Roadmap#

Current development line — 0.1.1.dev1#

  • Delegate smooth sklearn model support and analytic input Jacobians to skgrad.

  • Recognize affine regression and binary or multiclass score-classification models through skgrad’s unified support API.

  • Preserve a constant-Jacobian fast path for exact affine attribution.

  • Provide an opt-in, batched finite-difference fallback for other smooth sklearn estimators.

  • Support weighted baseline distributions and CBaseline Background objects uniformly across all backends.

  • Attribute multiclass classifiers as one centered decision-score vector with K - 1 effective dimensions and provide derived pairwise contrasts.

  • Expose TreeIGNumeric as an explicit approximate fallback for CatBoost and recognized piecewise-constant tree models, including weighted backgrounds and centered multiclass raw scores.

  • Explain probability-only sklearn tree classifiers as binary log odds or centered multiclass log scores, with an explicit zero-probability floor.

  • Document the complete model inventory, output semantics, baseline behavior, numerical diagnostics, and SHAP plotting adapter.

  • Distinguish vector class scores from explicitly declared multi-output regression for generic automatic-gradient models.

  • Add native TensorFlow automatic gradients, direct TensorFlow-backed Keras models, and backend-native Keras 3 dispatch for JAX and PyTorch.

JAX backend#

Implemented optional JAX support for differentiable prediction functions with:

  • parameters supplied explicitly or captured in a closure;

  • scalar, multi-output regression, or centered multiclass score output per sample;

  • native JAX automatic gradients;

  • Gauss–Legendre path integration;

  • shared baseline distributions;

  • dtype and 64-bit-mode diagnostics; and

  • completeness, cross-framework, packaging, and optional-dependency tests.

JAX libraries such as Flax, Equinox, NNX, and Haiku are supported through the lightweight public JaxModel adapter rather than separate explainer classes.

TensorFlow and Keras#

Implemented optional TensorFlow support with native GradientTape derivatives, quadrature nodes batched into each model pass, shared weighted backgrounds, scalar and vector outputs, and an explicit TensorFlowModel adapter for arbitrary prediction functions. TensorFlow-backed Keras models work directly. Keras 3 models configured for JAX or PyTorch reuse those native UnifiedIG backends instead of introducing a Keras-specific differentiation layer.

Before the next published release#

  • Verify LossExplainer squared-error and score-based log-loss parity across analytic sklearn, exact TreeIG, PyTorch, JAX, TensorFlow/Keras, and explicit finite-difference backends.

  • Run source-tree and installed-wheel tests for all optional backends.

  • Review the README, changelog, error messages, and dependency matrix against the packaged artifacts.

Deferred work#

  • Multiple-input and structured-output deep-learning models.

  • Exact structural CatBoost, probability-averaging forest, and sklearn histogram-gradient-boosting support.

  • Exact piecewise-linear IG for ReLU networks by detecting activation-region transitions along the baseline path.

  • Further batching and memory optimization for large smooth models.

  • A public third-party backend registry, if model coverage eventually makes one useful.