Fix fpmax for sparse matrices #1000
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While this function technically supports sparse matrices, it calls `pd.DataFrame.values` in order to get the number of rows in the DataFrame. Unfortunately, `.values` forces the entire DataFrame to be converted into a non-sparse 2D numpy array, which obliviates the whole purpose behind using a sparse matrix in the first place. It's a simple fix though.
tkellogg
changed the title
Wow, good catch! Thanks for the PR |
Codecov ReportBase: 77.45% // Head: 77.46% // Increases project coverage by
Additional details and impacted files@@ Coverage Diff @@
## master #1000 +/- ##
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+ Coverage 77.45% 77.46% +0.01%
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Files 198 198
Lines 11171 11171
Branches 1406 1406
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+ Hits 8652 8654 +2
+ Misses 2305 2304 -1
+ Partials 214 213 -1
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Description
While the current functionality supports sparse matrices on an API level, it calls
pd.DataFrame.valuesin order to get the number of rows in the DataFrame. Unfortunately,.valuesforces the entire DataFrame to be converted into a non-sparse 2D numpy array. So using sparse matrices to fix an OOM doesn't actually make the OOM go away.This fix is semantically identical, in Pandas terms, except that it doesn't materialize a dense array just to find it's length.
Related issues or pull requests
N/A
Pull Request Checklist
./docs/sources/CHANGELOG.mdfile (if applicable)./mlxtend/*/testsdirectories (if applicable)mlxtend/docs/sources/(if applicable)PYTHONPATH='.' pytest ./mlxtend -svand make sure that all unit tests pass (for small modifications, it might be sufficient to only run the specific test file, e.g.,PYTHONPATH='.' pytest ./mlxtend/classifier/tests/test_stacking_cv_classifier.py -sv)flake8 ./mlxtend