Pasteur

Stress-test clinical AI models before you trust them. Pasteur simulates the ways real clinical data goes wrong, then measures how much a model's predictions move:

• Blackout: a lab or feature goes missing, together with its companion columns
• Jitter: measurement noise
• Flipper: interpolation between patients with different labels, to find where a decision flips

100% local	Runs on your machine's CPU. No server, daemon, or admin rights.
No network, no telemetry	Makes no network calls at runtime. Data, models, and results never leave the machine.
Standard tooling	pip install pypasteur or cargo install pasteur-cli. BSD-3-Clause open source.

local parquet + ONNX models  →  Pasteur (CLI or Python)  →  local parquet + JSON metrics

Pasteur produces evidence about model behaviour under declared perturbations, not a pass/fail badge. It is a research and evaluation tool, not a medical device.

Reviewing Pasteur for your organisation? Start with Security and data handling or the one-page overview (PDF).

Install

pip install pypasteur       # Python: blackout and jitter simulators (CPython ≥ 3.12)
cargo install pasteur-cli   # CLI: simulate, evaluate, compare (Rust ≥ 1.95)

The PyPI package is pypasteur. The unrelated pasteur package on PyPI is a different project.

Quickstart

Start from a parquet with an integer id column and numeric model features (input schemas):

pasteur-cli simulate \
  --input cohort.parquet \
  --labels groups.parquet \
  --positive-group-id 1 \
  --id-col patient_id \
  --feature crp \
  --blackout-companion crp_measured \
  --output ./sim

pasteur-cli compare blackout \
  --sim-root ./sim \
  --labels groups.parquet \
  --positive-group-id 1 \
  --model models/logreg.onnx \
  --model models/gbm.onnx \
  --output results-blackout.json

simulate writes sim/clean/, sim/blackout/, sim/jitter/, and (with --labels) sim/flipper/. compare scores each ONNX model on the clean and perturbed rows and writes the metrics as JSON. See Reading the results and the model-selection walkthrough.

From Python:

import polars as pl
import pypasteur

df = pl.read_parquet("cohort.parquet")
blackout = pypasteur.BlackoutSimulator(
    "crp", rate=0.2, companions=["crp_measured"], random_state=42
)
blackout.fit(df)
missing = blackout.transform(df)

Docs

• Full documentation: docs.krv.ai/pasteur
• Security and data handling: data flow, network behaviour, offline install, PHI in outputs
• Walkthrough: choosing between models
• Reading the results: metric definitions and caveats
• CLI reference

Workspace

Crate	Role
core (pasteur-core)	Simulators, evaluators, schemas
cli (pasteur-cli)	Local CLI: simulate, evaluate, compare, card, cache
hf (pasteur-hf)	Simulation bundle layout + Hugging Face dataset cards
model (pasteur-model)	ONNX model loading and scoring
bindings/python-bindings (pypasteur)	PyO3 Python bindings

pasteur-model downloads ONNX Runtime at build time (the ort crate's download-binaries). At runtime, pasteur-cli never opens a network connection.

Development

cargo fmt --all
cargo clippy --workspace -- -D warnings
cargo test --workspace --exclude pypasteur-bindings

See CONTRIBUTING.md. Pushing a v* tag runs .github/workflows/release.yml.

Automation: the rules for agents are in AGENTS.md, and the publishing workflow is in the agent skills under .agents/skills/. Never publish bundles made from patient data.