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Match Gamma log-score gradients for small positive targets - #460
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rudycelekli wants to merge 1 commit into
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rudycelekli wants to merge 1 commit into
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The Gamma score uses its actual log density, but the log-shape gradient uses log(epsilon+beta*Y). For small positive targets this clips the derivative substantially: alpha=2, beta=1 and Y=1e-200 gives about 46.90 instead of 921.88.
Compute log(beta)+log(Y), preserving the positive-support derivative without clipping or a potentially underflowing product. Two parameterized cases cover alpha=1/2 and 2, small targets, varied rates and ordinary targets. They use closed-form digamma identities and independent central finite differences of the actual log score.
Validation on Python 3.11.15, scikit-learn 1.6.1, NumPy 2.4.6 and SciPy 1.17.1: this independent branch's regression tests and full configured pre-commit hooks pass. The unchanged upstream slow suite passes 169 tests, with 2 xfailed and 22 xpassed. A private composition of the six proposed fixes passes 201 tests with the same expected/unexpected-pass statuses; final organized regression tests pass all 32 cases and combined lint passes. The production files are identical across that integration run and the final branches; test organization was verified separately.
Implementation and tests are AI-assisted, with additional independent automated correctness/duplicate review. Numerical cases use synthetic fixtures and analytic/SciPy oracles. Hosted CI and maintainer acceptance are separate from these local results.