0.0.9

[0.0.9] - 2026-07-13

Added

• Arbitrary loss functions: importance can now be defined through any per-sample loss instead of only the squared-error (L2) residual difference. New fdfi/losses.py registry provides regression losses (squared_error/l2, absolute_error/l1, huber, pinball) and binary-classification losses (log_loss/bce, brier, zero_one), plus resolve_loss()/available_losses(). Custom callables loss(y_true, y_pred) are also accepted.
• loss argument on OTExplainer, EOTExplainer, FlowExplainer, and Crossfitting (default squared error → unchanged behaviour). Passing true labels y at call time uses the loss-difference (DFI) form; when y is omitted a label-free form is used that references the model's own prediction — the prediction shift for regression losses and a Bregman divergence (e.g. KL for log-loss) for proper scoring rules.
• method='cpi'|'scpi' now available on OTExplainer and EOTExplainer (previously only FlowExplainer), selecting the averaging order for the counterfactual prediction (CPI averages the prediction before the loss; SCPI averages the per-sample loss).
• New tests: tests/test_losses.py (registry/built-ins) and loss-integration tests in tests/test_explainers.py (L2 parity, regression/classification losses, CPI/SCPI, guards, cross-fitting).

Changed

• FlowExplainer SCPI now follows the documented definition E_b[L(Y, f(X̃_b))] (for squared error, equal to CPI plus the prediction variance) rather than the raw prediction variance, making SCPI consistent across all explainers.
• Updated docs/user_guide/concepts.rst, docs/user_guide/choosing_explainer.rst, and docs/api/explainers.rst to document loss selection and the generalized CPI/SCPI formulas.