VCM: Variable Coverage Model

Overview

VCM is a framework for generating synthetic data with controllable coverage guarantees. It ensures that generated samples adequately represent target subgroups and distributions in the real data.

Repositoryhttps://github.com/SSTDV-Project/VCM
LicenseMIT

Installation

pip install vcm-model

Quick Start

import vcm
model = vcm.VCM(coverage_target=0.95, min_group_size=50, diversity_weight=0.3)
model.fit(real_data, epochs=100)
synthetic_data = model.generate(n_samples=1000, coverage='guaranteed')
coverage = vcm.metrics.coverage(real_data, synthetic_data)
print(f"Actual coverage: {coverage:.2%}")

Coverage Modes

ModeBehavior
guaranteedEnsures minimum coverage across all subgroups
balancedEqual weight to coverage and fidelity
fidelityOptimizes for sample quality

Dependencies

  • torch>=2.0
  • numpy>=1.21
  • scipy>=1.7
  • scikit-learn>=1.0

Citation

@software{vcm,
  title   = {VCM: Variable Coverage Model},
  author  = {SSTDV Project},
  year    = {2024},
  url     = {https://github.com/SSTDV-Project/VCM}
}

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