initial poisson race implementation by hayden-johnson · Pull Request #840 · lnccbrown/HSSM

hayden-johnson · 2025-10-28T13:52:09Z

Issue #801

This PR introduces a preliminary implementation of the Poisson race model as described in Appendix A of:

A comparison of two response time models applied to perceptual matching

Key Differences from the original formulation

Stimulus-dependent rate parameters are not implemented. This would require trial-by-trial stimulus identifiers for log-probability evaluation.

Shape parameter generalization: we do not restrict the shape parameter to be an integer. This simplifies the implementation but requires evaluation of the incomplete gamma function using scipy, rather than using factorials.

codecov · 2025-10-28T14:38:06Z

Codecov Report

✅ All modified and coverable lines are covered by tests.

Files with missing lines	Coverage Δ
src/hssm/_types.py	`100.00% <ø> (ø)`
src/hssm/likelihoods/__init__.py	`100.00% <ø> (ø)`
src/hssm/likelihoods/analytical.py	`100.00% <100.00%> (ø)`
src/hssm/modelconfig/poisson_race_config.py	`100.00% <100.00%> (ø)`
tests/test_likelihoods_poisson_race.py	`100.00% <100.00%> (ø)`

... and 4 files with indirect coverage changes

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AlexanderFengler

Thanks @hayden-johnson this looks reasonable as far as the likelihood is concerned. We might want to check in on the trial-wise covariate issues if it remains unresolved.

Apart from that a few more high level comments to get the PR over the finish line.

We need some tests for the basic likelihood
2 We need the basic simulator implemented in ssm-simulators (separate small PR)
We need a small notebook to that illustrates the model and shows a model fit (goal is to start having these for all incoming models and backfill for existing ones, so people have a much easier time getting starting with using specific models in the toolbox).

src/hssm/likelihoods/analytical.py

Copilot

Pull Request Overview

This PR implements the Poisson race model for two-choice response time modeling, based on a comparison of response time models. The implementation uses continuous shape parameters and evaluates log-likelihoods analytically using gamma distributions.

Key changes:

Adds analytical log-likelihood computation for the Poisson race model with two accumulators
Includes comprehensive unit tests validating vectorization, exponential edge cases, and parameter validation
Registers the new model in HSSM's type system and likelihood exports

Reviewed Changes

Copilot reviewed 5 out of 5 changed files in this pull request and generated 1 comment.

Show a summary per file

File	Description
`src/hssm/likelihoods/analytical.py`	Core implementation of `logp_poisson_race` function with parameter bounds and distribution registration
`src/hssm/modelconfig/poisson_race_config.py`	Configuration defining model parameters, priors, and choices for the Poisson race model
`tests/test_likelihoods_poisson_race.py`	Unit tests covering vectorization, exponential special case, parameter validation, and edge cases
`src/hssm/likelihoods/__init__.py`	Exports the new `logp_poisson_race` function
`src/hssm/_types.py`	Registers "poisson_race" as a valid model type

Comments suppressed due to low confidence (2)

src/hssm/likelihoods/analytical.py:1

Corrected grammar: 'We do not condition on the underlying stimulus condition.'

"""pytensor implementation of the Wiener First Passage Time Distribution.

src/hssm/likelihoods/analytical.py:1

The documentation states 'Response > 0 indicates accumulator 1', but the code at line 618 uses r_c = pt.switch(flip, r2, r1) where flip = response > 0. This means positive responses select r2/k2 (accumulator 2), not accumulator 1. The documentation should be corrected to match the implementation, or clarify the accumulator indexing convention.

"""pytensor implementation of the Wiener First Passage Time Distribution.

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src/hssm/likelihoods/analytical.py

cpaniaguam

The tests look great! I added a suggestion for adopting the more declarative and pythonic list comprehension pattern with a function to do the number crunching.

tests/test_likelihoods_poisson_race.py

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hayden-johnson · 2025-12-16T02:06:35Z

@AlexanderFengler

Added an implementation of the Poisson race model in ssm-simulators (see PR 246), along with a minimal example notebook in HSSM/docs/poisson_race.ipynb.

Lmk what you think! Feel free to suggest additions to the tutorial notebook.

fix shape bounds Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

add pythonic syntax Co-authored-by: Carlos Paniagua <cpaniaguam@gmail.com>

cpaniaguam

Looking real good! Left a few more observations. I haven't looked at the notebook yet.

src/hssm/likelihoods/analytical.py

docs/tutorials/poisson_race.ipynb

Co-authored-by: Carlos Paniagua <cpaniaguam@gmail.com>

Copilot

Pull request overview

Copilot reviewed 5 out of 6 changed files in this pull request and generated 8 comments.

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docs/tutorials/poisson_race.ipynb

src/hssm/likelihoods/__init__.py

src/hssm/likelihoods/analytical.py

Copilot · 2026-01-28T14:05:29Z

tests/test_likelihoods_poisson_race.py

+@pytest.mark.parametrize(
+    "param,bad_value",
+    [
+        ("r1", 0.0),
+        ("r2", -0.1),
+        ("k1", 0.0),
+        ("k2", -1.0),
+        ("t", -0.2),
+    ],
+)
+def test_poisson_race_parameter_validation(poisson_race_data, param, bad_value):
+    """Invalid parameter values should produce a ParameterValueError."""
+    assert_parameter_value_error(
+        poisson_race_data, theta_poisson_race, **{param: bad_value}
+    )


The test only validates that 0.0 and negative values are rejected, but doesn't test boundary values like very small positive numbers (e.g., 1e-10). Additionally, there's no test case for when t equals 0, which is a valid edge case according to the bounds (t >= 0) but might behave differently than other values.

src/hssm/likelihoods/__init__.py

AlexanderFengler · 2026-02-03T09:31:07Z

Overall looks like this is basically in shape, apart from some of the CoPilot comments (some of which seem worth addressing). thanks @hayden-johnson

fix doctring typo Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

AlexanderFengler

This looks good now, thanks @hayden-johnson

Copilot

Pull request overview

Copilot reviewed 6 out of 7 changed files in this pull request and generated 4 comments.

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Copilot · 2026-02-16T23:28:40Z

tests/test_likelihoods_poisson_race.py

+    return np.array(
+        [
+            (0.45, 1.0),
+            (0.55, -1.0),
+            (0.65, 0.0),
+            (0.75, 1.0),
+        ],


The test data includes response == 0.0, but the Poisson race config declares choices as [-1, 1] and the simulator typically emits -1/1. Using 0.0 here makes the test exercise behavior for an out-of-spec response code; consider switching these responses to -1.0 or 1.0 (or, if 0 is intended to be supported, update the model config/validation accordingly).

Copilot · 2026-02-16T23:28:41Z

tests/test_likelihoods_poisson_race.py

+    data = np.array(
+        [
+            (0.4, 1.0),
+            (0.5, -1.0),
+            (0.8, 0.0),
+            (0.9, 1.0),
+        ],
+        dtype="float32",
+    )


This parametrized dataset includes response == 0.0, but the model config advertises only [-1, 1] choices and most downstream code assumes binary signed choices. Recommend changing 0.0 to a valid choice value (e.g. -1.0) unless 0 is an explicitly supported third outcome (in which case the config and likelihood should validate/document it).

Copilot · 2026-02-16T23:28:41Z

src/hssm/modelconfig/poisson_race_config.py

+        "choices": [-1, 1],
+        "description": "The Poisson Race Model",


choices is set to [-1, 1], but the analytical likelihood routes all response <= 0 trials to accumulator 1 (so 0 is treated as a valid code) and the new tests/tutorial examples include response == 0.0. Either tighten the likelihood to validate/enforce response ∈ {-1, 1} or expand choices/documentation to reflect that 0 is a supported value.

Suggested change

"choices": [-1, 1],

"description": "The Poisson Race Model",

"choices": [-1, 0, 1],

"description": "The Poisson Race Model (supports responses -1, 0, 1; responses <= 0 map to accumulator 1 in the analytical likelihood)",

I will make one last change in this regard, then all set from my perspective ;)

Copilot · 2026-02-16T23:28:42Z

src/hssm/simulator.py

+    # The analytical Poisson-race likelihood assumes exact race finishing times.
+    # ssms defaults to smooth_unif=True; override to False unless the caller
+    # explicitly requests otherwise.
+    if model == "poisson_race" and "smooth_unif" not in kwargs:
+        kwargs["smooth_unif"] = False
+


This changes simulate_data behavior by implicitly forcing smooth_unif=False for the poisson_race model unless the caller overrides it. Since tests/test_simulator.py already covers simulator defaults, consider adding a focused test that asserts (1) smooth_unif defaults to False for poisson_race, and (2) an explicit smooth_unif=True/False passed by the caller is respected.

cpaniaguam

lgtm

cpaniaguam · 2026-02-26T19:12:00Z

src/hssm/likelihoods/analytical.py

+    "k1": (np.finfo(float).eps, np.inf),
+    "k2": (np.finfo(float).eps, np.inf),
+    "t": (0.0, np.inf),
+}


One can always retrieve the poisson_race_params object from poisson_race_bounds with list(mydict.keys())

Copilot

Pull request overview

Copilot reviewed 6 out of 8 changed files in this pull request and generated no new comments.

Comments suppressed due to low confidence (2)

tests/test_likelihoods_poisson_race.py:78

In _compute_exponential_logp, the response parameter is never used. This makes the helper signature misleading and can trigger unused-argument linting in some setups. Consider removing the response argument (or using it if it was intended for a check).

    def _compute_exponential_logp(rt, response, winner_rate, loser_rate):
        log_pdf = np.log(winner_rate) - winner_rate * rt
        log_survival = -loser_rate * rt
        return log_pdf + log_survival

tests/test_likelihoods_poisson_race.py:115

The test name test_poisson_race_negative_rt_returns_logp_lb is misleading: the test data uses a positive RT (0.02) and is really exercising the rt <= t (non-decision time) clipping behavior. Renaming the test (and/or updating the docstring) would make the intent clearer.

def test_poisson_race_negative_rt_returns_logp_lb():
    """Trials with rt <= t should clip to LOGP_LB."""
    data = np.array([(0.02, 1.0)], dtype="float32")
    theta = dict(r1=2.0, r2=3.0, k1=1.2, k2=1.4, t=0.05)
    logp = logp_poisson_race(data, **theta).eval()
    assert np.allclose(logp, LOGP_LB)

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initial poisson race implementation

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AlexanderFengler reviewed Nov 2, 2025

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hayden-johnson added 3 commits November 16, 2025 17:54

add to public api

132e2e1

add basic test suite for poisson race

6c0e39f

fix formatting

5f91971

AlexanderFengler requested a review from cpaniaguam November 17, 2025 22:32

AlexanderFengler marked this pull request as ready for review November 17, 2025 22:32

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hayden-johnson mentioned this pull request Dec 16, 2025

poisson race simulator lnccbrown/ssm-simulators#246

Merged

minimal tutorial notebook

7a24aa1

hayden-johnson and others added 3 commits December 29, 2025 19:55

Update src/hssm/likelihoods/analytical.py

c62d4d8

fix shape bounds Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

Update tests/test_likelihoods_poisson_race.py

f2fc290

add pythonic syntax Co-authored-by: Carlos Paniagua <cpaniaguam@gmail.com>

fix formatting for test_likelihoods_poisson_race

776de18

hayden-johnson requested review from AlexanderFengler and cpaniaguam January 6, 2026 17:54

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hayden-johnson and others added 4 commits January 22, 2026 11:17

build bound dicts with comprehension

f390d2c

Co-authored-by: Carlos Paniagua <cpaniaguam@gmail.com>

fix logp_poisson_race docstring

737b069

fix docstring, add consitent safe usage

aeb123e

fix formatting

425aebc

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Update src/hssm/likelihoods/analytical.py

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fix doctring typo Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

AlexanderFengler previously approved these changes Feb 10, 2026

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hayden-johnson added 5 commits February 17, 2026 00:14

fix export in init

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fix docstring

71b2ff6

remove smooth uniform for poisson race

6d43a60

fix parameter bounds

c8e933b

update tutorial notebook

5fb5ac9

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hayden-johnson dismissed AlexanderFengler’s stale review via 5fb5ac9 February 27, 2026 19:50

address remaining comments

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AlexanderFengler merged commit ad82147 into main Mar 3, 2026
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