Shap

- Downgrade numpy API usage to support older versions.

- Fixes a byte-alignment issue on Windows when loading XGBoost models.
- Now matches tree_limit use in XGBoost models courtesy of HughChen
- Fix an issue with the expected_value of transformed model outputs in TreeExplainer

- Add support for rank-based feature selection in `KernelExplainer`.
- Depreciate `l1_reg="auto"` in `KernelExplainer` in favor of eventually defaulting to `l1_reg="num_features(10)"`
- New color scales based on the Lch color space.
- Better auto-color choices for multi-class summary plots.
- Better plotting of NaN values in dependence_plots
- Updates for Pytorch 1.0 courtesy of gabrieltseng
- Fix the sklearn DecisionTreeClassifier handling to correctly normalize to a probability output
- Enable multi-output model support for `TreeExplainer` when `feature_dependence="independent"`
- Correctly load the objective of LightGBM models for use in explaining the model loss.
- Fix numerical precision mismatch with sklearn models.
- Fix numerical precision mismatch with XGBoost models by now directly loading from memory instead of JSON.

- 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.