Extract estimator batching helpers from model base

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Move estimator grouping, input stacking, categorical masks, and output splitting from base.py into the private sdm/models/_batching.py module. Keep model execution, callbacks, and gradient handling in base.py.

Extract table-layout comparison into a small helper and skip compatibility checks when estimator_batch_size=1. Keep the existing slice-based grouping, prediction offsets, and cache format.

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📝 Summary

Summary by CodeRabbit

• Refactor
  • Prediction now processes compatible estimators together when their data layouts and class values match, subject to configured batch-size limits. Estimators with related tables or incompatible layouts are processed separately, and results retain each estimator’s original class-column order. Single-query predictions avoid unnecessary stacking. These changes reorganize internal processing without adding user-facing functionality.

Walkthrough

The change moves estimator batching helpers from sdm/models/base.py to sdm/models/_batching.py. Estimator execution uses the extracted helpers for grouping, input stacking, categorical masks, class-value collection, and output unstacking.

Changes

Estimator batching

Layer / File(s)	Summary
Batch compatibility and planner wiring sdm/models/_batching.py, sdm/models/base.py	The new module defines table-layout checks and groups consecutive estimators by compatible layouts and class values, subject to related-table and batch-size rules. sdm.models.base imports the helpers and removes their former local implementations.
Stacking and categorical masks sdm/models/_batching.py	The module stacks tables, contexts, and queries. It also creates categorical masks for single-member and batched inputs.
Estimator execution and output alignment sdm/models/base.py, sdm/models/_batching.py	Prediction paths call _run_estimators. The runner uses a single context and query directly when there is one query. The helpers collect class values when applicable and restore each member’s class-column order when unstacking outputs.

Priority: ⬇️ Low

Estimated code review effort: 3 (Moderate) | ~20 minutes

Merge Risk: ⚪ Minimal · up to bcba1

This refactor moves estimator batching helpers into a private module without a visible behavior change. No merge-blocking issues were found in the supplied changes.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 40.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 15 functions across 3 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and concisely describes the main change: extracting estimator batching helpers from the model base module.
Description check	✅ Passed	The description explains the helper extraction and identifies behavior and compatibility details relevant to the changeset.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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Inline comments:
Review comments at @sdm/models/base.py:
- Line 422: Update the state-restoration path that populates the cache so older
saved models without member_ids reconstruct consecutive IDs from each cached
batch’s x_schemas length before predict() accesses cache["member_ids"]. Preserve
existing member_ids when present.

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