Skip to content

Latest commit

 

History

History
134 lines (93 loc) · 4.88 KB

NEWS.md

File metadata and controls

134 lines (93 loc) · 4.88 KB

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.

compboost 0.1.1

  • 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++.

compboost 0.1.0

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.