Comments (6)
I'm not sure it is worth adding. I'd put this as an issue and lean towards example notebooks using other packages with DiverseSelector or to compare with DiverseSelector, rather than rewriting/appropriating the methods.
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This makes sense. Thank you.
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Can you also try to add https://chemfp.readthedocs.io/en/chemfp-4x/chemfp_diversity.html?highlight=%20MaxMin#heapsweep-picker in our algorithm performance profiling when selecting molecules from the dataset? Thank you. @Shania99
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From the reading here, it seems that heapsweep is slower than maxmin and usually used only when you want to find all equivalent guesses. It's fine to profile against it, but since it's even slower than maxmin (and that behavior is already known), I'm not sure why to include it.
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We don't have a plan to include this implementation for now. But maybe we can mention it in the manuscript.
Thanks to your suggestions, we are not going to benchmark it. Thanks for the advice.
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Given the fact we don't have a plan to include this, I am going to close this issue for now.
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Related Issues (20)
- returning incorrect number of molecules HOT 2
- Finalizing the distance and diversity modules HOT 7
- [Diversity module] Adding tests for diversity measurements HOT 1
- [Similarity module] Add support of scaled similarity HOT 7
- [Converter module] Conversion between similarity and dissimilarity/distance HOT 7
- [Similarity module] Add more similarity measurements HOT 7
- [Distance module] Add Formulas to Docstrings and Add Tests HOT 3
- [Features module] Fix failed tests and improve code/coverage HOT 9
- [Distance Module] Bitstring functionality already covered by non-bitstring functions HOT 3
- [Diversity module] Add Formulas to Docstrings, Add Tests, & Polish Module HOT 2
- [Utils] Utils.py has unfinished to-do and lacks testing HOT 4
- [method.distance module] HOT 4
- [methods.partition DirectedSphereExclusion] HOT 1
- [methods.partition GridPartitioning] HOT 4
- [Docs] List the application scenarios of `Selector` HOT 1
- [Selector module] Implementing D-optimal designs HOT 3
- Implementing Gini coefficient in `metric.py` HOT 4
- Supporting scaled similarity HOT 10
- Stratified Sampling
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