Fix nan_euclidean_distances symmetry for self-distances #32874
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Fixes scikit-learn#32848 When computing distances with NaN values, nan_euclidean_distances produced slightly asymmetric matrices due to floating-point rounding errors after the sqrt operation. This caused issues with downstream tools expecting perfect symmetry. The fix ensures perfect symmetry by averaging the matrix with its transpose when X is Y, and carefully preserves NaN diagonal values for rows with all missing values. Added regression test to verify symmetry and zero diagonal.
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Fixes #32848
Summary
Ensures
nan_euclidean_distancesreturns perfectly symmetric matrices when computing self-distances with NaN values, eliminating floating-point rounding asymmetries.Problem
When computing distances with NaN values,
nan_euclidean_distancesproduced slightly asymmetric matrices (~4e-16 differences) due to floating-point rounding after the sqrt operation. This breaks downstream tools likescipy.spatial.distance.squareformthat expect exact symmetry.Solution
(distances + distances.T) / 2after sqrtTesting
assert_array_equalfor strict bitwise symmetry checkNote
Developed independently alongside PR #32851. This approach guarantees perfect symmetry rather than relying on
allclosetolerance.