Releases: scikit-learn-contrib/scikit-matter
v0.3.2
What's Changed
Full Changelog: v0.3.1...v0.3.2
Contributors
Assets 4
v0.3.1
This is a small release containing a new KernelPCovC class by @rvasav26 in #254
Full Changelog: v0.3...v0.3.1
Contributors
Assets 4
v0.3
We are happy for our new release of scikit-matter after almost two years 🥳!
What's Changed
- Add
_BasePCovclass (#248) - Add
PCovCclass that inherits shared functionality from_BasePCov(#248) - Add
PCovCtesting suite and examples (#248) - Modify
PCovRto inherit shared functionality from_BasePCov_(#248) - Update to sklearn >= 1.7.0 and scipy >= 1.15.0 (#239, #257)
- Fixed moved function import from scipy and bump scipy dependency to 1.15.0 (#236)
- Fix rendering issues for
SparseKDEandQuickShift(#236) - Updating
FPSto allow a numpy array of ints as an initialize parameter (#145) - Supported Python versions are now ranging from 3.9 - 3.13.
- Updating
skmatter.datasetssubmodule to support sklearn 1.5.0 (#229) - Add
SparseKDEclass (#222) - Add
QuickShiftclass (#222) - Add an example on how to conduct PAMM algorithm with
SparseKDEandQuickShift
(#222) - Add H2O-BLYP-Piglet dataset (#222)
- Add two distance metrics that support the periodic boundry condition,
periodic_pairwise_euclidean_distancesandpairwise_mahalanobis_distances(#222)
New Contributors
- @GardevoirX made their first contribution in #232
- @AtharvaRai07 made their first contribution in #249
Full Changelog: v0.2.0...v0.3
Contributors
Assets 4
Version 0.2.0
- Add this
CHANGELOGfile (#198) - Update example of WHO feature selection (#212)
- Rename
RidgeRegression2FoldCV->Ridge2FoldCV(#211) - Adding metrics for prediction rigidity (#209)
- Overhaul of documentation page (#200 to #204)
- Rename and add member variables (#197)
- Fix/check estimator (#196)
- fixed small typo in
PCovRclass documentation (#194) - Resolved Issue WHO dataset missing function call section in doc (#181, #192)
- Speeding up tests (#190)
- Removing kernel optimization from who example (#189)
- Ignore rendered examples for linting (#188)
- Add more infos on documentation landing pages &
CODE_OF_CONDUCT(#186) - Add contributors pictures to
README, show pip install instructions in docs (#185) - Add linting and tests for docstring and documentation code (#184)
- Rerestructure requirements after (#171, #183)
- Update
README.mdto show banners (#176) - Modernize package infrastructure (#172)
- Add an example of GCH for molecular materials (#171)
- Port examples to
sphinx_gallery(#170)
Version 0.1.4
This is a patch release of skmatter that contains fixes for the documentation and directional convex hull
Version 0.1.3
This is a patch release the last version of skcosmo before the refactor to skmatter
- Deprecation warning was added to link to renamed package (#154)
- dropped python <3.8 support, because we are now using scikit-learn version >=1.1.0 (#139 #146 #152 )
- WHO dataset and examples were added (#149)
- nice dataset was added (#143)
- overhaul of documentation (#142 #150)
- added DirectionalConvexHull class (#140)
- added test_precomputed_regression function to KPCovR (#136)
- other bugfixes (#141 #148)
Version skmatter-0.1.3
Version 0.1.2
This is a patch release of skcosmo. It contains only bug fixes and small improvements, all users are encouraged to update.
Version 0.1.1
This is a patch release of skcosmo. It contains only bug fixes and small improvements, all users are encouraged to update.
Version 0.1.0
This is the first public release of scikit-cosmo. Scikit-cosmo is a collection of scikit-learn compatible utilities that implement methods developed in the COSMO laboratory. This first release contains multiple tools of general interest:
- Principal Covariate Regression (PCovR) and the kernel extension KPCovR
- Feature and sample selection methods: Farthest point sampling and CUR selection, as well as PCovR version of these methods
- tools to compare different features spaces: Global Feature Reconstruction Error (GFRE), Global Feature Reconstruction Distortion (GFRD), Local Feature Reconstruction Error (LFRE)
Have a look at the documentation if you are interested!