如何使用 mlr3 估算数据并使用 NA 值进行预测？

Question

I followed the documentation of mlr3 regarding the imputation of data with pipelines. However, the mode that I have trained does not allow predictions if a one column is NA

Do you have any idea why it doesn't work?

train step

library(mlr3)
library(mlr3learners)
library(mlr3pipelines)


data("mtcars", package = "datasets")
data = mtcars[, 1:3]
str(data)
task_mtcars = TaskRegr$new(id="cars", backend = data, target = "mpg")


imp_missind = po("missind")
imp_num     = po("imputehist", param_vals =list(affect_columns = selector_type("numeric")))
scale = po("scale")
learner = lrn('regr.ranger')

graph = po("copy", 2) %>>% 
  gunion(list(imp_num %>>% scale,imp_missind)) %>>%
  po("featureunion") %>>%
  po(learner)
graph$plot()

graphlearner = GraphLearner$new(graph)

predict step

data = task_mtcars$data()[12:12,]
data[1:1, cyl:=NA]
predict(graphlearner, data)

The error is

Error: Missing data in columns: cyl.

Answer 1

The example in the mlr3gallery seems to work for your case, so you basically have to switch the order of imputehist and missind .

Another approach would be to set the missind's which hyperparameter to "all" in order to enforce the creation of an indicator for every column.

This is actually a bug, where missind returns the full task if trained on data with no missings (which in turn then overwrites the imputed values). Thanks a lot for spotting it. I am trying to fix it here PR