Sparse Hypergraph Interaction Learning

Date/time: 2026-07-30 19:53 +03:00 Tool: Codex Model, if known: GPT-5.6 Extra High (xhigh, user-attested) Operation ID: shil-ijmlc-public-repo-sync-20260730-1953

Replication package for the diagnostic sparse-interaction support-selection manuscript.

Status: public replication package. The repository includes the initial experiments and the final stability-calibration, Dry Bean, and fixed-C artifacts used by the manuscript. It also provides pinned requirements, source and output hashes, per-seed completion records, citation metadata, licensing notices, and non-compute integrity checks.

Author

Özkan Canay is the sole and corresponding author. He is affiliated with the Department of Information Systems and Technologies, Faculty of Computer and Information Sciences, Sakarya University, Esentepe Campus, 54187 Serdivan, Sakarya, Türkiye. Email: canay@sakarya.edu.tr. ORCID: 0000-0001-7539-6001.

Claim Boundary

This package supports the narrowed diagnostic claim: SHIL is evaluated as a fixed-budget support-selection and reporting procedure for sparse pair/triple interaction regimes. It does not claim broad tabular state-of-the-art performance, general method superiority over direct sparse polynomial logistic regression, or superiority over budget-matched L1 top-k coefficient ranking.

Contents

• code/: reproduction, follow-up, scale-stress, Q1 numeric-update, and bounded-sensitivity scripts.
• replication_package/results/: frozen and Q1 follow-up result summaries.
• replication_package/calibrated_runs/: the hash-locked stability-calibration protocol, twelve-scenario synthetic artifacts, Dry Bean artifacts, fixed-C sensitivity artifacts, source/configuration files, and per-seed records.
• docs/: claim boundary and release boundary.
• data_sample/: no raw benchmark data are bundled; public datasets are reconstructed through loaders.
• requirements.txt: pinned environment from the verified local run.
• run_smoke.ps1: dependency, file-presence, and package-local evidence-schema check; it does not run an experiment.
• run_all.ps1: review-oriented wrapper around the same non-compute integrity checks.
• LICENSE and LICENSE-CONTENT.md: software and content license boundaries.
• CITATION.cff: repository citation metadata.

Quick Start

python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
python -m pip install -r requirements.txt
.\run_smoke.ps1

The smoke test verifies dependencies, required files, SHA-256 entries, calibrated run row counts, per-seed completion records, and selected evidence schemas. It does not regenerate evidence. The full calibrated experiments are intentionally separated from this check because they require long CPU runs.

Data Reconstruction

Synthetic datasets are generated by the scripts. Breast cancer and wine are loaded through scikit-learn. Diabetes OpenML 37 is fetched through OpenML by data id. Dry Bean is retrieved from UCI dataset 602 and checked against the recorded archive hash. Raw public benchmark data are not bundled in this repository.

Licensing

• Source code, execution scripts, and software-oriented files are available under the MIT License in LICENSE.
• Documentation and generated result, manifest, selected-edge, figure, and synthetic-example artifacts are available under CC BY 4.0 as specified in LICENSE-CONTENT.md.
• Third-party dataset and dependency rights remain with their respective providers. Raw public benchmark datasets are not redistributed.

Citation

Use the repository metadata in CITATION.cff and identify the commit or release used for an analysis.