deup 0.3.1

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.

Method credit: DEUP is due to Lahlou et al., 2023 (TMLR). This package is a maintained, benchmarked, scikit-learn-compatible implementation with time-series / cross-sectional finance support and aggregation-reliability diagnostics.

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)

Time-series / cross-sectional finance (flagship preset):

from deup.domains.finance import CrossSectionalDEUP

model = CrossSectionalDEUP(horizon=20).fit(panel_df)
pred, unc = model.predict(test_df, return_uncertainty=True)
health = model.health_report(test_df)

Install

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

Why this package?

The only public DEUP code was a stale research repo of notebooks — no maintained pip-installable package. Major UQ libraries (torch-uncertainty, uncertainty-toolbox, MAPIE) don't implement DEUP. deup fills that gap with leakage-correct OOF error construction and walk-forward / purged CV for time-series and finance.

Comparison (California housing, Spearman ρ vs realized squared error)

Method	ρ	Notes
DEUP	0.509	OOF error predictor
Ensemble disagreement	0.460	Bootstrap variance
Conformal residual	0.447	|y − ŷ| on cal split
Laplace (last-layer)	0.015	Not applicable to trees

Full results: Benchmarks.

Documentation

Topic	Link
Getting started	docs/getting-started
Five-axis guide	docs/concepts
Tutorials	tabular · finance · conformal · active learning
When is agg-g reliable?	reliability
Contributing	CONTRIBUTING.md

Status

v0.3.1 — full stack: estimators, conformal calibration, reliability diagnostics, domain presets, benchmarks, tutorials (CI-tested).

Citing

Cite this software (CITATION.cff) and Lahlou et al. (2023).

License

Apache-2.0. See LICENSE.