Releases · mlr-org/mlr3

Result containers ResampleResult and BenchmarkResult now optionally remove
the DataBackend of the Tasks in order to reduce file size and memory
footprint after serialization. To remove the backends from the containers,
set store_backends to FALSE in resample() or benchmark(),
respectively. Note that this behaviour will eventually will be the default for
future releases.
Prediction objects generated by Learner$predict_newdata() now have row ids
starting from 1 instead auto incrementing row ids of the training task.
as.data.table.DictionaryTasks now returns an additional column properties.
Added flag conditions to ResampleResult$score() and
BenchmarkResult$score() to allow to work with failing learners more
conveniently.

New methods for Task: $set_col_roles and $set_row_roles as a replacement
for the deprecated and less flexible $set_col_role and $set_row_role.
Learners can now have a timeout (#556).
Removed S3 method friedman.test.BenchmarkResult() in favor of the new
mlr3benchmark package.

MeasureOOBError now has set property minimize to TRUE.
New learner property "featureless" to tag learners which can operate on
featureless tasks.
Fixed [ResampleResult] ignoring argument predict_sets for returned
[Prediction] objects.
Compability with new version of lgr.

Updated properties of featureless learners to apply it on all feature types
(did not work on POSIXct columns).
Fixed measures being calculated as NaN for BenchmarkResult for resamplings
with a single iteration (#551).
Fixed a bug where a broken heuristic disabled nested parallelization via
package future (mlr3tuning#270).
ResampleResult and BenchmarkResult now share a common interface to store
the experiment results. Manual construction is still possible with helper
function as_result_data()
Fixed deep cloning of ResamplingCV and ResamplingRepeatedCV.
New measure classif.prauc (area under precision-recall curve).
Removed dependency on orphaned package bibtex.

Compact in-memory representation of R6 objects to save space when
saving objects via saveRDS() or serialize().
Objects in containers like ResampleResult or BenchmarkResult are now
de-duplicated for an optimized serialization.
Fixed data set breast_cancer: all factor features are now
correctly stored as ordered factors.
Added a new utility function convert_task().

Added classification task breast_cancer
Added ResamplingLOO for leave-one-out resampling.
Regression now supports predict type "distr" using the distr6 package.
Fixed ResamplingBootstrap in combination with grouping (#514).
Fixed plot method of TaskGeneratorMoons.
Added hyperparameter keep_model to learners "classif.rpart" and
"regr.rpart".

Added new task generators ("cassini", "circle", "simplex", "spirals",
and "moons").
Columns in tasks generated by task generators have been renamed to be more
consistent.
Added a plot() method for most task generators.
Corrected data in task german_credit (#514).

Package future.apply is now imported (instead of suggested).
This is necessary to ensure reproducibility: This way exactly the same result
is calculated, independent of the parallel backend.
Fixed a bug where prediction on new data for a task with blocking information
raised an exception (#496).
New binding: Task$order.

Added S3 methods to combine ResampleResults and BenchmarkResults with
c().
Fixed a bug where automatic generation of row ids could lead to duplicated ids
via Task$predict_newdata()/Task$rbind() (#423).

Switched to new roxygen2 documentation format for R6 classes.
resample() and benchmark() now support progress bars via the package
progressr.
Row ids now must be numeric. It was previously allowed to have character row
ids, but this lead to confusion and unnecessary code bloat. Row identifiers
(e.g., to be used in plots) can still be part of the task, with row role
"name".
Row names can now be queried with Task$row_names.
DataBackendMatrix now supports to store an optional (numeric) dense part.
Added new method $filter() to filter ResampleResults to a subset of
iterations.
Removed deprecated character() -> object converters.
Empty test sets are now handled separately by learners (#421). An empty
prediction object is returned for all learners.
The internal train and predict function of Learner now should be implemented
as private method: instead of public methods train_internal and
predict_internal, private methods .train and .predict are now
encouraged.
It is now encouraged to move some internal methods from public to private:
- Learner$train_internal should now be private method $.train.
- Learner$predict_internal should now be private method $.predict.
- Measure$score_internal should now be private method $.score.
  The public methods will be deprecated in a future release.
Removed arguments from the constructor of measures classif.debug and
classif.costs. These can be set directly by msr().

Releases: mlr-org/mlr3