benchmark-reliability 0.1.4

Benchmark Reliability Framework (BRF) - dataset-level reliability auditing for predictive benchmarks

benchmark-reliability

A Python package for computing the Benchmark Reliability Framework (BRF): a four-dimension audit protocol that evaluates whether a predictive dataset is structurally reliable before model development.

Installation

pip install benchmark-reliability

Requires Python 3.8+ with numpy, scikit-learn, and matplotlib.

Quick Start

import numpy as np
from brf import BRFAnalyzer

# Your data
X = np.random.randn(200, 10)
y = np.random.randn(200)
groups = np.random.choice(["A", "B", "C"], 200)

# Run the audit
analyzer = BRFAnalyzer(n_splits=30, n_permutations=200).fit(X, y, groups=groups)

# Results
print(analyzer.brf_vector)
# {'B': 0.123, 'I': 0.045, 'N': 0.97, 'M': 0.82,
#  'S': 0.925, 'E': 0.943, 'class': 'Reliable'}

BRF Dimensions

Dimension	Name	Meaning
B	Baseline Gain	Model improvement over mean predictor
I	Instability	Sensitivity to train/test split choice
N	Null Separability	Signal distinguishability from noise
M	Metadata Sufficiency	Group structure completeness

The embedding coordinates S = N - I (Signal Identifiability) and E = B + M (Epistemic Completeness) classify datasets into Reliable, Fragile, or Void.

Visualization

from brf.phase import plot_phase_diagram

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

Export

from brf.report import export_json, export_latex

export_json(analyzer.brf_vector, "results.json")
latex_table = export_latex(analyzer.brf_vector)

Citation

If you use this package, please cite the BehaviorAudit paper:

BehaviorAudit: a four-dimension pre-modeling audit protocol
for educational prediction benchmarks. Scientific Reports (under review).

License

MIT

Links

• GitHub: https://github.com/zhanglizhuo/BenchmarkReliability
• PyPI: https://pypi.org/project/benchmark-reliability/