Rlssm class make model dist by cpaniaguam · Pull Request #915 · lnccbrown/HSSM

cpaniaguam · 2026-03-02T18:40:53Z

This pull request introduces reinforcement learning sequential sampling model (RLSSM) support to the HSSM package. It adds a new RLSSM class, supporting configuration, likelihood construction, and data validation for RL+SSM models, and refines the configuration workflow to require a fully annotated log-likelihood function. The changes also improve pre-commit configuration and update the package's public API.

Major features and changes:

1. RLSSM Model Integration

Added a new RLSSM class in src/hssm/rl/rlssm.py to support models that combine reinforcement learning processes with sequential sampling models. This class builds a differentiable pytensor Op from an annotated JAX log-likelihood function and enforces strict data requirements for balanced panels.
Introduced a utility function validate_balanced_panel in src/hssm/rl/utils.py to ensure input data forms a balanced panel, which is required for RLSSM models.

2. Configuration Enhancements

Extended RLSSMConfig in src/hssm/config.py to require an ssm_logp_func (an annotated JAX SSM log-likelihood function), replacing the previous loglik/loglik_kind workflow. Added runtime validation to ensure this function is callable and properly annotated. [1] [2] [3]
Updated from_rlssm_dict to accept a config dictionary and extract ssm_logp_func and model_name directly from it, simplifying model instantiation.

3. Public API and Package Structure

Registered RLSSM and RLSSMConfig in the package's public API via src/hssm/__init__.py and created a new src/hssm/rl/__init__.py for RL-related exports. [1] [2] [3]

4. Developer Experience

Updated .pre-commit-config.yaml to exclude the tests/ directory from ruff and mypy checks, streamlining development workflows.

…r RL parameters

…del structure

Copilot

Pull request overview

Adds first-class RL + SSM (RLSSM) support to HSSM by introducing a new RLSSM model that builds a differentiable PyTensor Op from an annotated JAX SSM log-likelihood and plugs it into the existing distribution-building pipeline.

Changes:

Introduces RLSSM model class plus RL utility validate_balanced_panel.
Extends configuration via RLSSMConfig.ssm_logp_func and exposes RLSSM in the public API.
Adds test coverage for RLSSM initialization/model build and updates RLSSMConfig validation tests.

Reviewed changes

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

Show a summary per file

File	Description
`src/hssm/rl/rlssm.py`	New RLSSM model implementation integrating RL likelihood `Op` into HSSMBase.
`src/hssm/rl/utils.py`	Adds balanced-panel validation helper for RLSSM datasets.
`src/hssm/rl/__init__.py`	RL subpackage exports for RLSSM and utilities.
`src/hssm/config.py`	Adds `ssm_logp_func` to `RLSSMConfig` and validates presence.
`src/hssm/__init__.py`	Exposes `RLSSM` / `RLSSMConfig` at top-level.
`tests/test_rlssm.py`	New end-to-end-ish RLSSM tests (init, model build, balanced panel, smoke sampling).
`tests/test_rlssm_config.py`	Updates RLSSMConfig tests to include the new required field.

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src/hssm/config.py

tests/test_rlssm_config.py

Copilot · 2026-03-02T19:01:39Z

src/hssm/rl/utils.py

+            f"same number of trials. Observed trial counts: {dict(counts)}"
+        )
+
+    return int(len(counts)), int(counts.iloc[0])


validate_balanced_panel only checks equal trial counts, but the RL likelihood builder reshapes the row order into (n_participants, n_trials, ...) (see make_rl_logp_func), which assumes each participant’s trials are in one contiguous block (and usually in-trial order). With interleaved participants, the panel can be “balanced” yet produce a silently incorrect likelihood. Consider validating contiguity (each participant appears in exactly one run of length n_trials) and/or sorting by participant_col (+ an optional trial_col if present) before returning (n_participants, n_trials).

Suggested change

return int(len(counts)), int(counts.iloc[0])

# Ensure that each participant's trials form a single contiguous block

# of rows of length n_trials. This is required because downstream code

# reshapes the data into (n_participants, n_trials, ...) based on row

# order, assuming no interleaving across participants.

n_trials = int(counts.iloc[0])

# Identify contiguous "blocks" of identical participant IDs.

blocks = data[participant_col].ne(data[participant_col].shift()).cumsum()

block_counts = data.groupby([participant_col, blocks]).size()

# Each participant must appear in exactly one block, and that block

# must have length n_trials.

blocks_per_participant = block_counts.groupby(level=0).size()

invalid_multi_blocks = blocks_per_participant[blocks_per_participant != 1]

invalid_block_sizes = block_counts[block_counts != n_trials]

if not invalid_multi_blocks.empty or not invalid_block_sizes.empty:

raise ValueError(

"Data must be ordered so that each participant's trials appear in "

"a single contiguous block of rows of length n_trials. "

"Participants with non-contiguous or incorrectly sized blocks "

f"were found. Consider sorting your data by '{participant_col}' "

"and, if available, by a trial index column before building the "

"RL likelihood."

)

return int(len(counts)), n_trials

Copilot · 2026-03-02T19:01:40Z

src/hssm/rl/utils.py

+            "Please provide the correct participant column name via "
+            "`participant_col`."
+        )
+


groupby(participant_col) drops NaN participant IDs by default, which can make n_participants/n_trials incorrect without an explicit error. Consider adding a check like data[participant_col].isna().any() and raising a clear ValueError if participant IDs are missing.

Suggested change

# Ensure there are no missing participant IDs, since groupby will drop NaNs

# silently, which would make n_participants / n_trials incorrect.

if data[participant_col].isna().any():

raise ValueError(

f"Column '{participant_col}' contains missing values. "

"Please fill or remove rows with missing participant IDs before "

"calling validate_balanced_panel."

)

… function

…ble and has required attributes

… callable and properly annotated

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Pull request overview

Copilot reviewed 8 out of 8 changed files in this pull request and generated 2 comments.

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Copilot · 2026-03-02T19:40:46Z

src/hssm/rl/rlssm.py

+            )
+
+        # Rearrange data so missing rows come first (no-op when missing_data=False).
+        self.data = _rearrange_data(self.data)
+


_rearrange_data(self.data) changes row order, but the RL logp Op reshapes trials purely by row order into (n_participants, n_trials, ...). If any rows are moved (e.g., when missing_data=True and rt == -999), this will break per-participant trial sequences and invalidate the RL learning dynamics. Since missing-data networks are not supported for RLSSM, consider raising an explicit error when missing_data/deadline handling is requested (or implement a participant-wise rearrangement that preserves within-subject order).

Copilot · 2026-03-02T19:40:47Z

src/hssm/rl/utils.py

+    counts = data.groupby(participant_col).size()
+    if counts.nunique() != 1:
+        raise ValueError(
+            "Data must form balanced panels: all participants must have the "
+            f"same number of trials. Observed trial counts: {dict(counts)}"
+        )
+
+    return int(len(counts)), int(counts.iloc[0])


validate_balanced_panel() only checks equal trial counts via groupby().size(), but it does not validate that rows are ordered/grouped by participant. The RL likelihood builder (make_rl_logp_func) reshapes arrays with .reshape(n_participants, n_trials, -1) based purely on row order, so interleaved participant rows will silently mix subjects/trials and produce an incorrect likelihood. Consider either (a) enforcing contiguous blocks per participant (and optionally stable-sorting by participant_col + a trial index column if available) or (b) returning a sorted copy of the data and using that downstream.

…preserve trial sequence integrity

… RLSSM initialization

Copilot

Pull request overview

Copilot reviewed 8 out of 8 changed files in this pull request and generated 4 comments.

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Copilot · 2026-03-02T20:22:54Z

src/hssm/rl/rlssm.py

+        # All RLSSM parameters are treated as trialwise: the Op expects arrays of
+        # length n_total_trials for every parameter, and make_distribution.logp
+        # broadcasts scalar / (1,)-shaped tensors up to (n_obs,) accordingly.
+        params_is_trialwise = [
+            True for param_name in self.params if param_name != "p_outlier"
+        ]
+
+        extra_fields_data = (
+            None
+            if not self.extra_fields
+            else [deepcopy(self.data[field].values) for field in self.extra_fields]
+        )
+
+        assert self.list_params is not None, "list_params should be set"
+        # self.loglik was set to the pytensor Op built in __init__; cast to
+        # narrow the inherited union type so make_distribution's type-checker
+        # accepts it without a runtime penalty.
+        loglik_op = cast("Callable[..., Any] | Op", self.loglik)
+        return make_distribution(
+            rv=self.model_name,
+            loglik=loglik_op,
+            list_params=self.list_params,
+            bounds=self.bounds,
+            lapse=self.lapse,
+            extra_fields=extra_fields_data,
+            params_is_trialwise=params_is_trialwise,
+        )


params_is_trialwise is derived from self.params (excluding p_outlier), but it is passed alongside list_params=self.list_params. If self.list_params includes p_outlier (common in HSSMBase), this makes params_is_trialwise shorter and potentially misaligned with list_params, which can cause incorrect broadcasting or length-check failures in make_distribution. Build params_is_trialwise from self.list_params in the same order, marking p_outlier as non-trialwise.

tests/test_rlssm.py

.pre-commit-config.yaml

…ation

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src/hssm/rl/rlssm.py

…ary assertion for list_params

… for independent copies

…tion

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Copilot · 2026-03-03T15:24:22Z

src/hssm/rl/rlssm.py

+        # Build the differentiable pytensor Op from the annotated SSM function.
+        # This Op supersedes the loglik/loglik_kind workflow: it is passed as
+        # `loglik` to HSSMBase so Config.validate() is satisfied, and
+        # _make_model_distribution() uses it directly without any further wrapping.
+        #
+        # Pass copies of list_params / extra_fields so the closure inside
+        # make_rl_logp_func captures its own isolated list objects.  HSSMBase will
+        # later append "p_outlier" to self.list_params (which is the SAME list
+        # object as `list_params` above), and that mutation must NOT be visible to
+        # the Op's _validate_args_length check at sampling time.
+        loglik_op = make_rl_logp_op(
+            ssm_logp_func=rlssm_config.ssm_logp_func,
+            n_participants=n_participants,
+            n_trials=n_trials,
+            data_cols=list(data_cols),
+            list_params=list(list_params),
+            extra_fields=list(extra_fields),
+        )


RLSSM builds the Op exclusively from rlssm_config.ssm_logp_func (and its .computed metadata) but never uses rlssm_config.learning_process. Since learning_process is still a required RLSSMConfig field, this creates a confusing/fragile API where users can supply learning functions that are silently ignored (or diverge from ssm_logp_func.computed). Consider making learning_process optional/removing it from RLSSMConfig, or validating it matches (or populates) ssm_logp_func.computed so there is a single source of truth.

cpaniaguam · 2026-03-03T15:49:07Z

@krishnbera @AlexanderFengler @digicosmos86 Here is a first draft for the RLSSM class.

digicosmos86 · 2026-03-04T14:05:30Z

src/hssm/rl/rlssm.py

+    def __init__(
+        self,
+        data: pd.DataFrame,
+        rlssm_config: RLSSMConfig,


I think we should keep the two classes as similar to each other as possible, so I'd prefer model_config here

digicosmos86 · 2026-03-04T14:07:39Z

src/hssm/rl/rlssm.py

+        data: pd.DataFrame,
+        rlssm_config: RLSSMConfig,
+        participant_col: str = "participant_id",
+        include: list[dict[str, Any] | Any] | None = None,


@AlexanderFengler include is a legacy naming convention from HDDM. However, to me it's kind of confusing now. Should we deprecate this for something clearer? We can use an alias for now with a deprecation warning and completely remove it in a future release

What would you call it instead here?
We would want to make that change globally not just for this class I guess.

Either way, would do that as a separate PR.

digicosmos86 · 2026-03-04T14:09:32Z

src/hssm/rl/rlssm.py

+        **kwargs: Any,
+    ) -> None:
+        # Validate config (ensures ssm_logp_func is present, etc.)
+        rlssm_config.validate()


Should we initiate the parent class first?

digicosmos86 · 2026-03-04T14:10:24Z

src/hssm/rl/rlssm.py

+        # would scramble per-participant trial sequences and corrupt RL dynamics.
+        # Raise early so the user gets a clear message before model construction.
+        if missing_data is not False:
+            raise ValueError(


I think we are implementing it in the future. Maybe NotImplementedError for now?

digicosmos86 · 2026-03-04T14:13:10Z

src/hssm/rl/rlssm.py

+        # Build a ModelConfig so HSSMBase._build_model_config can apply the
+        # RLSSM-specific fields (response, list_params, choices, bounds, …).
+        # default_priors is an empty dict (no parameter-specific priors pre-set)
+        # so that the prior_settings="safe" mechanism in HSSMBase assigns
+        # sensible priors from bounds. Populating it with params_default scalar
+        # floats would fix every parameter as a constant, which is incorrect.
+        mc = ModelConfig(
+            response=(tuple(rlssm_config.response) if rlssm_config.response else None),
+            list_params=list_params,
+            choices=(tuple(rlssm_config.choices) if rlssm_config.choices else None),
+            default_priors={},
+            bounds=rlssm_config.bounds or {},
+            extra_fields=extra_fields if extra_fields else None,
+            backend="jax",  # RLSSM always uses the JAX backend
+        )


I wouldn't do this tbh. The purpose of inheritance is not to funnel sub-class functionalities into base-class functionalities. Rather subclass should expand base-class functionalities through overrides

…d simplify Op parameter handling

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Copilot · 2026-03-04T17:15:24Z

src/hssm/rl/utils.py

+    counts = data.groupby(participant_col).size()
+    if counts.nunique() != 1:
+        raise ValueError(
+            "Data must form balanced panels: all participants must have the "
+            f"same number of trials. Observed trial counts: {dict(counts)}"
+        )
+
+    return int(len(counts)), int(counts.iloc[0])


validate_balanced_panel() will raise an IndexError on empty input because counts is empty and counts.iloc[0] is accessed. Please add an explicit empty-data check (e.g., if data.empty: raise ValueError(...)) so callers (notably RLSSM.__init__) get a clear, consistent ValueError instead of an internal indexing error.

src/hssm/config.py

…oved configuration handling

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Copilot · 2026-03-09T18:52:32Z

src/hssm/config.py

    response: list[str] | None = field(default_factory=DEFAULT_SSM_OBSERVED_DATA.copy)
-    choices: tuple[int, ...] | None = DEFAULT_SSM_CHOICES
+    choices: list[int] | tuple[int, ...] | None = DEFAULT_SSM_CHOICES



choices was widened to list[int] | tuple[int, ...], but Config.update_choices() is still typed/docs as tuple[int, ...] and _build_model_config can pass a list[int]. To avoid inconsistent public typing (and future mypy confusion), consider updating update_choices (and any related docstrings/types) to accept list[int] | tuple[int, ...] and optionally normalize to a single internal representation (e.g., always store a tuple).

…nfig and DataValidatorMixin

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src/hssm/config.py

…tency

AlexanderFengler

small comments.

AlexanderFengler · 2026-03-26T22:55:09Z

src/hssm/rl/rlssm.py

+        data: pd.DataFrame,
+        rlssm_config: RLSSMConfig,
+        participant_col: str = "participant_id",
+        include: list[dict[str, Any] | Any] | None = None,


What would you call it instead here?
We would want to make that change globally not just for this class I guess.

Either way, would do that as a separate PR.

AlexanderFengler · 2026-03-27T00:22:43Z

src/hssm/rl/rlssm.py

+            )
+        if deadline is not False:
+            raise ValueError(
+                "RLSSM does not support `deadline` handling. "


@krishnbera do we actually have a solution for this?

AlexanderFengler · 2026-03-27T00:25:47Z

src/hssm/base.py

        """
        # Start with defaults
-        config = cls.config_class.from_defaults(model, loglik_kind)
+        # get_config_class is provided by Config/RLSSMConfig mixin through MRO


why does RLSSMConfig show up here in this file?

All this will be cleaned up after #936 and #931 get merged into their respective base branches.

tests/test_rlssm_config.py

AlexanderFengler · 2026-03-27T00:30:52Z

tests/test_rlssm_config.py

+            "decision_process": "ddm",
+            "learning_process": {},
+            "learning_process_loglik_kind": "blackbox",
+            "decision_process_loglik_kind": "analytical",


learning_process_loglik_kind not a valid concept.

src/hssm/config.py

…sistency

cpaniaguam added 4 commits March 2, 2026 12:33

Add ssm_logp_func to RLSSMConfig and update validation tests

20ddc4c

Add RLSSM model and utilities for reinforcement learning integration

d97dcee

Refactor RLSSM parameter handling and add custom prefix resolution fo…

a6a0238

…r RL parameters

Add tests for RLSSM class covering initialization, validation, and mo…

d880977

…del structure

cpaniaguam changed the base branch from main to cp-main-sb March 2, 2026 18:41

Refactor loglik handling in RLSSM to improve type safety with casting

bef8d6c

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cpaniaguam added 5 commits March 2, 2026 14:29

Add NaN value check for participant column in validate_balanced_panel…

3981ef6

… function

Add validation for ssm_logp_func in RLSSMConfig to ensure it is calla…

d84a800

…ble and has required attributes

Add exclude rules for ruff and mypy hooks to skip tests directory

15ad6e2

Add validation tests for ssm_logp_func in RLSSMConfig to ensure it is…

262ec07

… callable and properly annotated

Add tests for NaN participant_id and unannotated ssm_logp_func in RLSSM

381275a

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cpaniaguam added 2 commits March 2, 2026 15:13

Reject missing data and deadline handling in RLSSM initialization to …

0e9ba42

…preserve trial sequence integrity

Add tests to validate error handling for missing data and deadline in…

4f28c68

… RLSSM initialization

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cpaniaguam added 3 commits March 2, 2026 15:25

Refactor path handling for loading RLDM fixture dataset in tests

5e9f566

Add fixture to set floatX to float32 for module tests

67ac2ce

Ensure params_is_trialwise aligns with list_params in RLSSM initializ…

e1c05df

…ation

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cpaniaguam added 2 commits March 2, 2026 16:18

Clarify comments on default_priors in ModelConfig and remove unnecess…

564232b

…ary assertion for list_params

Update RLSSM to use to_numpy(copy=True) for extra_fields and add test…

bafc037

… for independent copies

Remove redundant exclude rule for ruff-format in pre-commit configura…

292d6f0

…tion

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cpaniaguam added 2 commits March 4, 2026 12:05

Add to_model_config method to RLSSMConfig for ModelConfig conversion

3b1aaf4

Refactor RLSSM to delegate ModelConfig construction to RLSSMConfig an…

0678c45

…d simplify Op parameter handling

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Integrate Config and RLSSMConfig into HSSM and RLSSM classes for impr…

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…oved configuration handling

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Update choices type from list to tuple for consistency in BaseModelCo…

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…nfig and DataValidatorMixin

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Update choices type from list to tuple in test_constructor for consis…

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Fix formatting of error messages in TestRLSSMConfigValidation for con…

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…sistency

-    return int(len(counts)), int(counts.iloc[0])
+    # Ensure that each participant's trials form a single contiguous block
+    # of rows of length n_trials. This is required because downstream code
+    # reshapes the data into (n_participants, n_trials, ...) based on row
+    # order, assuming no interleaving across participants.
+    n_trials = int(counts.iloc[0])
+    # Identify contiguous "blocks" of identical participant IDs.
+    blocks = data[participant_col].ne(data[participant_col].shift()).cumsum()
+    block_counts = data.groupby([participant_col, blocks]).size()
+    # Each participant must appear in exactly one block, and that block
+    # must have length n_trials.
+    blocks_per_participant = block_counts.groupby(level=0).size()
+    invalid_multi_blocks = blocks_per_participant[blocks_per_participant != 1]
+    invalid_block_sizes = block_counts[block_counts != n_trials]
+    if not invalid_multi_blocks.empty or not invalid_block_sizes.empty:
+        raise ValueError(
+            "Data must be ordered so that each participant's trials appear in "
+            "a single contiguous block of rows of length n_trials. "
+            "Participants with non-contiguous or incorrectly sized blocks "
+            f"were found. Consider sorting your data by '{participant_col}' "
+            "and, if available, by a trial index column before building the "
+            "RL likelihood."
+        )
+    return int(len(counts)), n_trials

+    # Ensure there are no missing participant IDs, since groupby will drop NaNs
+    # silently, which would make n_participants / n_trials incorrect.
+    if data[participant_col].isna().any():
+        raise ValueError(
+            f"Column '{participant_col}' contains missing values. "
+            "Please fill or remove rows with missing participant IDs before "
+            "calling validate_balanced_panel."
+        )

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Copilot AI Mar 4, 2026

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Copilot AI Mar 9, 2026

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cpaniaguam commented Mar 2, 2026 •

edited

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