bioscore 0.1.0

Biomedical scoring toolkit — reproducibility, data quality, and model readiness metrics for computational biology

bioscore

Biomedical scoring toolkit — reproducibility, data quality, and model readiness metrics for computational biology.

Install

pip install bioscore

Quick Start

from bioscore import reproducibility, data_quality, model_readiness

# Score a notebook for reproducibility
reproducibility("analysis.ipynb")
# → {"score": 0.65, "issues": ["missing seed", "no version pinning"], "level": "partial"}

# Assess dataset quality
data_quality("dataset.csv", domain="oncology")
# → {"completeness": 0.8, "consistency": 0.9, "overall": 0.85}

# Check ML model readiness for deployment
model_readiness("model.pkl")
# → {"score": 0.72, "ready": false, "gaps": ["no validation split", "no bias audit"]}

API

reproducibility(source: str) -> dict

Evaluates a notebook or script for reproducibility best practices.

Checks: random seed, package version pinning, data source documentation, output preservation.

Returns {"score": float, "issues": list[str], "level": str}.

data_quality(source: str, domain: str = "general") -> dict

Assesses a dataset for completeness, consistency, and domain-specific quality.

Returns {"completeness": float, "consistency": float, "overall": float}.

model_readiness(source: str) -> dict

Evaluates an ML model artifact for production readiness.

Returns {"score": float, "ready": bool, "gaps": list[str]}.

License

MIT © K-RnD-Lab