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 $filter() to filter ResampleResults to a subset of iterations.
Removed deprecated character() -> object converters.
Empty test sets are now handled separately by learners (#421). An empty prediction object is returned for all learners.
The internal train and predict function of Learner now should be implemented as private method: instead of public methods train_internal and predict_internal, private methods .train and .predict are now encouraged.
Learner$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().