

   on Mar 7, 2023