Skip to content

Latest commit

 

History

History
103 lines (60 loc) · 4.3 KB

CHANGELOG.md

File metadata and controls

103 lines (60 loc) · 4.3 KB
Raw

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

Note: We try to adhere to these practices as of version [v0.2.1].

Version [1.2.0] - 2024-12-03

Changed

  • Largely removed unicode characters from code base. [#134]
  • Removed legacy v1.9 from CI testing. [#134]

Added

  • Added general support for MLJ [#126] [#134]

Version [1.1.1] - 2024-09-12

Changed

  • Fixed an issue in MLJFlux implementation that led to long compute times for predictions. [#122]

Version [1.1.0] - 2024-09-03

Changed

  • Predict function now returns predictive distribution that includes observational noise estimates for regression. [#116]

Added

  • Adds support for calibration. [#90]

Version [1.0.2] - 2024-08-12

Added

  • added TaijaPlotting to the docs env

Changed

  • modified the MLJFlux.train function so that it now properly return a trained chain [#112]

Version [1.0.0] - 2024-07-22

Changed

  • added the option to return meand and variance to predict in the case of regression[#101]
  • modified mlj_flux.jl by adding the ret_distr parameter and fixed mljflux.predict both for classification and regression tasks.
  • Changed the behavior of the predict function so that it now gives the user the possibility to get distributions from the Distributions.jl package as output. [#99]
  • Calling a Laplace object on an array, (la::AbstractLaplace)(X::AbstractArray) now simply calls the underlying neural network on data. In other words, it returns the generic predictions, not LA predictions. This was implemented to facilitate better interplay with MLJFlux. [#39]
  • Moving straight to 1.0.0 now for package, because zero major versions cause compat headaches with other packages in Taija ecosystem. [#39]
  • Removed support for v1.7, now v1.9 as lower bound. This is because we are now overloading the MLJFlux.train and MLJFlux.train_epoch functions, which were added in version v0.5.0 of that package, which is lower-bounded at v1.9. [#39]
  • Updated codecov workflow in CI.yml. [#39]
  • fixed test functions [#39]
  • adapted the LaplaceClassification and the LaplaceRegression struct to use the new @mlj_model macro from MLJBase.[#39]
  • Changed the fit! method arguments. [#39]
  • Changed the predict functions for both LaplaceClassification and LaplaceRegression.[#39]

Removed

  • Removed the shape, build and clean! functions.[#39]
  • Removed Review dog for code format suggestions. [#39]

Added

  • Added new keyword parameter ret_distr::Bool=false to predict. [#99]

Version [0.2.3] - 2024-05-31

Changed

  • Removed the link_approx parameter in LaplaceRegression since it is not required.
  • Changed MMI.clean! to check the value of link_approx only in the case likelihood is set to :classification
  • Now the likelihood type in LaplaceClassification and LaplaceRegression is automatically set by the inner constructor. The user is not required to provide it as a parameter anymore.

Version [0.2.2] - 2024-05-30

Changed

  • Unified duplicated function MMI.clean!: previously MMI.clean! consisted of two separate functions for handling :classification and :regression types respectively. Now, a single MMI.clean! function handles both cases efficiently.[#39]
  • Split LaplaceApproximation struct in two different structs:LaplaceClassification and LaplaceRegression [#39]
  • Unified the MLJFlux.shape and the MLJFlux.build functions to handle both :classification and :regression tasks. In particular, shape now handles multi-output regression cases too [#39]
  • Changed model metadata for LaplaceClassification and LaplaceRegression

Added

Added Distributions to LaplaceRedux dependency ( needed for MMI.predict(model::LaplaceRegression, fitresult, Xnew) )

main

Version [0.2.1] - 2024-05-29

Changed

  • Improved the docstring for the predict and glm_predictive_distribution methods. [#88]

Added

  • Added probit helper function to compute probit approximation for classification. [#88]