interpretml/DiCE v0.5

Rolling out DiCE for sklearn and regression models

on GitHub

• [Major] DiCE now supports sklearn models. Added three model-agnostic methods: randomized, genetic algorithm, and kd-tree
• [Major] Support for regression and multi-class problems
• [Major] Added local and global feature importance scores based on counterfactuals
• [Major] Better support for customizing counterfactuals through features_to_vary and permitted_range parameters for both continuous and categorical features
• [Refactor] ML Model and DiCE Explainer can use different feature transformations. Model's transformation can be provided as an input to the dice_ml.Model constructor. DiCE accepts inputs in the original data frame and does its transformations internally
• Enhanced tests for the library
• Deep learning libraries (tensorflow and pytorch) marked as optional dependencies
• New notebooks showing applications of DiCE in docs/source/notebooks/

A big thanks to @raam93, @soundarya98 and @gaugup for this release!