# MissCP

Anonymous research code for missingness-aware conformal prediction.

This repository contains the implementation used for the MissCP experiments:
split conformal prediction, missingness-defined Mondrian calibration, model-swap
diagnostics, simulation sweeps, and clinical/non-clinical experiment runners.
The repository intentionally excludes raw datasets, generated outputs, local
experiment logs, paper drafts, and environment-specific paths.

## Installation

```bash
uv sync
```

Optional external baselines:

```bash
uv sync --extra external-baselines
```

This extra installs `conditionalconformal==0.0.5`.

Optional non-clinical ACS dependency:

```bash
uv sync --extra non-clinical
```

## Repository Layout

- `src/sepsis_mcp/`: implementation and experiment entry points.
- `tests/`: unit tests and smoke tests.
- `VERSION`: repository version marker.
- `pyproject.toml`: package metadata and dependencies.

## Data

No raw data are included. Public datasets should be downloaded separately from
their official sources and passed to commands through CLI arguments.

Common local layout examples:

```text
data/physionet2019/training/
data/gossis/
data/tableshift_cache/
data/home_credit/application_train.csv
```

## Example Commands

PhysioNet-style smoke run:

```bash
uv run python -m sepsis_mcp.cli run \
  --data-root data/physionet2019/training \
  --train-hospital A \
  --test-mode both \
  --model-type sklearn_gbdt \
  --train-patients 128 \
  --calibration-patients 64 \
  --test-patients 128 \
  --lookback-hours 6 \
  --horizon-hours 6 \
  --alpha 0.1 \
  --output-dir outputs/mvp
```

GOSSIS hospital-disjoint smoke run:

```bash
uv run python -m sepsis_mcp.gossis_experiment \
  --data-root data/gossis \
  --model-type xgboost \
  --random-state 0 \
  --output-dir outputs/gossis-smoke
```

Non-clinical validation smoke run:

```bash
uv run python -m sepsis_mcp.non_clinical_experiments \
  --dataset airquality \
  --seeds 2 \
  --output-dir outputs/non_clinical/airquality-smoke
```

## Tests

```bash
uv run pytest -q
```

## Anonymization Notes

This export is prepared for anonymous review:

- author metadata has been removed;
- raw data, generated outputs, manuscript files, and local logs are excluded;
- absolute local filesystem paths have been replaced with relative examples;
- no Hugging Face token or credential file is included.