Document SCRIB's empty-prediction-set scoring - #1218
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Re-verified the existing favscrib branch's SCRIB fixes (ambiguity-loss
un-squaring, fill_max search/inference consistency) directly against
Lin, Glass, Westover, Xiao, and Sun, "SCRIB: Set-classifier with
Class-specific Risk Bounds for Blackbox Models" (AAAI 2022,
arXiv:2103.03945) -- both confirmed correct, no new bug found there.
Found one worth documenting rather than fixing: the paper's
Chance-Ambiguity (P{|H(X)|>1}) and per-class risk
(P{k not in H(X) | |H(X)|=1}) are defined only relative to singleton and
multi-label sets; the paper never specifies how an empty set (|H(X)|=0)
should be scored. This implementation treats empty sets the same as
multi-label sets ("not sure"), consistently in both the optimized loss
and the rejection_rate/error_ps metrics used to evaluate it -- and this
is the safer choice: under the paper's literal formula, an always-empty
classifier would show zero ambiguity loss and zero risk loss, appearing
loss-free while being useless.
Also added the exchangeability/no-distribution-shift caveat that applies
to LABEL and SCRIB (as opposed to CovariateLabel) to the CXR conformal
prediction example.
Deliberately scoped to avoid touching the same lines already fixed on
favscrib, so the two branches merge independently.
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looks good. |
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Adds better documentation for SCRIB