# mlr3 0.11.0

* Added a `as.data.table.Resampling` method.
* Renamed column `"row_id"` to `"row_ids"` in the `as.data.table()` methods
  for `PredictionClassif` and `PredictionRegr` (#547).
* Added converters `as_prediction_classif()` and `as_prediction_regr()` to
  reverse the operation of `as.data.table.PredictionClassif()` and
  `as.data.table.PredictionRegr()`.
* Specifying a weight column during `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`.
* New (experimental) column role `uri` which is intended to point to external
  resources, e.g.  images on the file system.
* New helper `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).
* New option `mlr3.debug`: avoid calls to `future` in `resample()` and
  `benchmark()` to improve the readability of tracebacks.
* New experimental option `mlr3.allow_utf8_names`: allow non-ascii characters in
  column names in tasks.


# mlr3 0.10.0

* 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.

# mlr3 0.9.0

* 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.

# mlr3 0.8.0

* `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`.

# mlr3 0.7.0

* 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`.

# mlr3 0.6.0

* 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()`.

# mlr3 0.5.0

* 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"`.

# mlr3 0.4.0

* 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).

# mlr3 0.3.0

* 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`.

# mlr3 0.2.0

* Some handy cheat sheets can now be downloaded from the project homepage.
* Added new measures `classif.bbrier` (binary Brier score) and `classif.mbrier`
  (multi-class Brier score).
* Added new Resampling: `ResamplingInsample`.
* Added base class for unsupervised tasks: `TaskUnsupervised`.

# mlr3 0.1.8

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

# mlr3 0.1.7

* 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 `ResampleResult`s 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()`.


# mlr3 0.1.6

* 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).


# mlr3 0.1.5

* 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.

# mlr3 0.1.4

* `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.


# mlr3 0.1.3

* 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`.


# mlr3 0.1.2

* 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 `ResampleResult`s are in a
  persistent order. Thus, `ResampleResult`s 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()`.


# mlr3 0.1.1

* Initial upload to CRAN.
