NEWS.md
In schalkdaniel/compboost: Efficient Component-Wise Boosting Implementation

Release Notes

10.03.2023 \ The package now supports saving without data (see the project page or vignettes for details).
08.03.2023 \ A mlr3 learner is now available.
08.03.2023 \ Exclude CustomCpp base learner until further notice (due to critical errors).
02.03.2023 \ Bigger refactoring of the documentation and smaller bugfixes.
24.02.2023 \ Removing the dev branch and return to simple feature branch workflow.
15.01.2023 \ Happy new year! Finally, it is possible to save and load Compboost objects as JSON.
21.12.2022 \ New methods for transforming data are now available (cboost$transformData(newdat)). Additionally, methods for accessing the meta data of a base learner were also added (cboost$baselearner_list$blfactory$factory$getMeta()).
09.12.2022 \ Re-writing the documentation for the Compboost class.
20.04.2022 \ Adding a new "intercept base learner". This base learner can be used to additionally add an intercept. This makes sense if, e.g. linear functions without intercept are added.
20.04.2022\ A lot happens until the last entry. The development was done more in a rush without prober documentation. In the future, a bigger update will come containing an updated documentation and a method to store models.
30.04.2020 \ Refactoring core code basis:
- Working just with protected/private class member and corresponding setter and getter.
- Consistent naming convention of class member.
- Simplification of constructing classes by using a more flexible class inheritance structure.
24.04.2020 \ It is now possible to choose between to solvers for fitting the base-learner. The two options are the Cholesky decomposition and to use the inverse.
15.04.2020 \ A new base-learner BaselearnerCategoricalBinary is now available. This base-learner reduces the memory load and improves the runtime.
03.04.2020 \ Binning can now be used for spline base-learner to reduce memory load and increase runtime performance. See ?compboost::BaselearnerPSpline.
12.04.2019 \ The Huber loss is now available for training.
08.04.2019 \ Quantile loss for quantile regression.
01.03.2019 \ It is now possible to use parallel optimizer to speed up training.

23.01.2019 \ Most parts of compboost are now using smart pointer.
23.01.2019 \ Style: Change . to _, e.g. change n.knots to n_knots, to be more consistent with C++ syntax.
19.01.2019 \ There is now a new Response class to be more versatile for given tasks.
14.12.2018 \ To track the out of bag risk is now easy controllable through a argument oob.fraction. The paths of inbag vs. out of bag risk can be plotted with plotInbagVsOobRisk()
28.11.2018 \ It is now possible to directly access the logger data with getLoggerData() and to calculate and plot feature importance with calculateFeatureImportance() and plotFeatureImportance().
27.11.2018 \ Fix bug in the spline base-learner for out of range values.
09.11.2018 \ Adding a new optimizer OptimizerCoordinateDescentLineSearch which conducts line search after each iteration.
09.11.2018 \ Improve trace of the training process by passing logger identifier directly to C++.

Initial release

19.07.2018 \ Compboost now uses sparse matrices for splines to reduce memory load.
29.06.2018 \ Compboost API is almost ready to use.
14.06.2018 \ Update naming GreedyOptimizer -> OptimizerCoordinateDescent and small typos.
30.03.2018 \ Compboost is now ready to do binary classification by using the BernoulliLoss.
29.03.2018 \ Upload C++ documentation created by doxygen.
28.03.2018 \ P-Splines are now available as base-learner. Additionally the Polynomial and P-Spline learner are speed up using a more general data structure which stores the inverse once and reuse it for every iteration.
21.03.2018 \ New data structure with independent source and target.
01.03.2018 \ Compboost should now run stable and without memory leaks.
07.02.2018 \ Naming of the C++ classes. Those are matching the R classes now.
29.01.2018 \ Update naming to a more consistent scheme.
26.01.2018 \ Add printer for the classes.
22.01.2018 \ Add inbag and out of bag logger.
21.01.2018 \ New structure for factories and base-learner. The function InstantiateData is now member of the factory, not the base-learner. This should also speed up the algorithm, since we don't have to check whether data is instantiated or not. We can do that once within the constructor. Additionally, it should be more clear now what the member does since there is no hacky base-learner helper necessary to instantiate the data.