Error in .__Graph__predict(self = self, private = private, super = super, : Cannot predict, Graph has not been trained yet

> # Load a binary classification task
> set.seed(1)
> library(mlr3calibration)
> library(mlr3verse)
> data("Sonar", package = "mlbench")
> task = as_task_classif(Sonar, target = "Class", positive = "M")
> splits = partition(task)
> task_train = task$clone()$filter(splits$train)
> task_test = task$clone()$filter(splits$test)
>
> # Initialize the uncalibrated learner
> learner_uncal <- lrn("classif.xgboost", nrounds = 50, predict_type = "prob")
>
> # Initialize the calibrated learner
> rsmp <- rsmp("cv", folds = 5)
> learner_cal <- as_learner(PipeOpCalibration$new(learner = learner_uncal,
+ method = "beta",
+ rsmp = rsmp))
>
> # Set ID's for the learners
> learner_uncal$id <- "Uncalibrated Learner"
> learner_cal$id <- "Calibrated Learner"
>
> # Train the learners
> learner_uncal$train(task_train)
> learner_cal$train(task_train)
[1] -456.1043
[1] 15.28018
[1] -39.84187
[1] 9.989902
[1] -19.32693
[1] 26.69576
[1] -27.22082
[1] 2.789276
[1] -60.92008
[1] 64.59263
Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred
This happened in PipeOp classif.xgboost's $train()
>
> # Predict the Learners
> preds_uncal <- learner_uncal$predict(task_test)
> preds_cal <- learner_cal$predict(task_test)
Error in .__Graph__predict(self = self, private = private, super = super, :
Cannot predict, Graph has not been trained yet

Based on the implementation provided in the above code, I was unable to achieve complete and accurate predictions, as the model did not perform as expected during the calibration process

Start by running the reproduction in the issue, focusing on PipeOpCalibration$new and the learner_train/predict calls. Trace why the calibrated learner's graph is not considered trained after learner_cal$train(task_train). Done means the reported calibrated prediction completes without the graph-not-trained error.

Written by the indexing model from the issue text.