This posts gives an overview by listing the recent release notes of mlr3 packages from the last quarter.
Due to the high amount of packages in the mlr3 ecosystem, it is hard to keep up with the latest changes across all packages. This posts gives an overview by listing the recent release notes of mlr3 packages from the last quarter. Note that only CRAN packages are listed here and the sort order is alphabetically.
Description: Black-Box Optimization Toolkit
$print() method of OptimInstance omits unnecessary columns now.$clear() method of OptimInstance raised an error.$clear() method of Archive missed to reset the $start_time field.Optimizer and Terminator objects have the optional field $label now.as.data.table() functions for objects of class Dictionary have been extended with additional columns.as.data.table.DictionaryTerminator() function.$.status() method of TerminatorRunTime and TerminatorClockTime was not in a consistent unit. The return is in seconds from now on.as.data.table.DictionaryOptimizer() function.$help() method which opens the manual page of an Optimizer.$nds_selection() method to Archive.Codomain class that allows extra parameters.ObjectiveRFun are named.OptimInstance, Archive and Objective objects were not cloned properly.$param_classes, $properties and $packages of Optimizer objects are read-only now.branin() function is exported now.Description: Machine Learning in R - Next Generation
label, i.e. Task, TaskGenerator, Learner, Resampling, and Measure.as.data.table() methods for objects of class Dictonary have been extended with additional columns.as_task_classif.formula() and as_task_regr.formula() now remove additional atrributes attached to the data which caused some some learners to break.$train() and $predict() methods of a Learner. This ensures that package loading errors are properly propagated and not affected by encapsulation (#771)."evaluate" (#763).as_task_classif() and as_task_regr() now support the construction of tasks using the formula interface, e.g. as_task_regr(mpg ~ ., data = mtcars) (#761)."validation" has been renamed to "holdout". In the next release, mlr3 will start switching to the now more common terms "train"/"validation" instead of "train"/"test" for the sets created during resampling.ResampleResult and BenchmarkResult.resample() and benchmark() got a new argument clone to control which objects to clone before performing computations.data.frame to Task in as_task_classif() and as_task_regr(). A warning is signaled if any column contains infinite values.Description: Filter-based feature selection for mlr3
FilterSelectedFeatures which makes use of embedded feature selection methods of learners. See the help page for more details (#102)NA as task type. This makes it possible to use other tasks than "regr" or "classif" for certain filters, e.g. FilterVariance (#106)Description: Wrapper feature selection for mlr3
FSelector objects as method in fselect() and auto_fselector().$label to FSelectors.fselect() function.$help() method which opens manual page of a FSelector.as.data.table.DictionaryFSelector function.min_features parameter to FSelectorSequential.store_models flag to fselect().store_x_domain flag.Description: Hyperband for ‘mlr3’
adjust_minimum_budget flag in OptimizerSuccessiveHalving. The minimum budget is adjusted in the base stage to use the maximum budget in last stage.repetitions parameter to specify the exact number of repetitions. Replaced the repeats parameter.TunerHyperband evaluates configurations of same budget across brackets in parallel now.repeats parameter to repeat runs of successive halving and hyperband until termination.Description: Probabilistic Supervised Learning for ‘mlr3’
model now called keep_model.TaskSurv$kaplan methodsimsurv task that made it impossible to predict the target$distr called for a learner that does not support this return typeas_task_dens and as_prediction_denst_max and p_max to Graf, Schmid and Integrated Log-loss as an alternative to times. t_max is equivalent to times = seq(t_max) and p_max is the proportion of censoring to integrate up to in the dataset.Description: Support for Spatial Objects Within the ‘mlr3’ Ecosystem
terra update.Description: Spatiotemporal resampling methods for mlr3
autoplot() support for "groups" column role in rsmp("cv")Description: Tuning for ‘mlr3’
Tuner objects as method in tune() and auto_tuner().Tuner to help page of bbotk::Optimizer.Tuner objects have the optional field $label now.as.data.table() functions for objects of class Dictionary have been extended with additional columns.as.data.table.DictionaryTuner function.$help() method which opens the manual page of an Tuner.as_search_space() function to create search spaces from Learner and ParamSet objects. Allow to pass TuningSpace objects as search_space in TuningInstanceSingleCrit and TuningInstanceMultiCrit.mlr3::HotstartStack can now be removed after tuning with the keep_hotstart_stack flag.Archive stores errors and warnings of the learners.auto_tuner() and tune_nested().$assign_result() method in TuningInstanceSingleCrit when search space is empty.TuningInstanceSingleCrit.TuningInstanceMultiCrit$assign_result().store_models flag to auto_tuner()."noisy" property to ObjectiveTuning.Description: Search Spaces for Hyperparameter Tuning
as.data.table.TuningSpace() function.TuningSpace objects have the optional field $label now.$help() method which opens the manual page of a TuningSpace.glmnet and kknn to default collection.as_search_space() function to create search spaces from TuningSpace objects.subsample hyperparameter is tuned on a logarithmic scale now. The lower bound of alpha is reduced from 1e-4 to 1e-3. The tuning range of the lambda hyperparameter was 0.1 to 1. From now on, lambda is tuned from 1e-3 to 1e3 on a logarithmic scale.mtry.ratio hyperparameter to tuning spaces of the ranger learner.$print() method to TuningSpace objects.Description: Easily Install and Load the ‘mlr3’ Package Family
Description: Visualizations for ‘mlr3’
default_values() function to extract default values from ParamSet objects.description.ParamHelpers is also loaded.For attribution, please cite this work as
Fischer (2022, April 25). mlr-org: mlr3 package updates - q1/2022. Retrieved from https://mlr-org.github.io/mlr-org-website/posts/2022-04-25-mlr3-package-updates-q12022/
BibTeX citation
@misc{fischer2022mlr3,
author = {Fischer, Sebastian},
title = {mlr-org: mlr3 package updates - q1/2022},
url = {https://mlr-org.github.io/mlr-org-website/posts/2022-04-25-mlr3-package-updates-q12022/},
year = {2022}
}