Enable precomputed similarity matrices #87
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Clustering functions such as `DBSCAN` accept `metric='precomputed'`, which requires a square distance (similarity) matrix to be passed as, in this case, `inverse_X`. However, the mapper isn't aware when precomputed matrices are being passed in, so when it slices according to the filter function values and hypercubes, the square-ness is un-squared. This PR adds a kludgey way to tell the mapper that a precomputed matrix is being passed in. When set to `True`, the slice will feed a square matrix to `clusterer.fit()`
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Hello @deargle, Thank you for updating !
Comment last updated on May 21, 2018 at 21:02 Hours UTC |
Codecov Report
@@ Coverage Diff @@
## master #87 +/- ##
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- Coverage 82.15% 81.84% -0.32%
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Files 6 6
Lines 409 413 +4
Branches 95 96 +1
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+ Hits 336 338 +2
- Misses 51 52 +1
- Partials 22 23 +1
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Hey! Thanks for this PR. There were some variable name changes. Could you please resolve the conflicts and add a couple of unit tests showing that it works and is equivalent to the typical method? |
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Oh, you're welcome! I hadn't written the unit tests yet, but I'll still do
that as a separate PR soon.
…On Thu, Jul 12, 2018, 6:36 PM Nathaniel Saul ***@***.***> wrote:
Merged #87 <#87>.
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Great, Thanks! Please notice some of the variable names have changed! |
Clustering functions such as
DBSCANacceptmetric='precomputed', which requires a square distance (dissimilarity) matrix. However, the mapper isn't aware whenprecomputed matrices are being passed in, so when it slices according to the filter function values and hypercubes, the square-ness is un-squared.
This PR adds a kludgey way to tell the mapper that a precomputed matrix
is being passed in. When set to
True, the slice will feed a squarematrix to
clusterer.fit()