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Adds the registry entry for the DDM with uniform trial-to-trial
variability in non-decision time (t_trial ~ Uniform(t - st, t + st); st
is the half-width, SD = st/sqrt(3)). approx_differentiable only, LAN via
ddm_uniform_st.onnx; bounds are the network's training box - in particular
t >= 0.25 and st in [1e-3, 0.25].
Requires the ddm_uniform_st LAN to be published to the franklab/HSSM hub as
ddm_uniform_st.onnx (90 KB; conversion and parity check scripted separately).
The simulator side is the same config that released ssm-simulators
(0.14.0) ships as ddm_st; the explicit name ddm_uniform_st arrives with
our ssm-simulators PR "Add ddm_normal_st and register ddm_uniform_st as
the explicit name of ddm_st" (branch feat/ddm-normal-st-model-config,
stacked on #359, not yet opened). Until that alias ships,
hssm.simulate_data("ddm_uniform_st") needs the ssms name ddm_st.
Correct inference for this model also needs the st-aware admissibility
floor (fix/ensure-positive-ndt-st-aware): without it the stock guard
floors real density in [t - st, t] on ~0.14% of trials and biases t.
Records that the bounds are the network's training box rather than modelling
choices, and that st means a uniform half-width in ddm_uniform_st but a Normal SD in
ddm_normal_st, so equal st values are not equal dispersions.
Lists the model where users look for it, and adds a changelog entry.
Stacked on #1292 (first commit is #1292's; review the top commit). Model string is ddm_uniform_st; the same config exists upstream in ssm-simulators as ddm_st, and an ssm-simulators PR registers ddm_uniform_st as its explicit alias so hssm.simulate_data accepts the new name. The network ddm_uniform_st.onnx is not yet on franklab/HSSM; until it is, constructing the model needs loglik=<local path>.
Tests: the branch's own test files pass on current main (fefed57) in an environment with ssm-simulators 0.14.0 and bambi 0.21; see the commit for the added cases. Fork CI has not been approved for this fork, so no check-runs appear here.
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The pull request adds a default configuration for ddm_st, updates model and simulator-related type declarations, and adds tests for configuration settings and response-time support. It also documents the model and its st parameter convention.
Changes: ddm_st model support
Layer / File(s)
Summary
Model configuration and type declarations src/hssm/_types.py, src/hssm/config.py, src/hssm/modelconfig/ddm_st_config.py, tests/test_modelconfig.py
The default configuration specifies ddm_st responses, parameters, choices, likelihood settings, and bounds. The types add the ddm_st model literal and allow simulator names in Config.rv. Tests check the default configuration and model registration.
Response-time support settings tests/test_modelconfig.py
Tests check that Config.from_defaults carries the ndt_edge_shift declaration and that the compiled log probability applies the floor to response times at or below t - st.
Model and parameter documentation docs/reference/models-and-likelihoods.md, docs/tutorials/likelihoods.ipynb, docs/explanations/coming_from_hddm.md
The documentation lists ddm_st as an approx_differentiable model, adds it to the tutorial model lists, and explains the difference between the HSSM and HDDM st conventions.
lnccbrown/HSSM#1435: Both PRs configure ndt_edge_shift from the uniform non-decision-time parameter st.
lnccbrown/HSSM#1441: Both PRs address training-range bounds for approx_differentiable LAN configurations.
Suggested labels:documentation
Suggested reviewers:alexanderfengler
Merge Risk: Moderate · up to 11bd1
Default ddm_st model construction will fail until the likelihood network is uploaded to franklab/HSSM, so the upload should land before or with this merge. Passing a local loglik path works in the meantime. The earlier changelog, test-alias and edge-test concerns are resolved.
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Actionable comments posted: 1
🪄 Fix CodeRabbit comments on this PR
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Inline comments:
In `@src/hssm/modelconfig/ddm_uniform_st_config.py`:
- Around line 26-34: Set the model configuration’s simulator alias to “ddm_st”
by adding the rv entry near the existing response and list_params settings,
while preserving “ddm_uniform_st.onnx” as the likelihood artifact.
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🪄 Fix CodeRabbit comments on this PR
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Inline comments:
In `@docs/changelog.md`:
- Line 7: Update the changelog entry for ddm_uniform_st to replace the `#XXXX`
placeholder with the actual issue or pull request number, or remove the
reference if no number is available.
In `@tests/distribution_utils/test_distribution_utils.py`:
- Line 328: Update the parametrized test’s rt values to include expected_edge
explicitly, covering the exact boundary for both fixed-t and st cases and
preserving the requirement that LOGP_LB is selected at the inclusive threshold.
In `@tests/test_modelconfig.py`:
- Line 300: Remove the negative assertion for "ddm_uniform_st" from the test,
while retaining the assertions that lk_approx_differentiable["rv"] equals
"ddm_st" and that this value exists in ssms_model_config.
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Adds the registry entry for the DDM with uniform trial-to-trial
variability in non-decision time (t_trial ~ Uniform(t - st, t + st); st
is the half-width, SD = st/sqrt(3)). approx_differentiable only, LAN via
ddm_uniform_st.onnx; bounds are the network's training box - in particular
t >= 0.25 and st in [1e-3, 0.25].
Requires the ddm_uniform_st LAN to be published to the franklab/HSSM hub as
ddm_uniform_st.onnx (90 KB; conversion and parity check scripted separately).
Released ssm-simulators (0.14.0) ships this simulator as 'ddm_st', so the
config sets rv: "ddm_st" inside the likelihood block; 'ddm_uniform_st'
becomes an ssms alias in lnccbrown/ssm-simulators#361.
Correct inference for this model also needs the st-aware admissibility
floor (fix/ensure-positive-ndt-st-aware): without it the stock guard
floors real density in [t - st, t] on ~0.14% of trials and biases t.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- Records that the bounds are the network's training box rather than modelling
choices, and that st means a uniform half-width in ddm_uniform_st but a Normal SD in
ddm_normal_st, so equal st values are not equal dispersions.
- Lists the model where users look for it, and adds a changelog entry.
The ddm_uniform_st LAN is retrained on 300 files with st in (0.001, 0.35);
the registration's bounds are its training box, so st's upper bound moves
from 0.25 to 0.35. ddm_normal_st no longer shares the same st bounds, so
the docstring and changelog stop saying it does.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Brings in lnccbrown#1292 in its merged form, which replaced the st-aware floor the
branch was built on with a per-likelihood `ndt_edge_shift` declaration
(NDTEdgeShift in _types.py, a LoglikConfig key, Config/ModelConfig fields,
validation in Config.validate and resolution in dist.py). Two conflicts:
- src/hssm/_types.py: kept both sides. main's NDTEdgeShift class and the
`ndt_edge_shift` LoglikConfig key stay where main put them; this branch's
"ddm_uniform_st" SupportedModels entry and the `rv` LoglikConfig key are
kept alongside.
- docs/changelog.md: taken from main entirely. The branch's hunk (a new
lnccbrown#1346 entry plus an edit of lnccbrown#1292's entry) is dropped: changelog entries
are written on main after merge, not carried in PRs.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
ssms' ddm_st simulator draws the non-decision time as t + U(-st, st), so
st is the half-width and the fastest admissible response time is t - st.
Without an `ndt_edge_shift` declaration the guard from lnccbrown#1292 keeps its
floor at t and replaces the likelihood on (t - st, t] with LOGP_LB, the
band this kernel puts real density on. Declare {"param": "st", "scale":
1.0} (full_ddm's hddm_wfpt reads st as the full width and declares 0.5).
The tests pin the registry entry and Config.from_defaults, and check the
compiled logp against a flat stand-in likelihood: LOGP_LB at and below
t - st, untouched above it.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
…edge guard
The guard keyed its ssms lookup on the HSSM model name, so a LAN that
aliases its simulator through `rv` (ddm_uniform_st -> ddm_st, lnccbrown#1346) was
never compared and the drift it exists to catch passed silently. Resolve
`rv` first, as hssm.py does, and pin the alias path.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
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Thanks Eitan. The code that is here works: rv routes prior and posterior predictive sampling to ssms ddm_st, the model-table row is in, and the head (6e8a3c8) merges cleanly onto main and is green on 3.12/3.13/3.14 plus the docs build. Since the first look, #1292 and #1293 have merged and I pushed two commits to your branch (below), which clears two of the earlier blockers. What is left is the name, the st box, the network and two docs points, so this stays a request for changes and is not part of 0.5.1, which is being prepared from main now.
Pushed to feat/register-ddm-st
0f3f6dc4 merges main after #1292. Two conflicts: _types.py keeps both sides (main's NDTEdgeShift and ndt_edge_shift key, your "ddm_uniform_st" entry and rv key); docs/changelog.md is taken from main and your hunk is dropped, as for the other PRs. Entries are written on main after the merge, and this hunk also edited #1292's entry.
6e8a3c82 adds "ndt_edge_shift": {"param": "st", "scale": 1.0} next to "rv", with two tests in tests/test_modelconfig.py: the registry entry and Config carry it, and the compiled logp (against a flat stand-in network) is LOGP_LB at and below t − st and untouched above. #1292 as merged does not infer the edge from a parameter named st; each likelihood declares it, and without the declaration the floor would sit at t and clip (t − st, t], the band this kernel puts density on. ssm-simulators 0.15.0 declares the same value on ddm_st (lnccbrown/ssm-simulators#370), and #1360's guard compares the two, resolving the simulator through rv, so it already covers this entry.
The NaN start is gone with #1293: the default t initval (0.025, below this model's t ≥ 0.25) is clamped into the bounds, so a default fit no longer diverges on every draw. Please still run a default .sample() smoke fit (no initvals) on the final head; nothing in the suite exercises that path for this model.
Still to do
Register it as ddm_st, the ssms name (src/hssm/modelconfig/ddm_uniform_st_config.py:39). Every other simulator-backed model uses its ssms name, and the alias was not kept: lnccbrown/ssm-simulators#361 is closed, its ddm_normal_st config landed separately (lnccbrown/ssm-simulators#369), and 0.15.0 has no ddm_uniform_st. Concretely: the training-box guard (test_declared_bounds_match_the_training_box) looks models up by name in ssms.config.model_config, so it skips this one; hssm.simulate_data("ddm_uniform_st") raises model must be one of [...] and plot_model_cartoon raises Unknown model 'ddm_uniform_st' from ssms, because rv covers only the predictive path. Same config and bounds, network file ddm_st.onnx, provenance under lan/ddm_st/; the rv entry and the comment above it (which still says an upstream alias is coming) go away. The guard then becomes active for this model, which is where the next point bites.
The st box: 0.25 or 0.35 (ddm_uniform_st_config.py:52, your commit 74ba935). ssms 0.15.0's ddm_st has st ≤ 0.25 in param_bounds, and the guard takes ssms as the source of truth, so after the rename it reports st: (0.001, 0.35) against (0.001, 0.25). A wider box needs ddm_st's param_bounds changed in ssm-simulators first, in its own PR. Two things to settle before that:
With t ≥ 0.25, st ≤ 0.35 puts 0.7% of the box at st > t, where the trial non-decision time can be negative: at t = 0.25, st = 0.35 the simulator emits rt ≤ 0 in up to 4.2% of trials (v = 0, a = 0.3); at st = 0.25 it emits none. ssms' full_ddm_rv bounds st by t ("st": (1e-3, "t")) for this reason.
Options: keep 0.25; raise t's lower bound to 0.35; or keep 0.35 and constrain st ≤ t. Which did the training data use?
The network (ddm_uniform_st_config.py:32). HSSM(model=...) fails with a raw Hugging Face 404 until the file is on franklab/HSSM. Our publishing path (lan-publish, the model card, the lan/<model>/ provenance copy) works from a lab MLflow run, so we will retrain ddm_st in-house through LAN_pipeline_minimal on the agreed box and run it through the same gates as the other 2026 networks. Your network is a valuable cross-check for that. Could you share: the bare .onnx with its sha256; the ssm-simulators commit and branches the 293 files were generated with; the generator config; the training config and history; the parameter order as the ONNX input expects it (the card says v, a, z, t, st); and your exact ddm_st quadrature script, so we can run the density gate and the recovery sweep on both.
On hf.co/Eitanm/ddm-st-lans the root network was replaced on 10-05 (5×100, 293 files per the card); lan/ddm_uniform_st/ and manifest.json still describe the previous 3×100 network with st ≤ 0.25 (root file 165,859 bytes, folder copy 84,861).
Your card says that at a true st of 0.05 the posterior peaks near 0.01, and that st is underestimated near v = 0. Could you check the network against the quadrature for st below 0.05? Your #1345 reports about 20 nats of spurious density at st → 0 for the KDE-trained Normal network, and the standard density gate does not sample that corner.
Docs (ddm_uniform_st_config.py:8, docs/explanations/coming_from_hddm.md:31). full_ddm and HDDM read st as the full width (t ± st/2); here it is the half-width (t ± st). One sentence in the docstring, and the HDDM page, which says st "carries over unchanged", needs qualifying for this model. The docstring's second paragraph (l.11-16) is about ddm_normal_st, which this PR does not register; please move the uniform-vs-Normal comparison into #1345.
Did you train through LAN_pipeline_minimal, and is there an MLflow run? The new network is 5×100 where the previous one was 3×100; is the larger network needed for the wider box?
Before merge
the rename to ddm_st, with the rv plumbing removed;
the st box settled and, if widened, changed in ssm-simulators first;
the validated network on franklab/HSSM;
a default .sample() smoke fit on the final head.
#1345 shares _types.py, the notebook, test_modelconfig.py and the reference table with this branch, so it rebases once this lands.
…rs name
The registry name now matches the simulator in ssm-simulators, so the
simulator resolves from the model name and the `rv` override (with its
note about an upstream alias) goes away. The network file becomes
ddm_st.onnx, following the other LAN entries.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
…nd HDDM
The paragraph comparing the uniform and Normal kernels moves to the
ddm_normal_st PR (lnccbrown#1345), where both models exist.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
… network as pending retraining
Moves the uniform-vs-Normal st comparison here from lnccbrown#1346, naming the
sibling ddm_st, and states the t - 3 * st floor. The st defect-corner
paragraph describes today's ddm_normal_st.onnx; a note says the network
that ships is pending retraining.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
…ing box
ssm-simulators 0.15.0 bounds ddm_st's st at 0.25, and the network that ships
from franklab/HSSM is retrained in-house on that box, so HSSM declares the
same bound and test_declared_bounds_match_the_training_box passes. Reverts
the 0.35 widening from 74ba935; widening the box goes through
ssm-simulators' param_bounds first.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Fixed: st is back to 0.25, matching ssm-simulators' ddm_st box, since the network you retrain in-house uses that box. Fast suite passes locally (1,297 passed, 0 failed). Widening to 0.35 can come later through ssm-simulators first.
Fixed: st is back to 0.25, matching ssm-simulators' ddm_st box, since the network you retrain in-house uses that box. Fast suite passes locally (1,297 passed, 0 failed). Widening to 0.35 can come later through ssm-simulators first.
@EItanm1999, let's forget about the 'in-house retraining' lingo. Important is that, as it is right now, the box from the ssm-simulators configs, matches the box that the network used.
Once that aligned, just ping me and we can go ahead with the merge.
Requesting @cpaniaguam and @digicosmos86 as reviewers. Eitan has addressed the review and CI is green; the remaining merge gate is the network upload to franklab/HSSM, which is on me.
Correction to my last reply: the networks were trained with st up to 0.35 (t from 0.25, no st <= t constraint), so HSSM's 0.25 doesn't match them, and neither does ssm-simulators. I'm opening an ssm-simulators issue + PR to set both ddm_st and ddm_normal_st to the training box, then I'll set #1346/#1345 back to 0.35 against that release. Please hold the merge until then. To your question on #1345: yes, both st networks were trained at full production size (ddm_st 293 training + 6 validation files of 5,000 sets × 200k samples; ddm_normal_st 300 files). full_ddm also has 300 production files, but no full_ddm network is chosen yet.
Resolves the one conflict: main added `jax_loglik` to LoglikConfig at the
line where this branch adds `rv`; both keys are kept.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Resolves the one conflict in tests/test_modelconfig.py, where main added the
softmax_inv_temperature_4 test at the same place as this branch's ddm_st
tests; both are kept. Main's ssm-simulators floor is already >=0.16.0.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
ssm-simulators 0.16.0 (lnccbrown#390) widened ddm_st's st to 0.35, the box the network
was trained on, so HSSM's bound and its test follow. This reverts 11b0e6d,
which held st at 0.25 while the released simulator still declared it.
Main's ssm-simulators floor is already >=0.16.0.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Updated for ssm-simulators 0.16.0, which carries #390 (the st box widened to 0.35):
Merged main (a0f7e62c). One conflict, in tests/test_modelconfig.py, where main's softmax_inv_temperature_4 test sits beside this branch's tests; both kept. Main's floor is already ssm-simulators>=0.16.0.
st bound back to (1e-3, 0.35) in ddm_st_config.py and its test (reverts 11b0e6d).
Full suite locally (uv run pytest tests/ -m "not slow", ssm-simulators 0.16.0): 1574 passed, 1 skipped, 1 xfailed, 0 failed. prek clean.
Default .sample() smoke fit on this head, no initvals, with the staged network (loglik=<local path>, sha256 2e2aec29…): one subject, 1,000 trials simulated from ddm_st at v 1.0, a 1.5, z 0.5, t 0.5, st 0.2. Converges with r-hat 1.00, bulk ESS 915 to 1,220, no divergences, 8 min on 4 CPU cores:
v
a
z
t
st
truth
1.0
1.5
0.5
0.5
0.2
posterior mean (89% interval)
0.99 (0.91, 1.10)
1.49 (1.40, 1.60)
0.52 (0.48, 0.56)
0.53 (0.44, 0.62)
0.16 (0.05, 0.26)
Remaining gate: ddm_st.onnx at the root of franklab/HSSM (#1394).
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Actionable comments posted: 1
Fix comments: Fix CodeRabbit comments on this PR
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Review comments at @docs/tutorials/likelihoods.ipynb:
- Line 295: Update the `approx_differentiable` availability documentation so it
does not present `ddm_st` as ready for default use without its network; state
that constructing `ddm_st` requires a user-supplied local `loglik` path unless
`ddm_st.onnx` is published in the repository root.
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docs/explanations/coming_from_hddm.md
docs/reference/models-and-likelihoods.md
docs/tutorials/likelihoods.ipynb
src/hssm/_types.py
src/hssm/config.py
src/hssm/modelconfig/ddm_st_config.py
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Major · Functional Correctness · Quick win
Publish the default network before advertising it as available.
The default configuration requests ddm_st.onnx, but that file is not in the root of franklab/HSSM. A default ddm_st construction therefore fails unless the user supplies a local loglik path. Publish the network before merge, or document the required local path. (raw.githubusercontent.com)
Prompt for AI Agents
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only still-valid issues, skip the rest with a brief reason, keep changes
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Review comment at @docs/tutorials/likelihoods.ipynb at line 295:
Update the `approx_differentiable` availability documentation so it does not
present `ddm_st` as ready for default use without its network; state that
constructing `ddm_st` requires a user-supplied local `loglik` path unless
`ddm_st.onnx` is published in the repository root.
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Agreed, and this PR does not merge until it is published: the root ddm_st.onnx on franklab/HSSM is the remaining gate in #1394. The validated network, its reports and its card are staged; promotion comes after review, and this PR merges after the file exists.
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@EItanm1999 Thanks for clarifying. The network publication remains the merge gate for #1394.
After publication, please verify that ddm_st.onnx is available at the root of franklab/HSSM and that default ddm_st construction works without a local loglik path.
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Register ddm_uniform_st as a supported model
Adds the registry entry for the DDM with uniform trial-to-trial
variability in non-decision time (t_trial ~ Uniform(t - st, t + st); st
is the half-width, SD = st/sqrt(3)). approx_differentiable only, LAN via
ddm_uniform_st.onnx; bounds are the network's training box - in particular
t >= 0.25 and st in [1e-3, 0.25].
Requires the ddm_uniform_st LAN to be published to the franklab/HSSM hub as
ddm_uniform_st.onnx (90 KB; conversion and parity check scripted separately).
The simulator side is the same config that released ssm-simulators
(0.14.0) ships as ddm_st; the explicit name ddm_uniform_st arrives with
our ssm-simulators PR "Add ddm_normal_st and register ddm_uniform_st as
the explicit name of ddm_st" (branch feat/ddm-normal-st-model-config,
stacked on #359, not yet opened). Until that alias ships,
hssm.simulate_data("ddm_uniform_st") needs the ssms name ddm_st.
Correct inference for this model also needs the st-aware admissibility
floor (fix/ensure-positive-ndt-st-aware): without it the stock guard
floors real density in [t - st, t] on ~0.14% of trials and biases t.
choices, and that st means a uniform half-width in ddm_uniform_st but a Normal SD in
ddm_normal_st, so equal st values are not equal dispersions.
Stacked on #1292 (first commit is #1292's; review the top commit). Model string is
ddm_uniform_st; the same config exists upstream in ssm-simulators asddm_st, and an ssm-simulators PR registersddm_uniform_stas its explicit alias sohssm.simulate_dataaccepts the new name. The networkddm_uniform_st.onnxis not yet on franklab/HSSM; until it is, constructing the model needsloglik=<local path>.Tests: the branch's own test files pass on current
main(fefed57) in an environment with ssm-simulators 0.14.0 and bambi 0.21; see the commit for the added cases. Fork CI has not been approved for this fork, so no check-runs appear here.🤖 Generated with Claude Code
Summary by CodeRabbit
ddm_stmodel, including its differentiable likelihood and default parameter settings.ddm_stparameters and likelihood support, including how its non-decision-time parameter differs from HDDM’s.ddm_st.