Comments (7)
As explained on Wikipedia there is a Tanimoto similarity and a Tanimoto distance. So both exist.
The easiest test is to compare an object to itself. Its similarity is greater than zero (often one) and the distance is zero.
I feel like it is better to add AIMSim as a dependence. Implementing 30+ methods is a lot of work.
We may wish to have a few basic methods implemented; the most common distance metrics and similarity measures are already there in scikit-learn
(distances) sklearn.metrics.DistanceMetric
(similarities and divergences) sklearn.metrics.pairwise
I'd lead with interfacing to scikit-learn (I think we already did this in large part?) and then considering interfacing to AIMSim a follow-up task.
I guess it is important to distinguish between similarities/affinities and distances/divergences. I'd suggest making sure that we have these distinguished, plus the "converter" between them.
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Yes, we should make them differentiable and be obvious as much as we can to avoid any ambiguity.
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Update: We decided not to include any wrappers to support the functionality in other packages (reason: additional overhead and unnecessary dependency), instead, we showcase how our package works with other libraries in notebooks/tutorials.
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Related Issues (20)
- returning incorrect number of molecules HOT 2
- Finalizing the distance and diversity modules HOT 7
- Add support of heapsweep HOT 6
- [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
- [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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