roam/implloss-functions.org

:PROPERTIES:
:ID: 001430d5-e1e7-4e72-baf6-17399bfd6447
:END:

#+title: impl/loss-functions
#+filetags: :project: :knowledge: :loss-functions:

* impl/loss-functions

** KernelizedROLLoss — Key Design Decisions

- =_icdf= uses **bisection** (not Newton-Raphson). NR was replaced because =F_prime= goes near-zero when =v= is large and =tau= drifts, causing divergence. Bisection bracket is =[min(scores)−10/v, max(scores)+10/v]=, guaranteed to converge.
- Score normalisation (=normalize= flag): divides =yh= by its std before KDE. Can be disabled via =kernelized_roll_fpr(fpr, normalize=False)=. The chain-rule correction (multiply grad by =score_scale=) is applied in backward.
- =_grad_tau_scores_neg= denominator clamped at =1e-10= to prevent 0/0 when all sigmoid kernels saturate.
- Variance clamped at =1e-8= in normalisation to prevent =inf= when all batch scores are identical.

** KernelizedROLLoss Unification (2026-07-14)

=KernelizedROLLoss= and =KernelizedROLLossAOC= merged into a single class accepting =alphas= (list of CDF targets) and =divisor=:
- Point-FPR: =KernelizedROLLoss.apply([1−fpr], 1, yh, y, gamma, normalize)=
- AOC: =KernelizedROLLoss.apply(_AOC_ALPHAS, _AOC_DIVISOR, yh, y, gamma, normalize)=

Backward always vectorised (K-dim); K=1 reduces to the scalar case. =ctx.divisor= stores the non-tensor divisor.

** BCE Combination

Both =kernelized_roll_fpr= and =kernelized_roll_tpr= accept =bce_weight= (default 0.0) and =bce_pos_weight= (default 1.0).
Loss = ROLL + bce_weight × BCE. Standard value: =bce_weight=0.5=, =bce_pos_weight=num_false/num_true=.

** ISJ Bandwidth Failure

ISJ fails when a class has too few samples in a split (~<20). Affects: cleveland, ecoli, glass4, glass5.

** Gamma Scheduling

=KernelScheduler= widens KDE kernels early then anneals. Default: =initial_gamma=100=, =decay=0.5=, =decay_every=500=. Gamma is a *divisor* of =v=.

** LibAUC Integration (2026-07-14, tuned 2026-07-14)

=libauc_auc_loss= in =src/roll.py= wraps =AUCMLoss= (Yuan et al., ICCV 2021) to the =(yh, y)= convention.
- Applies =torch.sigmoid(yh)= before passing to AUCMLoss (squared-hinge surrogate, no built-in sigmoid).
- AUCMLoss has learnable params =a, b, alpha= — must use PESG optimizer (not Adam).
- =opt_factory= signature: =opt_factory(model)= — does NOT take criteriorator (would be unbound at call site in =_perform_episode=).
- =opt_factory= pattern: =loss_fn.pesg_opt_factory(...)= returns picklable =_PESGFactory= sharing the same inner =AUCMLoss= instance as the criteriorator's loss func (preserved by pickle memo).
- =ExperimentConfiguration= gained =opt_factory: callable = None=; overrides =optim_class/optim_args= when set.
- =tqdm= is an undeclared dep of libauc — must be in =propagatedBuildInputs= in flake.nix.

*** Recommended settings (v2 + adam, tuned 2026-07-14)
- =version='v2'= — removes class prior from formulation; LibAUC explicitly recommends over v1; default in =libauc_auc_loss=.
- =mode='adam'= in PESG — Adam-style primal updates, converges much faster than SGD on small datasets; default in =pesg_opt_factory=.
- =imratio= passed explicitly as =num_true / (num_true + num_false)= — avoids noisy mini-batch estimates on imbalanced data. Note: v2 ignores imratio (prior-free), so this only matters if reverting to v1.
- =lr=0.1= (PESG default, much larger than Adam's 1e-3). If diverging: try =epoch_decay=0.0= or drop lr to =0.01=.

** Grace Period (BasicCriteriorator / CRBasedCriteriorator, 2026-07-14)

=grace_period: int = 0= parameter added to both criteriorators. During the first =grace_period= epochs:
- =best_flag= is always False (no model snapshot taken)
- Patience counter does not increment (early stopping cannot fire)

All keel configs use =grace_period=50=. Prevents noisy early-epoch models from being selected as best.

** Naming fix: =_kernelized_roll_fpr_internal='s =fpr= param renamed =cdf_target= (2026-09-10)

Mentor-audit finding (screenshots, no direct cite): =kernelized_roll_fpr(fpr, ...)= calls
=partial(_kernelized_roll_fpr_internal, fpr=1 - fpr, ...)= — the INTERNAL function's =fpr=
parameter actually holds =1-fpr= (the CDF target, thesis's =1-α=), not the FPR itself, the
opposite of what its name implies. Logic was always correct (verified independently, see
next entry); only the name was misleading. Fixed by renaming the internal parameter (and
the keyword in the =partial(...)= call) to =cdf_target= — user's preferred name over
=alpha= (avoids clashing with the thesis's own use of α to mean the RAW target rate, not
its complement). The PUBLIC =kernelized_roll_fpr(fpr, ...)= parameter is untouched — it's
correctly named; the complement happens only when forwarding internally. No callers use
=fpr== as a keyword arg anywhere in the codebase, so the rename is fully self-contained.
Mirrored the identical rename in the thesis's own code listing
(=content/appendixs/appendix_roll_code.tex=, which reproduces this function verbatim) so
the two don't drift apart.

** Verified correct (not a bug): =_kernelized_roll_tpr_internal='s =1-yh= and un-complemented =tpr= (2026-09-10)

While investigating the =fpr=/=cdf_target= naming issue above, also audited two
look-alike oddities in the neighboring =_kernelized_roll_tpr_internal= — both checked out
as correct, for non-obvious reasons worth remembering before anyone "fixes" them later:

1. *=1 - yh= instead of =-yh=* for the score transform (thesis's stated general rule says
   "negate scores", i.e. =-f_theta(x)=). Directly compared =1-yh= vs =-yh= as inputs:
   identical loss and gradients to ~3e-8 (float noise). Reason: =1-yh = -(yh-1)=, i.e.
   negation plus a constant shift, and =KernelizedROLLoss= is translation-invariant in its
   scores (same property underlying the thesis's gradient-balance proof) — so the shift is
   silently absorbed and does nothing. Added a one-line code comment at the call site
   (roll.py, right above the =KernelizedROLLoss.apply= call in
   =_kernelized_roll_tpr_internal=) explaining this, so it doesn't look like a stray bug to
   a future reader — this comment is the only thing that changed; the surrounding logic
   was intentionally left as-is.
2. *=kernelized_roll_tpr(tpr)= passes =tpr= straight through with no complement*, while
   =kernelized_roll_fpr(fpr)= complements (=1-fpr=). Looks asymmetric, but is required by
   the math: reducing FPR@TPR(β) to the TPR@FPR machinery requires complementing the
   target rate to =(1-β)= first (per the thesis's own reduction rule), and separately,
   evaluating TPR@FPR at any rate =γ= uses CDF-target =1-γ= internally — chain the two
   complements: =1-(1-β)=β=. They cancel exactly, so the raw =tpr= value reaching the
   internal CDF-target IS =β= correctly, no complement needed. The apparent asymmetry
   between the two wrappers is expected, not a bug.

Closed the loop with an independent numerical check (not just internal
self-consistency): hand-computed =1 - F0_hat(F1_hat^{-1}(1-beta))= (thesis's
eq:roll-fpr-at-tpr) completely from scratch in a throwaway script — same normalization,
same KDEpy bandwidth selector, same sigmoid kernel, same bisection, but applied directly
to the ORIGINAL untransformed (yh, y) with no negation/swap trick at all. Result matched
=_kernelized_roll_tpr_internal='s actual output to ~3e-5 (bisection tolerance noise).
Confirms the function exactly reproduces the paper's formula. No code fix needed for
either point beyond the one comment above.

** MAJOR: appendix_roll_code.tex was stale relative to impl/src/roll.py (2026-09-10)

User asked directly: "did we ever update the code [listing] after we changed [the real
code]... is the code in the thesis matching actual code?" Answer at the time: NO. Did a
full AST-based function-by-function diff (Python =ast= module, not just eyeballing) of
every function in appendix_roll_code.tex against impl/src/roll.py. Found:

Serious (pre-fix or actively-buggy code shown as current):
- =_icdf= / =_icdf_batch=: listing showed the OLD signatures entirely (single =v=/=scores=
  instead of =v0=/=scores_neg=/=v1=/=scores_pos=, old fixed =+-10/v= bracket margin instead
  of the exact =h0*log(alpha/(1-alpha))= bracket, old "stop on CDF residual" criterion
  instead of the certified-loss-interval one) -- none of the bisection-bracket /
  stopping-criterion audit fixes from earlier this session had been mirrored here.
  =forward()='s calls to these were correspondingly stale (3-arg, not 5-arg).
- =backward()=: showed the OLD, numerically-vulnerable =kernel = s_neg*(1-s_neg)= gradient
  formula -- the EXACT formula independently found to have a live bug (collapses to an
  all-zero gradient when scores saturate) and replaced with the log-space
  =_log_sigma_prime=/softmax version earlier this session. The listing never got that fix,
  so it directly contradicted appendix_kde_grad.tex (which documents the fix as done) --
  a self-contradiction within the thesis between two appendices.
- =_log_sigma_prime= itself was MISSING from the listing entirely (not just stale) --
  =backward()= as shown couldn't even have called it.

Minor (comments from this session's smaller fixes not mirrored):
- =_torch_mean_std=: listing still showed =torch.std(yh)= (Bessel-corrected), not the
  =correction=0= MLE fix from earlier the same day (see next entry).
- =_kernelized_roll_tpr_internal=: missing the =1-yh= translation-invariance comment.

Also softened the appendix's opening claim from "reproduced verbatim" to "reproduced ...
reformatted for print width ... logic is unchanged" -- it was never literally verbatim
(unicode sigma/tau stripped to ASCII, citation tags like =[eq:sigmoid-kernel]= dropped from
docstrings, lines rewrapped for column width), so "verbatim" was itself a small overclaim.

Fixed all of the above directly in appendix_roll_code.tex: ported the current =_icdf=/
=_icdf_batch=/=forward=/=backward= logic across (reformatted to the listing's existing
ASCII/wrapped style, not raw-pasted), added the missing =_log_sigma_prime= method right
after =_sigma_prime= (matching live file ordering), and added the two missing comments.

VERIFICATION METHOD (worth remembering for next time this comes up): manual reading isn't
reliable for this kind of check -- used Python's own =ast= module to extract every
function's source from both files and diff them programmatically. First pass (raw source
diff) caught line-wrapping noise mixed in with real differences; had to write a stricter
second pass that parses both into ASTs, strips docstrings/comments via =ast.unparse=, and
diffs the RESULT -- that gave a clean "0 functions differ in actual executable logic"
confirmation across all 21 functions in the listing. Recommend this exact
=ast.parse=+=ast.unparse=-diff technique any time "does the thesis's code listing match
the real implementation" needs checking again -- eyeballing a big code appendix against a
live file is exactly how this drift went unnoticed for multiple fix-rounds in the first
place.

Rebuilt thesis clean (nix build exit 0) after the fix, confirmed no new undefined
references introduced.

** Fixed: Gaussian ROLL =_torch_mean_std= used Bessel-corrected std, not MLE (2026-09-10)

Mentor-audit finding: Appendix A (appendix_gauss_grad.tex) states explicitly "The MLE
parameters are: ... σ_k² = (1/|B_k|)Σ(f_θ(x_j)-μ_k)²" — the biased, n-denominator MLE
variance. But =_torch_mean_std= (roll.py, Gaussian ROLL helpers) used =torch.std(yh)=,
whose default (=correction=1=) is Bessel-corrected (n-1 denominator) — NOT the MLE. Real
documentation-vs-implementation mismatch, same family as the AOC-divisor issue, but lower
stakes: verified =_torch_mean_std=/=_torch_normal_fit= are ONLY used by
=_roll_loss_from_fpr_internal= (Gaussian ROLL), which is NOT among the loss configs used
in any reported experiment (only =roll-aoc= and =roll-tpr90=, both KDE-based, are) — so
fixing this required no re-running of any experiment.

Separately checked whether the ANALOGOUS =torch.var(yh, correction=1)= in KDE score
normalization (=score_scale=, roll.py ~line 271) has the same problem: it does NOT.
Thesis's own definition of the normalization constant (method.tex, sec:roll-numerical-
stability) is deliberately generic — =\hat\sigma_\mathcal{B} = sqrt(Var(...))= with no
n vs n-1 commitment anywhere, unlike Appendix A's explicit "MLE" language. Nothing textual
is being contradicted there. Also, per the thesis's own prop:normalization-invariance,
for Silverman's rule (scale-equivariant) the exact value of σ̂_B doesn't affect the loss
or gradient AT ALL; for ISJ (already flagged elsewhere as "a heuristic"), the n vs n-1
choice is only a sqrt(n/(n-1)) deviation (~2.6% at batch 20, <1% by batch 64) -- verified
numerically. Left this one alone entirely, per user's explicit instruction to only fix
the Gaussian ROLL side.

Fix applied: =torch.std(yh)= -> =torch.std(yh, correction=0)= in =_torch_mean_std=, with a
one-line comment noting this matches Appendix A's MLE formula rather than torch.std's
Bessel-corrected default. Verified: output now matches a hand-computed
=sqrt(mean((x-mean(x))^2))= MLE formula exactly (=torch.allclose= true), and the full
=roll_loss_from_fpr= forward+backward still runs cleanly end-to-end after the change.