Feature/joss review comments #13
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added 30 commits
August 22, 2025 15:49
- implement explained_variance_ratio_ for both, NipalsPCA and NipalsPLS - implemented corresponding unit tests - re-organized imports to comply with pylint policy - currently still empty slice warning in NipalsPLS l. 799
- set up documentation with sphinx-quickstart - built documentation with tox -e docs - view docs with python -m http.serve from docs/ dir and visiting localhost:8000 in browser
- create docs with sphinx - adapted docstrings in src/open_nipals/ folder
- include .readthedocs.yaml config file - change README.md pypi installation paragraph - recompile the docs
Change to python version 3.13
Address rsenne's Small patch candidate 1.
- guard convergence division against zero norm in both modules, adressing rsenne's small patch candidates 2. - restructure calc_imd in nipalsPCA.py in line with rsenne's small patch candidate suggestion 1
Use pseudo-inverse in nipalsPCA.py projection bit in transform to address rsenne's small patch candidates 3.
- change print to warning statement in case iterations are terminated due to reaching maximum number of iterations in both modules, addressing rsenne's small patch candidate 4. - use fstrings in the prints for verbose option instead of .format(...)
- use np.concatenate instead of np.append for performance gains, addressing rsenne's small patch candidates 6 - relax the default relative tolerance criteria to 1e-6, addressing rsenne's comment on performance
Correct bug in NIpalsPLS.calc_imd when using scores
Missed another inv->pinv
unify exponential numbers format
- change base classes for NipalsPCA from _BasePCA (private, unstable) to BaseEstimator, TransformerMixin (public) - change base classes for NipalsPLS from _PLS (private, unstable) to BaseEstimator, TransformerMixin, RegressorMixin (public) - re-organize inputs to comply with pylint input organization hierarchy (python native, thrid party, first party)
- fit new components in NipalsPCA transform conditionalMean with set_components, adddressing rsenne's major issue 5 - updated and streamlined documentation where necessary - streamlined NipalsPLS calc_oomd, adapted to NipalsPCA counterpart - re-create documentation
- Ledoit Wolf does still not work due to dimensionality issue - updated docs
- two more options for calc_imd in both modules: full using the full covariance matrix, and ledoit_wolf for using the full covariance matrix using the Ledoit Wolf shrinkage for performance - update the docs
- split get_explained_variance and get_Explained_variance_ratio methods from properties - both methods now return numpy arrays - fixed the unit tests
This was referenced Jan 6, 2026
- nipalsPCA.py: remove use_Denom=True statements, as this is the default value anyway - nipalsPLS.py: follow the recipe of K. Dunn's book for normalization - update the documentation
nielsschlusser
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Correct normalization
nielsschlusser
temporarily deployed
to
testpypi
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Inactive
This was referenced Jan 8, 2026
- implement tighter limits on PLS unit tests - remove regression vector computation from set_Components unit test
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This feature branch contains the adjustments and augmentations of the code based on @rsenne 's suggestions in issues #10 and #12.