fix(hrp): symmetrise correlation distance before squareform (2.0.1)#13
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hrp() raised "ValueError: Distance matrix must be symmetric" when the correlation matrix was only symmetric to floating-point tolerance — the normal case for matrices coming from cov_to_corr or denoise_cov, where sub-epsilon asymmetry is unavoidable. Average the distance with its transpose and zero the diagonal before squareform; clustering is unchanged. Mirrors the same fix in RiskLabAI.jl v0.5.1. Adds a regression test (test_hrp_asymmetric_correlation) and bumps the patch version to 2.0.1. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Problem
optimization.hrp.hrpraisedValueError: Distance matrix 'X' must be symmetricwhenever the input correlation matrix was only symmetric to floating-point tolerance. That is the normal case for correlation matrices produced bycov_to_corrordenoise_cov, where sub-epsilon asymmetry (~1e-16) is unavoidable. The failure surfaced while building the Python portfolio-construction tutorial (parity withNotebooks.jl).Fix
Symmetrise the correlation distance (
(d + dᵀ)/2, zero diagonal) beforescipy.spatial.distance.squareform. Clustering output is unchanged for genuinely symmetric input. This mirrors the same fix already shipped in RiskLabAI.jl v0.5.1 ((distance .+ distance') ./ 2).Tests
test_hrp_asymmetric_correlation(fails onmain, passes here).test/optimization/suite green (12 passed).Patch version bump 2.0.0 → 2.0.1; CHANGELOG updated.
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