release: 0.17.1 (#985)

* release: 0.17.1

* chore: silence CRAN checks

Author: be-marc

.Rbuildignore

@@ -22,3 +22,4 @@
 ^CONTRIBUTING.md
 ^cran-comments\.md$
 ^CRAN-SUBMISSION$
+^benchmark$

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: mlr3
 Title: Machine Learning in R - Next Generation
-Version: 0.17.0.9000
+Version: 0.17.1
 Authors@R:
   c(
     person("Michel", "Lang", , "[email protected]", role = c("cre", "aut"),

NEWS.md

@@ -1,5 +1,6 @@
-# mlr3 (development version)
+# mlr3 0.17.1
 
+* Remove `data_prototype` when resampling from `learner$state` to reduce memory consumption.
 * Reduce number of threads used by `data.table` and BLAS to 1 when running `resample()` or `benchmark()` in parallel.
 * Optimize runtime of `resample()` and `benchmark()` by reducing the number of hashing operations.
 

R/ResultData.R

@@ -49,9 +49,10 @@ ResultData = R6Class("ResultData",
           uhashes = data.table(uhash = unique(data$uhash))
           setkeyv(data, c("uhash", "iteration"))
 
-          data[, task_hash := task[[1]]$hash, by = "uhash"]
-          data[, learner_phash := learner[[1]]$phash, by = "uhash"]
-          data[, resampling_hash := resampling[[1]]$hash, by = "uhash"]
+          task = learner = resampling = NULL
+          data[, "task_hash" := task[[1]]$hash, by = "uhash"]
+          data[, "learner_phash" := learner[[1]]$phash, by = "uhash"]
+          data[, "resampling_hash" := resampling[[1]]$hash, by = "uhash"]
 
           tasks = data[, list(task = .SD$task[1L]),
             keyby = "task_hash"]