Quarterly mlr3 package updates.
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 FSelector
s.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_dens
t_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} }