benchmark-reliability 0.1.0

Benchmark Reliability Framework (BRF) =?unknown-8bit?b?4oCU?= dataset-level reliability auditing for predictive benchmarks

BenchmarkReliability ��� BRF Python Package

Target

Provide a standardized, pip-installable Python package that computes the Benchmark Reliability Framework (BRF) for any predictive dataset, enabling researchers to run the four-dimension audit protocol with a single API call.

Method

The package wraps the core logic from the BehaviorAudit project into a sklearn-style API:

from brf import BRFAnalyzer
from brf.phase import plot_phase_diagram
from brf.report import export_json

analyzer = BRFAnalyzer(n_splits=30, n_permutations=200).fit(X, y, groups=groups)
print(analyzer.brf_vector)   # (B, I, N, M) ��� (S, E) ��� class

# Visualization
plot_phase_diagram(
    [analyzer.S], [analyzer.E],
    labels=[analyzer.class_],
    classes=[analyzer.class_],
)

# Export
export_json(analyzer.brf_vector, "results.json")

Package Structure

brf/
��������� __init__.py
��������� analyzer.py          ��� BRFAnalyzer main class
��������� metrics/
���   ��������� baseline_gap.py  ��� B
���   ��������� instability.py   ��� I
���   ��������� null_test.py     ��� N (permutation test)
���   ��������� metadata.py      ��� M
��������� phase/
���   ��������� embedding.py     ��� S = N - I, E = B + M
���   ��������� classifier.py    ��� Reliable / Fragile / Void
���   ��������� visualization.py ��� phase diagram, clustering plot
��������� report/
���   ��������� json_export.py
���   ��������� latex_export.py

Steps

Phase 1: Package skeleton (1-2 weeks)

• Initialize Python project with pyproject.toml
• Implement BRFAnalyzer main class with fit/predict interface
• Port compute_b, compute_i, compute_n, compute_m from BehaviorAudit
• Write unit tests for each metric

Phase 2: Phase embedding + classification (1 week)

• Implement compute_phase(S, E) and classify_dataset(S, E)
• Build phase diagram visualization (matplotlib)
• Test on all 7 datasets from BehaviorAudit; verify BRF output matches SR paper results

Phase 3: Documentation + distribution (1-2 weeks)

• Write README with quick-start tutorial and API docs
• Publish to TestPyPI ��� PyPI
• Set up ReadTheDocs for auto-generated documentation
• Add GitHub Actions CI (test on Python 3.9���3.12)

Phase 4: HuggingFace Hub integration (optional, 1 week)

• Add HF dataset loading wrapper
• Allow brf.fit(dataset_id="OULAD") shorthand

Dependencies

• numpy>=1.21
• scikit-learn>=1.0
• matplotlib>=3.5
• No deep learning dependencies required

Relationship to Sister Repos

• BehaviorAudit/: source of the audit logic; this package refactors and generalizes it
• LLMScoringAudit/: first applied use case (MM-TBA �� multiple LLMs)
• BenchmarkPhase/: large-scale application (30 datasets BRF leaderboard)
• llm-annotation/: cited for complementary MLLM pseudo-label reliability findings

Target Journal

• Journal of Open Source Software (JOSS) ��� tool paper, lightweight submission
• Followed by application papers in C&E / BJET

Timeline

• Phase 1���2: 3 weeks
• Phase 3: 2 weeks
• Phase 4: optional
• JOSS submission: after Phase 3