Domain presets

The core library is domain-agnostic; these modules are thin presets that wire the right splitter, features, and diagnostics for common workflows. They do not duplicate OOF collection or error-estimator logic — see ARCHITECTURE.md.

Cross-sectional finance (flagship)

import pandas as pd
from deup.domains.finance import CrossSectionalDEUP

# long-format panel: one row per (date, asset)
panel = pd.read_parquet("signals.parquet")  # columns: date, score, vol_20d, ...

model = CrossSectionalDEUP(horizon=20, cv=5, embargo=1).fit(panel)
model.calibrate(cal_panel, alpha=0.1)

pred, unc = model.predict(test_panel, return_uncertainty=True)
health = model.health_report(test_panel)   # per-date context gating
health.gate                                # bool per date: trust / trade?

Defaults wired in:

Setting	Value
Estimator	:class:~deup.estimators.DEUPRanker
CV	:class:~deup.splitters.PurgedWalkForward + embargo
Rank geometry	residualization ON
g-features	vol / breadth / regime preset columns when present
Context health	:class:~deup.diagnostics.HealthIndex

Requires pip install "deup[finance]" (pandas).

Pre-computed walk-forward residuals

If your primary model already produced walk-forward scores and rank losses:

from deup.domains.finance_walkforward import walkforward_g_on_enriched

preds, diag = walkforward_g_on_enriched(enriched_df, horizons=[20])

Generic tabular

from deup.domains.tabular import TabularDEUP

model = TabularDEUP(task="regression", cv=5).fit(X, y)
unc = model.predict_epistemic(X_test)

Wires KFold + raw X + Mahalanobis density features for g.

Gradient-boosting backends

Use backend to wire LightGBM, XGBoost, or CatBoost as both the base model f and the error predictor g (same five-axis defaults):

# pip install "deup[gbm]"      — LightGBM
# pip install "deup[xgb]"       — XGBoost
# pip install "deup[catboost]"  — CatBoost
# pip install "deup[gbm-all]"   — all three

model = TabularDEUP(backend="xgb", cv=5, random_state=0).fit(X, y)
unc = model.predict_epistemic(X_test)

backend	Extra	Base f + error g
"sklearn"	core	HistGradientBoosting* (default)
"lgbm"	deup[gbm]	LightGBM
"xgb"	deup[xgb]	XGBoost
"catboost"	deup[catboost]	CatBoost

You can still pass custom base_model / error_model for full control.

Vision / OOD classification

from deup.domains.vision import VisionDEUP

model = VisionDEUP(cv=5).fit(images, labels)   # tensors OK — auto-flattened
unc = model.predict_epistemic(images)

Wires embedding → density + variance features for g (CIFAR-style high-N path). Pass a custom embedding= transformer or callable for CNN embeddings.