metrics-adjuster 1.0.2

Adjusted group-aware clinical prediction metrics with Python API and CLI.

metrics-adjuster

metrics-adjuster computes conventional and adjusted group-aware metrics for binary prediction models. It provides a typed Python API, a command-line interface, deterministic synthetic demos, and self-contained HTML reports.

Installation

Install from PyPI (Python 3.11+):

python -m pip install metrics-adjuster

That one command installs both the metrics-adjuster CLI and the Python API.

Other setups:

• CLI only, without a project environment: pipx install metrics-adjuster
• uv project: uv add metrics-adjuster
• conda or mamba env: run the pip command above inside your activated environment
• pinned release: python -m pip install "metrics-adjuster==1.0.2"
• source checkout: see Contributing

Quick Example

metrics-adjuster demo --output-dir demo_outputs --report

For caller-provided CSV or Parquet data:

metrics-adjuster run \
  --input cohort.csv \
  --output-dir adjusted_metric_outputs \
  --group-col group \
  --ref-group ref \
  --response-col outcome \
  --risk-col risk \
  --metrics aTPR,aPPV,aNB,aHR \
  --report

The CLI writes one CSV per adjusted metric and, when --report is enabled, a self-contained report.html.

Python API

from metrics_adjuster import ColumnSpec, MetricConfig, adjusted_metrics
from metrics_adjuster.synthetic import generate_synthetic_metrics_data

frame = generate_synthetic_metrics_data(n=600, seed=2026)
config = MetricConfig(
  columns=ColumnSpec(group="group", response="outcome", risk="risk"),
  ref_group="ref",
  random_state=2026,
)

result = adjusted_metrics(frame, config)
print(result.metrics["aTPR"])

Documentation

• Quickstart
• API manual
• CLI manual
• Contributing

License

metrics-adjuster is distributed under the GNU General Public License v3.0.