Shap

- Better hierarchal clustering orderings that now rotate subtrees to give more continuity.
- Work around XGBoost JSON issue.
- Account for NaNs when doing auto interaction detection.
- PyTorch fixes.
- Updated LinearExplainer.

- Complete refactor of TreeExplainer to support deeper C++ integration
- The ability to explain transformed outputs of tree models in TreeExplainer, including the loss. In collaboration with HughChen
- Allow for a dynamic reference value in DeepExplainer courtesy of AvantiShri
- Add `x_jitter` option for categorical dependence plots courtesy of ihopethiswillfi
- Added support for GradientBoostingRegressor with quantile loss courtesy of dmilad
- Better plotting support for NaN values
- Fixes several bugs.

- Allows ordering_keys to be given to force_plot courtesy of JasonTam
- Fixes sparse nonzero background issue with KernelExplainer courtesy of imatiach-msft
- Fix to support tf.concat in DeepExplainer.

Fixes a problem where tree_shap.h was not included in the pip bundle.

- Support for PyTorch in GradientExplainer and preliminary support for PyTorch in DeepExplainer courtesy of gabrieltseng.
- A matplotlib version of the single sample force_plot courtesy of jverre.
- Support functional Keras models in GradientExplainer.
- KernelExplainer speed improvements.
- Various performance improvements and bug fixes.

New improvements include: Faster KernelExplainer execution for sparse inputs. Support for sklearn gradient boosting classifiers. DeepExplainer extended to support very deep models.