deup 0.1.1

Direct Epistemic Uncertainty Prediction (DEUP) for any scikit-learn model, with first-class time-series support.

deup

Direct Epistemic Uncertainty Prediction (DEUP) for any scikit-learn model — with first-class, leakage-correct time-series support.

DEUP estimates epistemic uncertainty by training a secondary error predictor on your model's out-of-sample errors — no ensembles, no Bayesian retraining, works with the model you already use. The method is due to Lahlou et al., 2023 (TMLR); this package is a maintained, installable, scikit-learn-compatible implementation of it.

Repository: https://github.com/ursinasanderink/deup · Docs: https://ursinasanderink.github.io/deup/

Quickstart

from sklearn.ensemble import RandomForestRegressor
from deup import DEUPRegressor

model = DEUPRegressor(base_model=RandomForestRegressor())
model.fit(X_train, y_train)

pred, unc = model.predict(X_test, return_uncertainty=True)

For time-series / cross-sectional data, pass a leakage-safe splitter:

from deup.splitters import PurgedWalkForward

model = DEUPRegressor(base_model=my_model, cv=PurgedWalkForward(embargo=5))

Install

pip install deup            # core (numpy + scikit-learn)
pip install "deup[gbm]"     # + LightGBM error predictor
pip install "deup[docs]"    # + MkDocs site locally

Why this exists

The only public DEUP code is a 3-year-old research repo of notebooks; no maintained pip-installable package existed until now. Major UQ libraries (torch-uncertainty, uncertainty-toolbox, MAPIE) don't implement DEUP. deup fills that gap with correct out-of-fold error construction for time-series / cross-sectional data.

On California housing (v0.1 benchmark), DEUP uncertainty ranks test errors better than ensemble disagreement or a conformal residual baseline — see BENCHMARKS.md.

Status / roadmap

v0.1 (released): DEUPRegressor, OOF collector, splitters, full loss registry (squared / Brier / pinball / rank), target transforms (log / asinh), benchmark, docs.

v0.2: DEUPClassifier / DEUPRanker, conformal intervals, aleatoric decomposition, density/GP features, aggregation-reliability diagnostics.

Citing

If you use deup, please cite both this software (see CITATION.cff) and the original DEUP paper (Lahlou et al., 2023).

License

Apache-2.0. See LICENSE.