mlr_reflections were broken when an extension package was not loaded on the workers.
Extension packages must now register themselves in the mlr_reflections$loaded_packages field.data_format and data_formats for Learner, Task, and DataBackend classes.partition() function creates training, test and validation sets now.Task$col_info.Learner$predict (#943).$internal_valid_task can now be set to an integer vector.$predict_sets (#1094).
This is relevant for measures that only extract information from the model of a learner (such as internal validation scores or AIC / BIC)$divide() methodTask$cbind() now works with non-standard primary keys for data.frames (#961)."info" instead of "debug" (#972).regr.pinball here and in mlr3measures.mu_auc here and in mlr3measures.msr("regr.rsq").classif.debug and regr.debug have new methods $importance() and $selected_features() for testing, also in downstream packages.default_fallback().$set_col_roles() and $col_roles.$encapsulate(method, fallback) method.
The $fallback field is read-only now and the encapsulate status can be retrieved from the $encapsulation field."primary_iters"$obs_loss.
This is possible for Prediction, ResampleResult and BenchmarkResult.Measures now also return a vector of numerics.msr("classif.mcc")."marshal" property, which allows learners to process models so they can be serialized.
This happens automatically during resample() and benchmark().default_values.Learner() function.lgr package.mlr_learners respects prototype arguments recently added in mlr3misc.resample().data.table tests on mac.data_prototype when resampling from learner$state to reduce memory consumption.data.table and BLAS to 1 when running resample() or benchmark() in parallel.resample() and benchmark() by reducing the number of hashing operations.HotstartStack anymore when the model is missing.hotstart_threshold are not added to the HotstartStack anymore.learner$state$train_time in hotstarted learners is now only the time of the last training.HotstartStack did not work with column roles set in the task.design of benchmark() can now include parameter settings.packageVersion().col_info to allow adding new methods for backends."mlr3.exec_chunk_bins" option to split the resampling iterations into a number of bins.data.table() is now re-exported."try", which works similar to "none" but captures errorspaired to benchmark_grid() function, which can be used to create a benchmark design, where
resamplings have been instantiated on tasks.ResultData for as_resample_result() converter.list for as_resample_result() converter.print method to make the output
more readable.distr6.GraphLearner.as_prediction_classif() for data.frame() input (#872).Learner during train for early
stopping.mauc_aunu, mauc_aunp, mauc_au1u, mauc_au1p.classif.costs does not require a Task anymore.as_task_unsupervised()mlr_reflections."mlr3.exec_random" and
"mlr3.exec_chunk_size"). These options are passed down to the respective map
functions in package future.apply.head() and tail() methods for Task.label, i.e. Task,
TaskGenerator, Learner, Resampling, and Measure.as.data.table() methods for objects of class Dictonary have been extended
with additional columns.as_task_classif.formula() and as_task_regr.formula() now remove additional
atrributes attached to the data which caused some some learners to break.$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).default_values() function to extract parameter default values from
Learner objects."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.(classif|regr|surv).xgboost with hyperparameter nrounds updated)
can now optionally store a stack of trained learners to be used to hotstart
their training. Note that this feature is still somewhat experimental.
See HotstartStack and #719.sim.jaccard (Jaccard Index) and sim.phi (Phi coefficient) (#690).predict_newdata() now also supports DataBackend as input.install_pkgs() to install required packages. This generic works
for all objects with a packages field as well as ResampleResult and
BenchmarkResult (#728).regr.debug for debugging.Task method $set_levels() to control how data with factor columns
is returned, independent of the used DataBackend.NA if prerequisite are not met (#699).
This allows to conveniently score your experiments with multiple measures
having different requirements.%.Task$label().
These will be used in visualizations in the future.Task$add_strata().partition() to split a task into a training and test
set.loglik() for class Learner."aic" and "bic" to compute the Akaike Information Criterion
or the Bayesian Information Criterion, respectively.ResamplingCustomCV. Creates a custom resampling split
based on the levels of a user-provided factor variable.encapsulate for resample() and benchmark() to conveniently
enable encapsulation and also set the fallback learner to the
featureless learner. This is simply for convenience, configuring each learner
individually is still possible and allows a more fine-grained control (#634,
#642).parallel_predict for Learner to enable parallel predictions via
the future backend. This currently is only enabled while calling the
$predict() or $predict_newdata methods and is disabled during resample()
and benchmark() where you have other means to parallelize.$data in
ResampleResult and BenchmarkResult to simplify the API and avoid
confusion. The converter as.data.table() can be used instead to access the
internal data.beta.ordered in Task$data() from TRUE to FALSE.ResamplingRepeatedCV$folds() (#643).uri. This role be split up into multiple
roles by the mlr3keras package.as.data.table.Resampling method."row_id" to "row_ids" in the as.data.table() methods
for PredictionClassif and PredictionRegr (#547).as_prediction_classif() and as_prediction_regr() to
reverse the operation of as.data.table.PredictionClassif() and
as.data.table.PredictionRegr().learner$predict_newdata() is not mandatory
anymore (#563).Task$data() defaults to return only active rows and columns, instead of
asserting to only return rows and columns. As a result, the $data() method
can now also be used to query inactive rows and cols from the DataBackend.uri which is intended to point to external
resources, e.g. images on the file system.set_threads() to control the number of threads during calls to
external packages. All objects will be migrated to have threading disabled in
their defaults to avoid conflicting parallelization techniques (#605).mlr3.debug: avoid calls to future in resample() and
benchmark() to improve the readability of tracebacks.mlr3.allow_utf8_names: allow non-ascii characters in
column names in tasks.ResampleResult and BenchmarkResult now optionally remove
the DataBackend of the Tasks in order to reduce file size and memory
footprint after serialization. To remove the backends from the containers,
set store_backends to FALSE in resample() or benchmark(),
respectively. Note that this behavior will eventually will be the default for
future releases.Learner$predict_newdata() now have row ids
starting from 1 instead auto incremented row ids of the training task.as.data.table.DictionaryTasks now returns an additional column properties.conditions to ResampleResult$score() and
BenchmarkResult$score() to allow to work with failing learners more
conveniently.Task: $set_col_roles and $set_row_roles as a replacement
for the deprecated and less flexible $set_col_role and $set_row_role.friedman.test.BenchmarkResult() in favor of the new
mlr3benchmark package.MeasureOOBError now has set property minimize to TRUE."featureless" to tag learners which can operate on
featureless tasks.predict_sets for returned
[Prediction] objects.lgr.NaN for BenchmarkResult for resamplings
with a single iteration (#551).future (mlr3tuning#270).ResampleResult and BenchmarkResult now share a common interface to store
the experiment results. Manual construction is still possible with helper
function as_result_data()ResamplingCV and ResamplingRepeatedCV.classif.prauc (area under precision-recall curve).bibtex.saveRDS() or serialize().ResampleResult or BenchmarkResult are now
de-duplicated for an optimized serialization.breast_cancer: all factor features are now
correctly stored as ordered factors.convert_task().breast_cancerResamplingLOO for leave-one-out resampling."distr" using the distr6 package.ResamplingBootstrap in combination with grouping (#514).TaskGeneratorMoons.keep_model to learners "classif.rpart" and
"regr.rpart"."cassini", "circle", "simplex", "spirals",
and "moons").plot() method for most task generators.german_credit (#514).future.apply is now imported (instead of suggested).
This is necessary to ensure reproducibility: This way exactly the same result
is calculated, independent of the parallel backend.Task$order.classif.bbrier (binary Brier score) and classif.mbrier
(multi-class Brier score).ResamplingInsample.TaskUnsupervised.ResampleResults and BenchmarkResults with
c().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 $.score.
The public methods will be deprecated in a future release.Removed arguments from the constructor of measures classif.debug and
classif.costs. These can be set directly by msr().
We have published an article about mlr3 in the Journal of Open Source
Software: https://joss.theoj.org/papers/10.21105/joss.01903.
See citation("mlr3") for the citation info.
New method Learner$reset().
New method BenchmarkResult$filter().
Learners returned by BenchmarkResult$learners are reset to encourage the
safer alternative BenchmarkResult$score() to access trained models.
Fix ordering of levels in PredictionClassif$set_threshold() (triggered an
assertion).
Switched from package Metrics to package mlr3measures.
Measures can now calculate all scores using micro or macro averaging (#400).
Measures can now be configured to return a customizable performance score
(instead of NA) in case the score cannot be calculated.
Character columns are now treated differently from factor columns.
In the long term, character() columns are supposed to store text.
Fixed a bug triggered by integer grouping variables in Task (#396).
benchmark_grid() now accepts instantiated resamplings under certain
conditions.
Task$set_col_roles() and Task$set_row_roles() are now deprecated.
Instead it is recommended for now to work with the lists Task$col_roles and
Task$row_roles directly.
Learner$predict_newdata() now works without argument task if the learner
has been fitted with Learner$train() (#375).
Names of column roles have been unified ("weights", "label",
"stratify" and "groups" have been renamed).
Replaced MeasureClassifF1 with MeasureClassifFScore and fixed a bug in the
F1 performance calculation (#353). Thanks to @001ben for reporting.
Stratification is now controlled via a task column role (was a parameter of
class Resampling before).
Added a S3 predict() method for class Learner to increase
interoperability with other packages.
Many objects now come with a $help() which opens the respective manual page.
It is now possible to predict and score results on the training set or on both
training and test set.
Learners can be instructed to predict on multiple sets by setting
predict_sets (default: "test"). Measures operate on all sets specified in
their field predict_sets (default: "test").
ResampleResult$prediction and ResampleResult$predictions() are now methods
instead of fields, and allow to extract predictions for different predict
sets.
ResampleResult$performance() has been renamed to ResampleResult$score()
for consistency.
BenchmarkResult$performance() has been renamed to BenchmarkResult$score()
for consistency.
Changed API for (internal) constructors accepting paradox::ParamSet().
Instead of passing the initial values separately, the initial values must now
be set directly in the ParamSet.
Deprecated support of automatically creating objects from strings.
Instead, mlr3 provides the following helper functions intended to ease the
creation of objects stored in dictionaries:
tsk(), tgen(), lrn(), rsmp(), msr().
BenchmarkResult now ensures that the stored ResampleResults are in a
persistent order. Thus, ResampleResults can now be addressed by their
position instead of their hash.
New field BenchmarkResult$n_resample_results.
New field BenchmarkResult$hashes.
New method Task$rename().
New S3 generic as_benchmark_result().
Renamed Generator to TaskGenerator.
Removed the control object mlr_control().
Removed ResampleResult$combine().
Removed BenchmarkResult$best().