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Displace RTs by the true admissibility boundary t - st, not by t - #359
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ChangesKDE boundary displacement
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In `@ssms/support_utils/kde_class.py`:
- Around line 83-88: Update the displace_t_val initialization around
_recover_admissibility_boundary so the float(t_vals[0]) fallback is used only
when both st and t_dist metadata are absent. Require exactly one unique numeric
boundary, and propagate ValueError for multiple st values, vector-valued
t_dist.keywords["loc"], or malformed metadata instead of selecting the first
value. Add regression tests covering multiple st values and vector-valued loc.
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In `@ssms/support_utils/kde_class.py`:
- Line 570: Update _unique_variability_value to reject non-finite values after
converting and deduplicating the inputs with np.unique: validate unique_vals
using np.isfinite and raise the same documented malformed-metadata ValueError
when any value is NaN or infinite, before the variability-count check.
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Inline comments:
In `@ssms/support_utils/kde_class.py`:
- Around line 616-620: Update the boundary-recovery validation around t_dist in
LogKDE to retrieve its keywords once and require that value to be a
collections.abc.Mapping, raising the existing ValueError for missing or
malformed metadata. Use the validated keywords mapping for the subsequent loc
lookup instead of accessing t_dist.keywords directly.
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Inline comments:
In `@tests/test_kde_displace_boundary.py`:
- Line 252: Update the kde_sample call in the KDE sampling test to pass the
fixed supported seed 42 via random_state, while preserving the existing sample
count and assertion behavior.
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LogKDE's displace_t path shifts response times by metadata['t'] before fitting.
For a model with trial-to-trial variability in non-decision time that is the
wrong quantity: t is drawn per trial from Uniform(t - st, t + st), so no rt can
fall below t - st, while real density fills the band [t - st, t]. Displacing by t
puts the hard cutoff INSIDE the support, and the KDE floors that whole band.
The error is not smearing below the edge, which was the original hypothesis and
turned out to be wrong - below the edge the KDE sits exactly at its floor by
construction. It is the opposite sign, on the other side: just ABOVE the edge the
KDE is floored where the truth is finite. At t - st + 0.02 the exact likelihood
gives -22.89 and the KDE gives -66.77.
Measured against the exact ddm_st likelihood, 4 of 4 parameter sets: mean
absolute error above the edge falls from 24.98 to 14.17 log units. Label-level
A/B over 8 parameter sets: the fraction of labels sitting at the KDE floor falls
from 5.897% to 3.912%; 49.59% of labels change at all but the median change is
only 0.034 with a maximum of 67.74 - the fix rewrites a small fraction of labels
dramatically (the un-floored band) and nudges the rest negligibly. A network
trained on the corrected labels, scored against the exact likelihood over 12
parameter sets: median MAE 0.0362 -> 0.0259, mean 0.3548 -> 0.0356, worst
2.9515 -> 0.0984, better on 10 of 12. It is a tail fix, which is why it never
showed up in aggregate training loss.
Two metadata shapes carry st and both are handled:
* ddm_st / ddm_sz_st fold st into metadata['t_dist'], a functools.partial of
uniform.rvs with loc=-st, so the boundary is t + loc.
* full_ddm / full_ddm2 keep 'st' as a plain numeric metadata entry and emit no
t_dist at all.
Reading only t_dist is why full_ddm previously appeared unfixable.
Models with neither fall back to t, where the boundary genuinely is t, so
behaviour for fixed-t models is unchanged.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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This was referenced Oct 5, 2026
AlexanderFengler
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This is the config part of #361 (890c8e9), split off so that the model can be released without #359, which that branch is stacked on and which is not ready. ddm_normal_st draws ndt ~ Normal(t, st), so st is a standard deviation, against the uniform half-width that ddm_st means by the same parameter. Parameters, order, bounds and simulator machinery are ddm_st's; only the kernel differs. The model joins KDE_NO_DISPLACE_T like the other ndt-variability models. The kernel is unbounded, so it emits rt <= 0 by design (about 9% of samples at the box corner t=0.25, st=0.25, a=0.3). KDE label generation therefore needs the non-positive-RT filter of #349, which this commit sits on. Left out of 890c8e9: - The Normal branch of _recover_admissibility_boundary in kde_class.py and its three tests. The helper only exists after #359, so they belong with that PR. - The ddm_uniform_st alias of ddm_st (config function, registry entry, KDE_NO_DISPLACE_T entry and test). The name of the uniform model is being settled separately, and an alias can be added later without breaking anything. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
AlexanderFengler
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This is the config part of #361 (890c8e9), split off so that the model can be released without #359, which that branch is stacked on and which is not ready. ddm_normal_st draws ndt ~ Normal(t, st), so st is a standard deviation, against the uniform half-width that ddm_st means by the same parameter. Parameters, order, bounds and simulator machinery are ddm_st's; only the kernel differs. The model joins KDE_NO_DISPLACE_T like the other ndt-variability models. The kernel is unbounded, so it emits rt <= 0 by design (about 9% of samples at the box corner t=0.25, st=0.25, a=0.3). KDE label generation therefore needs the non-positive-RT filter of #349, which this commit sits on. Left out of 890c8e9: - The Normal branch of _recover_admissibility_boundary in kde_class.py and its three tests. The helper only exists after #359, so they belong with that PR. - The ddm_uniform_st alias of ddm_st (config function, registry entry, KDE_NO_DISPLACE_T entry and test). The name of the uniform model is being settled separately, and an alias can be added later without breaking anything. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
This was referenced Oct 7, 2026
AlexanderFengler
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LogKDE(displace_t=True) shifts every RT by t and floors the labels of anything that lands at or below zero. For the models in KDE_NO_DISPLACE_T and those declaring ndt_edge_shift that puts the cutoff inside the response-time support: a uniform st kernel starts at t - st, and the Normal, truncated-Normal and Rayleigh kernels have no edge at t at all. TrainingDataGenerator already overrides displace_t to False for them, so the pipeline was never affected; a direct LogKDE caller got flat labels in the band below t without a word. LogKDE now resolves the model from metadata["model"] (a _deadline suffix stripped, as lan_mlp does) and raises a ValueError naming the model when its config declares ndt_edge_shift or its name is in KDE_NO_DISPLACE_T. An unregistered name is refused when the metadata carries a non-zero st. Every other model keeps the shift by t unchanged. This replaces #359 by EItanm1999, which recovered the boundary from the simulator metadata instead: t - st for the uniform kernels, which was right, and t + t_dist.keywords["loc"] otherwise, which for ddm_truncnormt is the kernel's centre rather than its edge at 0 and collapsed the labels. Since the generator never displaces these models, the honest behaviour for direct callers is an error pointing at displace_t=False. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Member
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Thanks for finding this, and for measuring it. You were right on both counts: What shipped is a guard rather than the boundary recovery. #375 (you are credited in the commit and the PR body) makes Why a guard, for the record:
Since the generator never displaces these models, an explicit error for direct callers is the honest behaviour, and it keys on the config's declaration rather than on introspecting Open question
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AlexanderFengler
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Oct 8, 2026
This is the config part of #361 (890c8e9), split off so that the model can be released without #359, which that branch is stacked on and which is not ready. ddm_normal_st draws ndt ~ Normal(t, st), so st is a standard deviation, against the uniform half-width that ddm_st means by the same parameter. Parameters, order, bounds and simulator machinery are ddm_st's; only the kernel differs. The model joins KDE_NO_DISPLACE_T like the other ndt-variability models. The kernel is unbounded, so it emits rt <= 0 by design (about 9% of samples at the box corner t=0.25, st=0.25, a=0.3). KDE label generation therefore needs the non-positive-RT filter of #349, which this commit sits on. Left out of 890c8e9: - The Normal branch of _recover_admissibility_boundary in kde_class.py and its three tests. The helper only exists after #359, so they belong with that PR. - The ddm_uniform_st alias of ddm_st (config function, registry entry, KDE_NO_DISPLACE_T entry and test). The name of the uniform model is being settled separately, and an alias can be added later without breaking anything. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
AlexanderFengler
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LogKDE(displace_t=True) shifts every RT by t and floors the labels of anything that lands at or below zero. For the models in KDE_NO_DISPLACE_T and those declaring ndt_edge_shift that puts the cutoff inside the response-time support: a uniform st kernel starts at t - st, and the Normal, truncated-Normal and Rayleigh kernels have no edge at t at all. TrainingDataGenerator already overrides displace_t to False for them, so the pipeline was never affected; a direct LogKDE caller got flat labels in the band below t without a word. LogKDE now resolves the model from metadata["model"] (a _deadline suffix stripped, as lan_mlp does) and raises a ValueError naming the model when its config declares ndt_edge_shift or its name is in KDE_NO_DISPLACE_T. An unregistered name is refused when the metadata carries a non-zero st. Every other model keeps the shift by t unchanged. This replaces #359 by EItanm1999, which recovered the boundary from the simulator metadata instead: t - st for the uniform kernels, which was right, and t + t_dist.keywords["loc"] otherwise, which for ddm_truncnormt is the kernel's centre rather than its edge at 0 and collapsed the labels. Since the generator never displaces these models, the honest behaviour for direct callers is an error pointing at displace_t=False. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
AlexanderFengler
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* Add forward race simulation utilities * Address race simulation review feedback * Fix race simulator boundary validation * test: define generic observation result contract * feat: validate native observation results * docs: specify structured observation results * fix: validate categorical labels against observation dtype * docs: require representable categorical labels * test: define every legacy singleton-axis projection * feat: normalize legacy results without inference * docs: migrate consumers without changing defaults * test: expose legacy omission authority * fix: make legacy omission authority explicit * docs: require explicit legacy omission authority * Simplify legacy observation projection * test: focus legacy projection evidence * test: preserve projection boundary parity * docs: tighten legacy projection guidance * test: preserve normalized result invariants * build(docs): consolidate simulator documentation dependencies * build(docs): add the standard simulator docs entry point * ci(docs): adopt shared artifact deployment * docs: update simulator documentation ownership * ci(docs): vendor the reusable workflow locally * fix(docs): repair rendered tutorial links * docs: align the site legal notice * docs: consolidate the HSSM RLSSM handoff * docs: align guides with their documentation genres * docs: complete the public API reference * docs: preserve API reference compatibility * docs: align RLSSM handoff initialization * chore: ignore generated .cursor/ and .agents/ directories HSSMSpine's sync_agent_config.py materializes these into every ecosystem repo on each launch, but only the spine gitignored them. That left every repo one 'git add -A' away from committing generated editor config -- which just happened on lnccbrown/HSSM#1248, where the swept-in files also leaked local worktree paths. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> * feat: add angle_extended, an angle variant with drift on (-6, 6) A collaborator needs `angle` fitted to data whose drift runs past the current +/-3 box. The simulator already handles it -- `cssm.ddm_flexbound` with the `angle` boundary does not care how large v is -- so this is a config, not new machinery. It is a separate model rather than wider bounds on `angle` because the published angle.onnx was trained on (-3, 3). Widening the declared bounds without retraining would let a sampler explore where the network never learned, and that fails silently: no error, just a wrong likelihood. A separate model earns a separate network and leaves `angle` and its artifact untouched. Naming follows `shrink_spot_extended`, the registry's only other bounds-only variant. Not `angle_drift_ext`: in this codebase `drift` names a drift FUNCTION -- base.py carries a drift_config registry, and `gamma_drift` is called that because its drift_name IS gamma_drift -- so a name of that shape would read as "angle with a custom drift function", which is exactly what this is not. The tests assert the RELATIONSHIP rather than just the shape, because two configs silently diverging is the failure mode a bounds-only variant actually has: only `name` and the bounds may differ, only v's bounds may have moved, and only outward. They also pin `name` == the registry key, which `get_shrink_spot_extended_config` gets wrong -- it names itself "shrink_spot" -- so the precedent's bug is not inherited. And they check the new range does something rather than merely not crashing: past +/-3 the choice follows the sign of v and RTs stay short, which is the region worth having a network for. Measured while validating, and relevant to whoever trains that network: 55.3% of uniform draws from this box produce one-sided data, against 32.3% for `angle`, and RT dispersion falls from sd 0.250 at v=1 to 0.059 at v=6. Validation per the add-ssm-model skill: config loads, counts consistent, bounds ordered, defaults inside, simulator runs on defaults and on random draws from the full box. 1189 passed, 123 skipped. * docs: say which registry APIs are for reading and which path is for authoring CodeRabbit reviewed the angle_extended config and asked for the boundary to be resolved through `get_boundary_registry()` and the model registered through `get_model_registry()`, citing this file's Config Registry System section. That would have been wrong -- zero of the 24 modules under _modelconfig/ use those APIs, 22 of 24 name `bf.<fn>` directly, and model_registry.py:357-367 builds the registry FROM the static configs dict at import, with a comment saying so. Registering through the registry would bypass the source it derives from, and `ssms.config.model_config` reads from `get_model_config()`, so the model would be missing there. But the section invited the reading. It lists five ways to reach a config under a heading about registration, without saying they are for consumers -- so a reviewer generalising from it lands exactly where CodeRabbit landed, and would land there again on the next model. One paragraph now separates reading a config from authoring one, and points at the add-ssm-model skill. No code change; angle_extended is already reachable through get_model_registry() and present in ssms.config.model_config, which is the outcome the review was asking for. * fix: mask omissions on rts, not on choices The simulator marks a trial that ran past its deadline by writing -999.0 into `rts` and leaving the latent choice alone. Two of the auxiliary labels masked on `choices` instead, where the sentinel never appears, so the mask matched nothing: omission_p = P(choices == SENTINEL) -> 0.0, always choice_p_no_omission masked on choices -> excluded nothing Measured before the fix, on the shipped deadline models at deadline 0.8: model true omission rate omission_p as recorded ddm_deadline 0.3511 0.0000 angle_deadline 0.3511 0.0000 gamma_drift_deadline 0.5429 0.0000 gamma_drift_angle_deadline 0.5429 0.0000 The consequence is worth stating plainly, because it is not a cosmetic mislabel: an OPN is trained on `omission_p`, so a deadline data-generation campaign -- around 6,500 core-hours per model -- would have produced a network trained against a constant zero. Nothing downstream would have failed loudly; the network would simply have been useless. `nogo_p` was already correct because it tested `rts` as well as `choices`, and that inconsistency between neighbouring lines is what made the defect visible. The three now share one `omitted` mask so they cannot drift apart again. Tests cover the premise (the simulator marks rts and not choices -- a regression here means the mask must be revisited), that omission_p tracks the real rate on three deadline models, that choice_p_no_omission now excludes what it claims to and differs from the all-trials version, and that nogo_p equals P(wrong choice, responded) + P(omitted) -- the identity the derived-network work depends on. 1197 passed, 123 skipped. * Prepare ssm-simulators 0.14.0 release * Refresh uv.lock for the 0.14.0 version bump The lockfile records the project's own version, and docs CI syncs with --locked; first release since that workflow exists, so #305 never hit this. One-line diff: ssm-simulators 0.13.2 -> 0.14.0, no dependency changes. * build(deps): bump pypa/cibuildwheel from 4.1.0 to 4.2.0 Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 4.1.0 to 4.2.0. - [Release notes](https://github.com/pypa/cibuildwheel/releases) - [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md) - [Commits](pypa/cibuildwheel@v4.1.0...v4.2.0) --- updated-dependencies: - dependency-name: pypa/cibuildwheel dependency-version: 4.2.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> * Filter non-positive RTs before KDE bandwidth estimation A genuinely negative or zero RT - real and expected for models whose non-decision-time kernel has unbounded support (e.g. a Normal(t, st) kernel), and distinct from the omission sentinel - produces log(non-positive) = NaN in the log-RT array. A single NaN poisons np.std() over the entire choice group inside bandwidth_silverman, which then fails the 'bandwidth > 0' check and silently discards the whole group as 'no_base_data'. Measured directly: 1 negative RT out of 198,865 samples collapsed its entire choice group, flattening that group's density to a theta-independent floor in the training labels. Extend the validity mask to require rt > 0 and finite log(rt), applied to the final (possibly displace_t-shifted) RTs. - The mask stays after the log rather than at it: with displace_t, measured 10 of 200 strictly-positive input RTs become non-positive only after the shift, so an earlier-only mask misses them. The RuntimeWarning from the earlier log is left unsuppressed - it reports real data. - All three mask terms are load-bearing, measured over 2M draws at the displace_t boundary: 3 cases where rt - t > 0 but the log path underflows to -inf (caught only by isfinite) and 1 in the reverse direction (caught only by rt > 0); rt = +inf is caught only by isfinite. != filter_rts is kept because filter_rts is a documented parameter and rt > 0 subsumes it only for negative sentinels. - No input existed for which the old code produced a finite bandwidth that this changes: contamination always forced no_base_data. Verified byte-identical (float hex) bandwidths and log_rts for clean data across ddm, ddm_st, full_ddm and ddm_sdv, at n=200 and n=5000. - Filtering can now leave a choice group with a single sample, where bandwidth_silverman returns its std_n_1 fallback of ~10.59 instead of collapsing. That is an improvement on a flat fallback but not a meaningful bandwidth, so it is documented on the class and pinned by a test. Raising the len(log_rts) == 0 floor to < 2 would also change pre-existing n=1 behavior and is left as a separate change. - Retention contract, the pre-filter basis of choice_proportions, and the n=1 limitation are now in the LogKDE class docstring, which is what the API docs render; the stale caller-precondition sentence in the kde_class tutorial is corrected. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * test: cover the post-shift mask and the isfinite term of the KDE RT filter The filter's two design choices were untested: all three tests used displace_t=False with a single negative RT, so applying the mask before the displace_t shift, or dropping the isfinite term, left them green. Parametrize the choice-group test over displace_t (t = 0.4 puts 11 of the 199 positive RTs at or below t, so they turn non-positive only after the shift), over rt in {-0.5, 0, +inf} and over rts-only and log_rts-only input, and pin the number of retained samples. Masking before the shift now fails the 6 displace_t cases and dropping isfinite fails the 4 rt = +inf cases; all 12 fail on main's kde_class.py. The test is renamed because +inf is not a non-positive RT. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * docs: say that LogKDE's pre-filter choice_proportions are intentional The class docstring states that choice_proportions still count the samples the RT filter drops, but not whether that is by design. It is: omitted trials keep their choice label and have always been counted the same way, in the proportion but not in the density. Saying so keeps the next reader from "fixing" the basis in passing; it changes the mass of the KDE training labels and should only be changed on purpose. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * Pin the trial-to-trial variability draws to random_state - set_seed seeds C's srand and a module-level NumPy Generator, but the *_dist simulator param mappings (e.g. ddm_st's t_dist = partial(sps.uniform.rvs, ...)) are scipy calls with no explicit random_state, so scipy drew them from NumPy's legacy global RNG, which nothing seeded. Two simulator() calls with the same random_state returned different data for every model carrying sv, sz or st, while plain ddm - which has no *_dist - stayed reproducible, hiding the gap. Any "same data across arms" comparison relying on random_state alone was not actually holding the data fixed. - Fix: bind those partials to a Generator derived from random_state, after adapt_parameters, so the draws are pinned without touching process-global state. Verified that np.random's stream position and its next draws are unchanged across seeded simulator() calls, and that reproducibility holds while the global stream is being consumed between calls. - Only integer seeds participate; any other random_state object passes through and fails where it already failed (the Cython layer requires an integer). The modulo keeps the validated negative-seed range inside default_rng's domain without changing that pre-existing failure point. - Regression test folded into the existing random_state block in tests/test_simulator.py, over ddm, ddm_st, full_ddm_rv and ddm_sdv. It fails for the three variability models without the fix and passes with it, and it also asserts that a call leaves NumPy's global RNG state byte-identical, which a global reseed or a global draw would not - so it pins where the draws come from, not only that they repeat. full_ddm is deliberately not used: it has no *_dist mappings and draws sz/sv/st internally from the generator set_seed already seeds, so it cannot exercise this path. - Alternatives considered: reseeding np.random globally inside simulator() (rejected - mutates process-global state for callers who did not ask for it), and adding the legacy seed inside set_seed itself (one line, and broader since sequential_models.pyx has the same bare np.random.* pattern, but it needs a Cython rebuild and widens the contract for every direct cssm caller). The sequential_models.pyx gap is left as a separate issue. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> - Binding is restricted to callables that accept random_state: a model config may map a parameter to any callable, and one that does not take the keyword would otherwise raise on the simulator call. scipy's rvs qualifies through **kwds. - The regression test snapshots and restores NumPy's global RNG state, so its deliberate consumption of that stream cannot make later tests order-dependent. - A positional-only random_state does not count as accepting the keyword: the value would be diverted into **kwargs while the parameter kept its default, or raise. A test pins both the exclusion and that divert-and-keep-default behaviour. - A distribution that already binds its own random_state is left alone rather than rebound, so a caller's deliberate per-distribution seeding survives; the docstring says so, since such draws follow that generator, not the seed. * fix: draw trial-to-trial variability from a child stream of the seed The variability generator was default_rng(seed), and the Cython layer seeds its own generator with default_rng(seed) as well, so both replayed one PCG64 stream: the sv/sz/st draws reused the words behind the diffusion noise. Spawn a child of the seed instead, which is independent of the C-level generator. default_rng([seed, 1]) is avoided because it aliases default_rng(seed + 2**32). The comment no longer calls the modulo load-bearing: it only keeps negative seeds inside SeedSequence's domain. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix: pin variability draws in Simulator.simulate as well Simulator.simulate duplicates simulator()'s preprocessing but had no binding step, so same-seed calls through the class API still drew sv/sz/st from NumPy's global RNG. That is the recommended entry point, and the one the training-data pipeline seeds. Move the binding into one helper shared by both entry points. Each calls it once theta is final: in the class that is after the custom adaptations, which may still replace a *_dist entry. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test: route the pre-bound random_state test through simulator() test_bound_random_state_is_not_overridden only inspected a functools.partial and never called simulator(), so it kept passing with the "already bound" check removed. Feed a pre-bound t_dist through simulator() and assert that the caller's generator was the one consumed. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test: cover the unreadable-signature branch of _accepts_random_state codecov/patch flagged the `except (TypeError, ValueError)` branch as never exercised. A callable whose __signature__ is not a Signature makes inspect.signature raise, on every supported Python, and must be left unbound. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test: pin the binding to run after the custom adaptations Simulator.simulate has to bind once theta is final. A parameter adaptation may still put a *_dist entry into theta, and with the binding ahead of the custom adaptations that entry would draw from NumPy's global RNG again, while the per-model regression test keeps passing. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * feat: add ddm_normal_st, a DDM with a Normal non-decision-time kernel This is the config part of #361 (890c8e9), split off so that the model can be released without #359, which that branch is stacked on and which is not ready. ddm_normal_st draws ndt ~ Normal(t, st), so st is a standard deviation, against the uniform half-width that ddm_st means by the same parameter. Parameters, order, bounds and simulator machinery are ddm_st's; only the kernel differs. The model joins KDE_NO_DISPLACE_T like the other ndt-variability models. The kernel is unbounded, so it emits rt <= 0 by design (about 9% of samples at the box corner t=0.25, st=0.25, a=0.3). KDE label generation therefore needs the non-positive-RT filter of #349, which this commit sits on. Left out of 890c8e9: - The Normal branch of _recover_admissibility_boundary in kde_class.py and its three tests. The helper only exists after #359, so they belong with that PR. - The ddm_uniform_st alias of ddm_st (config function, registry entry, KDE_NO_DISPLACE_T entry and test). The name of the uniform model is being settled separately, and an alias can be added later without breaking anything. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test: LogKDE keeps both choice groups of ddm_normal_st at the box corner At t=0.25, st=0.25, a=0.3 about 9% of ddm_normal_st's RTs are non-positive. Without the non-positive-RT filter of #349, one such RT turns its whole choice group into "no_base_data" and its labels into a constant, on the displace_t=False path the generator takes for this model. The test fails when kde_class.py is restored to main's version, so it pins the model's dependency on that filter. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * feat(config): declare the response-time support edge for the uniform-st models HSSM (lnccbrown/HSSM#1292, #1358) reads where a model's response-time support starts from the per-model config, and ssms owns that fact: its own KDE label estimator needs it, and HSSM mirrors ssms configs for LAN models. ddm_st, full_ddm (and its alias full_ddm2) and full_ddm_rv draw the non-decision time as t + U(-st, st), so their RTs start at t - st. Every other model's support starts at t and declares nothing. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * feat(config): validate and carry ndt_edge_shift through the builder A malformed declaration would silently mislead HSSM's likelihood floor and the KDE estimator, so check its shape once (a dict with exactly param and scale, both param and t in params, a finite non-negative scale) in one helper shared by ModelConfigBuilder.validate_config and the import-time registry check, and let from_scratch keep the key like the other optional fields. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(config): cover the ndt_edge_shift declaration and its plumbing Check the declarations against the simulators (min(rt) >= t - scale * param on several parameter sets, and RTs below t at the corner so the declaration is not vacuous), that undeclared models start at t, that declaring models stay in KDE_NO_DISPLACE_T, and that the builder and validators carry and police the key. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * docs: document the ndt_edge_shift model-config key Add the key to the places that list the model-config fields for users: the configs tutorial, the contributing guide's Level 1 example notes, and the add-ssm-model skill. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(config): pin the declared edge scale and check it is tight min(rt) >= t - scale * st holds for any scale that is too large, so a declaration twice the true shift passed every test. Pin the shipped value for each declaring model, and check at the a-lower corner that the fastest RTs fall below t - scale * st / 2, which they only do (by about 0.1 s) when the scale is right. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(config): cover the ndt_edge_shift registry hook get_invalid_ndt_edge_shift_configs was tested on its own, but the call from _validate_configs() could be dropped without a test noticing. Swap in a one-config registry whose only fault is the declaration and check the import-time validator raises on it. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(config): prove non-vacuity at the a-lower corner only At the plain corner (t at its lower bound, st at its upper bound, the rest mid-box) only a handful of the 3000 simulated RTs fall below t, and the count depends on the RNG stream, so the assertion could flake. The a-lower corner puts hundreds below t and is the one that matters. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * docs: add the ndt_edge_shift checkbox to the model-validation checklist The skill's field table documents the key; its checklist did not remind an author to declare it when the non-decision time varies per trial. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * docs: say when not to declare ndt_edge_shift "Absent means RTs start at t" was wrong for kernels with no lower edge (ddm_normal_st, now on main) and for models without a t parameter; absence only means that no finite edge below t is declared. The docs now say so, list the cases where the key must be left out, state the scale convention (1.0 for a half-width), and tell a model author to add declaring and unbounded models to KDE_NO_DISPLACE_T. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * docs: say when not to declare ndt_edge_shift, all remaining sites The previous commit reached only the validator docstring. This one covers the builder docstrings, the add-ssm-model skill and its checklist, the contributing guide, the config tutorial and the test-file docstrings: absence means no finite edge below t is declared; leave the key out for models without a t parameter, for kernels with no lower edge (ddm_normal_st) and for edges not of the form t - scale * param; and add declaring and unbounded models to KDE_NO_DISPLACE_T. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * Prepare ssm-simulators 0.15.0 release Minor bump: ddm_normal_st is a new model, and the seeded variability streams (#348) change seeded outputs for the full_ddm_rv-based models. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * Refresh uv.lock for the 0.15.0 version bump The lockfile records the project's own version, and docs CI syncs with --locked. Plain `uv lock` on 273c5f9 moved nothing else: one-line diff, ssm-simulators 0.14.0 -> 0.15.0, no dependency changes. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * build(deps): bump pypa/cibuildwheel from 4.2.0 to 4.2.1 Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 4.2.0 to 4.2.1. - [Release notes](https://github.com/pypa/cibuildwheel/releases) - [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md) - [Commits](pypa/cibuildwheel@v4.2.0...v4.2.1) --- updated-dependencies: - dependency-name: pypa/cibuildwheel dependency-version: 4.2.1 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> * build(deps): bump actions/deploy-pages from 5.0.0 to 5.0.1 Bumps [actions/deploy-pages](https://github.com/actions/deploy-pages) from 5.0.0 to 5.0.1. - [Release notes](https://github.com/actions/deploy-pages/releases) - [Commits](actions/deploy-pages@cd2ce8f...368f825) --- updated-dependencies: - dependency-name: actions/deploy-pages dependency-version: 5.0.1 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> * Stop tracking uv.lock A lockfile never reaches users of a published library; it only pinned the dev/CI environment, and CI did not enforce it (uv sync without --frozen re-resolves whenever pyproject.toml changes). HSSM and LANfactory already resolve fresh, which is what surfaces upstream breakage early. Dropping it removes the lock-refresh commit from every version bump. Also: scripts/docs.sh no longer passes --locked, the docs workflow no longer triggers on the lockfile, and the uv rule and CLAUDE.md say that pyproject.toml is the source of truth. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(rl): build the non-numeric frames explicitly for pandas 3 Two data-validation tests assigned a string into an int64/float64 column and relied on pandas upcasting it to object. pandas 3 raises LossySetitemError instead, so the tests failed before reaching the validator. Cast the column to object first; the validator's own dtype report is what the tests check. The lockfile had pinned pandas 2.3.3; resolving fresh is what surfaced this. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix(kde): refuse displace_t for models whose RTs start below t LogKDE(displace_t=True) shifts every RT by t and floors the labels of anything that lands at or below zero. For the models in KDE_NO_DISPLACE_T and those declaring ndt_edge_shift that puts the cutoff inside the response-time support: a uniform st kernel starts at t - st, and the Normal, truncated-Normal and Rayleigh kernels have no edge at t at all. TrainingDataGenerator already overrides displace_t to False for them, so the pipeline was never affected; a direct LogKDE caller got flat labels in the band below t without a word. LogKDE now resolves the model from metadata["model"] (a _deadline suffix stripped, as lan_mlp does) and raises a ValueError naming the model when its config declares ndt_edge_shift or its name is in KDE_NO_DISPLACE_T. An unregistered name is refused when the metadata carries a non-zero st. Every other model keeps the shift by t unchanged. This replaces #359 by EItanm1999, which recovered the boundary from the simulator metadata instead: t - st for the uniform kernels, which was right, and t + t_dist.keywords["loc"] otherwise, which for ddm_truncnormt is the kernel's centre rather than its edge at 0 and collapsed the labels. Since the generator never displaces these models, the honest behaviour for direct callers is an error pointing at displace_t=False. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(kde): cover the displace_t guard Every KDE_NO_DISPLACE_T model (plus full_ddm2, whose config name differs from its key) is refused at displace_t=True with the model named and still builds at displace_t=False. ddm, ddm_sdv and angle keep the plain shift by the metadata t, pinned against the label formula. An unregistered model is judged by its st, and a _deadline name is looked up by its base model. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * Give each generated file its own theta block and pin the sampling order Two independent defects made multi-file LAN training sets far smaller than their nominal size. Both were introduced together in 57389be ("big refactor of training data generators to improve modularity", PR #247, 2026-01-02) and are present in every release from v0.12.0-beta.1 through v0.14.0. 1. `AbstractParameterSampler._topological_sort` walked `self._dependency_graph`, a dict whose insertion order was seeded at `_build_dependency_graph` by `all_params = set(self.param_space.keys())`. Python randomises string hashing per interpreter, so the sampling order -- and therefore which parameter receives which draw from the sequentially consumed RNG in `sample()` -- differed between processes. Same seed, same numbers, different parameters. Verified: for a full_ddm-shaped space the order is `v,a,sv,t,st,z,sz` at PYTHONHASHSEED=0 and `a,v,sv,t,st,z,sz` at PYTHONHASHSEED=2. Fixed by traversing `param_space` insertion order first, then any dependency-only names sorted, and by sorting the adjacency sets. Topological validity is unchanged. 2. `SimulationPipeline.generate_for_parameter_set` used the theta INDEX directly as the parameter-RNG seed, and every caller supplied `range(0, n_parameter_sets)`. Two consequences: - `ssms generate --n-files N` loops `generate_data_training(save=True)` N times in ONE process (ssms/cli/generate.py), so the N files carry N identical theta sets. Reproduced: two calls on a 4-theta full_ddm config share 4 of 4 theta rows. - one SLURM array task per output file gives files that are coordinate permutations of a single theta set, by defect 1. Fixed with a `parameter_sampler_index_offset` read from `generator_config` (set it to `task_id * n_parameter_sets` for array jobs) plus a per-generator cursor in `TrainingDataGenerator` that advances by `n_parameter_sets` on every call, so repeated in-process calls consume disjoint index blocks. Together the two mean that files which look distinct recycle one pool of rows, each file assigning that pool's per-index numbers to a different parameter -- so the design acquires cross-axis structure it was never meant to have, while every margin stays uniform and nothing errors. Measured effect on a 10-file x 5000-theta ddm_sz set generated with the pre-fix code: ~20k distinct values per axis out of 50k rows, and a union centred L2 discrepancy of 1.7e-4 against 2.2e-5 for 50k i.i.d. points -- the union is 5000 distinct unit points, not 50,000. On a 10-file x 5000-theta ddm_sdv set, ten files nominally drew 250,000 unit values and delivered 25,408; file 1's `v` column is file 0's `a` column, and files 2 and 7 share a permutation exactly. Regression coverage: tests/test_parameter_sampling_determinism.py asserts sampling-order stability across PYTHONHASHSEED values in separate subprocesses, identical thetas for equal offsets, disjoint thetas (row-wise and per-axis) for distinct offsets, and disjoint thetas across two `generate_data_training()` calls. All four need more than one process or more than one call, which is why the existing single-process tests passed. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix(datagen): apply the theta-index offset once, in the generator The offset introduced by the previous commit could only come from the YAML, and LAN_pipeline_minimal runs one YAML for every SLURM array task. With the sampling order now pinned and the offset defaulting to 0, every array task would have written the SAME theta block -- the KDE path going from permuted copies of one block to exact copies. Two further problems sat next to it: each concrete pipeline read the offset itself, so a custom DataGenerationPipelineProtocol got the cursor but never the offset, and the documented array formula task_id * n_parameter_sets collides as soon as a task writes a second file. TrainingDataGenerator now owns the whole index line. When the config gives no parameter_sampler_index_offset it draws a 62-bit base from OS entropy once per instance (wide enough that independently drawn blocks never overlap, narrow enough that base + block survives YAML, JSON, MLflow and int64 unchanged); an explicit offset is used as given. Base and cursor are added before the index is handed to the pipeline, so pipelines -- custom ones included -- receive final indices and the per-pipeline reads are gone. Every output file records its own first index under generator_config["pipeline"]["parameter_sampler_index_offset"], so a run started without an offset can be audited and any single file regenerated by passing the recorded value back. The config helper returns None for "absent" (an explicit 0 must stay 0) and rejects negative offsets, which cannot seed an RNG. The documented array formula is task_id * n_files * n_parameter_sets. Tests adapted to the design: two offset-free generators draw different bases and share no row on any axis; a generator built from a file's recorded config reproduces the file in a fresh interpreter under another PYTHONHASHSEED; offset 0 called twice equals offsets 0 and n; an explicit offset is honoured from either config placement and reaches a custom pipeline; neither concrete pipeline shifts an index itself. The dependency-order test was vacuous -- no model it listed has a dependent bound -- and now covers full_ddm_rv plus a synthetic space with a two-dependent parameter, with the edge counts asserted. One subprocess per hash seed prints every space's order instead of one per (seed, model): the file runs in 16 s instead of 47 s. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(datagen): prove the recorded offset is what reproduces a file The regeneration test passed on the previous source as well: with no base drawn and a default offset of 0, any offset-free config reproduces any other, so the check could not tell a recorded value from a fixed default. The worker now regenerates from both the recorded config and the offset-free one it came from, in one interpreter, and only the first may match. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix(sampler): sample in the declared parameter order The first pin put the sampling order at the exact reverse of the declaration -- the depth-first search prepends -- and visited "dependency-only" names that cannot exist, since _build_dependency_graph rejects any bound naming a parameter outside param_space. The order changes for every stored training set in this release regardless, so pin the one that needs no explanation: param_space insertion order, a dependency pulled ahead only when it is declared after a parameter that needs it. For every registered model that is `params` itself, so the parameter <-> draw assignment of a file is readable from its model config. Topological validity and cycle detection are unchanged; the dependency graph is still built, for its validation. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix(datagen): keep the PyDDM legacy seed inside the 32-bit range PyDDMPipeline still seeds the legacy global RNG with the theta index for any downstream code that draws from it, and np.random.seed rejects anything above 2**32 - 1. With the generator now drawing a 62-bit base from entropy, every PyDDM run raised "Seed must be between 0 and 2**32 - 1" from inside the worker pool; the KDE path was unaffected because default_rng takes any non-negative integer. Reduce the legacy seed modulo 2**32 and pin both pipelines on a 62-bit index. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * docs(race): cite the validation reference and record why the inline RNG stays The module docstring pointed at a race_model.pdf that is not in the repo. Describe the model directly and cite the preprint whose Appendix B the Volterra validation reference (#365) follows. Also state why this kernel keeps the xoshiro/Box-Muller generator copied from addm_models.pyx rather than the shared GSL RNG: the GSL path is only live on GSL builds and only for n_threads > 1 (PyPI wheels build without GSL and the stub returns 0.0), whereas the inline generator needs no GSL and gives n_threads-independent results. Consolidation of the two kernels is tracked in #376. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * refactor(race): mechanical review items, no behaviour change - stage_changed is a bint assigned True/False - the per-accumulator stage-count memoryview is n_stages, not d (the public d_array keyword is unchanged) - shape validation names the offending array instead of one combined condition, and the node-monotonicity check is a single vectorised numpy expression over the active transitions that reports the row and accumulator - nondecision_time / deadline resolution uses plain if blocks, and the two length checks are separate with distinct messages Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(race): add simulator behaviour tests Taken unchanged from the closed #364 branch (6114f34), where they were written for this simulator but never merged: deterministic single-accumulator crossing, fastest accumulator wins, native stage switches are used, the omission sentinel, and seeded output independent of n_threads. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix(race): reject negative nondecision_time/deadline and deadline < ndt A negative nondecision_time silently produced negative response times and a deadline below the nondecision time omitted every trial without saying so. Validate both after they are resolved to per-trial arrays. The three tests are the ones proposed in review on #342. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(race): cover return_option and the input contract return_option: minimal omits x_final, full reports it per (sample, trial, accumulator), anything else raises. Input contract through the public function: per-array shape mismatch, d_array range and shape, decreasing active nodes (and decreasing nodes in padded stages being ignored), x0 shape, the 32-accumulator cap, non-3D mu, zero samples, and nondecision_time / deadline of the wrong length. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * refactor: move race_math to notebooks/ as notebook-only reference code The three analytical one-accumulator quantities (first-passage density, CDF, and surviving-position density) are only used by the two race notebooks, so they move out of ssms.basic_simulators as suggested in review, and their validation-only test goes with them. The notebooks now import the module from their own directory. While moving: describe each function and the shared arguments in the docstrings instead of citing an unnamed "equation (14)", and split the dense density expression into a normaliser and an exponent. The maths is unchanged. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> --------- Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: “mariama” <“maria_ma@brown.edu”> Co-authored-by: Alexander Fengler <alexanderfengler@gmx.de> Co-authored-by: Claude Opus 5 <noreply@anthropic.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: eitan_perlin@brown.edu <eperlin@node2333.oscar.ccv.brown.edu>
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LogKDE's displace_t path shifts response times by metadata['t'] before fitting. For a model with trial-to-trial variability in non-decision time that is the wrong quantity: t is drawn per trial from Uniform(t - st, t + st), so no rt can fall below t - st, while real density fills the band [t - st, t]. Displacing by t puts the hard cutoff INSIDE the support, and the KDE floors that whole band.
The error is not smearing below the edge, which was the original hypothesis and turned out to be wrong - below the edge the KDE sits exactly at its floor by construction. It is the opposite sign, on the other side: just ABOVE the edge the KDE is floored where the truth is finite. At t - st + 0.02 the exact likelihood gives -22.89 and the KDE gives -66.77.
Measured against the exact ddm_st likelihood, 4 of 4 parameter sets: mean absolute error above the edge falls from 24.98 to 14.17 log units. Label-level A/B over 8 parameter sets: the fraction of labels sitting at the KDE floor falls from 5.897% to 3.912%; 49.59% of labels change at all but the median change is only 0.034 with a maximum of 67.74 - the fix rewrites a small fraction of labels dramatically (the un-floored band) and nudges the rest negligibly. A network trained on the corrected labels, scored against the exact likelihood over 12 parameter sets: median MAE 0.0362 -> 0.0259, mean 0.3548 -> 0.0356, worst 2.9515 -> 0.0984, better on 10 of 12. It is a tail fix, which is why it never showed up in aggregate training loss.
Two metadata shapes carry st and both are handled:
Models with neither fall back to t, where the boundary genuinely is t, so behaviour for fixed-t models is unchanged.
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