Shap

This release contains more support for the new API, many bug fixes, and preliminary model agnostic text/image explainer support (still beta). Specific contributions include:

- Fix Sampling explainer sample counting issue courtesy of tcbegley
- Add multi-bar plotting support.
- Preliminary support for cohorts.
- Fixed an import error courtesy of suragnair
- Fix Tree explainer issues with isolation forests with max_features < 1 courtesy of zhanjiezhu
- Huge documentation cleanup and update courtesy of lrjball
- Typo fix courtesy of anusham1990
- Added a documentation notebook for the Exact explainer.
- Text and Image explainers courtesy of anusham1990 and Ryan Serrao
- Bug fix for shap.utils.hclust
- Initial support for InterpretML EBM models.
- Added column grouping functionality to Explainer objects.
- Fix for loop index bug in Deep explainer for PyTorch courtesy of quentinRaq
- Initial text to text visualization concepts courtesy of vivekchettiar
- Color conversion warning fix courtesy of wangjoshuah
- Fix invertibility issues in Kernel explainer with the pseudoinverse courtesy of PrimozGodec
- New benchmark code courtesy of maggiewu19 and vivekchettiar
- Other small bug fixes and enhancements.

This version contains a significant refactoring of the SHAP code base into a new (cleaner) API. Full backwards compatibility should be retained, but most things are now available in locations with the new API. Note that this API is still in a beta form, so refrain from depending on it for production code until the next release. Highlights include:
- A new shap.Explainer object that auto-chooses the explainer based on the given model and masking dataset.
- A new shap.Explanation object that allows for parallel slicing of data, SHAP values, base values (expected values), and other explanation-specific elements.
- A new shap.maskers.* module that separates the various ways to mask (i.e. perturb/hide) features from the algorithms themselves.
- A new shap.explainers.Partition explainer that can explain any text or image models very quickly.
- A new shap.maskers.Partition masker that ensures tightly grouped features are perturbed in unison, so preventing "unrealistic" model inputs from inappropriately influencing the model prediction. It also allows for the exact quadratic time computation of SHAP values for the 'structured games' (with coalitions structured according to a hierarchical clustering).
- A new shap.plots.* module with revamped plot types that all support the new API. Plots are now named more directly, so `summary_plot` (default) becomes `beeswarm`, and `dependent_plot` becomes `scatter`. Not all the plots have been ported over to the new API, but most have.
- A new notebooks/plots/* directory given examples of how to use the new plotting functions.
- A new shap.plots.bar function to directly create bar plots and also display hierarchical clustering structures to group redundant features together, and show the structure used by a Partition explainer (that relied on Owen values, which are an extension of Shapley values).
- Equally check fixes courtesy of jameslamb
- Sparse kmeans support courtesy of PrimozGodec
- Pytorch bug fixes courtesy of rightx2
- NPM JS code clean up courtesy of SachinVarghese
- Fix logit force plot bug courtesy of ehuijzer
- Decision plot documentation updates courtesy of floidgilbert
- sklearn GBM fix courtesy of ChemEngDataSci
- XGBoost 1.1 fix courtesy of lrjball
- Make SHAP spark serializable courtesy of QuentinAmbard
- Custom summary plot color maps courtesy of nasir-bhanpuri
- Support string inputs for KernelSHAP courtesy of YotamElor
- Doc fixes courtesy of imatiach-msft
- Support for GPBoost courtesy of fabsig
- Import bug fix courtesy of gracecarrillo and aokeson

This release includes:

- Better support for TensorFlow 2 (thanks imatiach-msft)
- Support for NGBoost models in TreeExplainer (thanks zhiruiwang)
- TreeExplainer support for the new sklearn.ensemble.HistGradientBoosting model.
- New improved versions of PartitionExplainer for images and text.
- IBM zOS compatibility courtesy of DorianCzichotzki.
- Support for XGBoost 1.0
- Many bug fixes courtesy of Ivan, Christian Paul, RandallJEllis, and ibuda.

This release includes:
- Many small bug fixes.
- Better matplotlib text alignment during rotation courtesy of koomie
- Cleaned up the C++ transformer code to allow easier PRs.
- Fixed a too tight check_additivity tolerance in TreeExplainer 950
- Updated the LinearExplainer API to match TreeExplainer
- Allow custom class ordering in a summary_plot courtesy of SimonStreicher

This release contains various bug fixes and new features including:

- Added PySpark support for TreeExplainer courtesy of QuentinAmbard
- A new type of plot that is an alternative to the force_plot, a `waterfall_plot`
- A new PermutationExplainer that is an alternative to KernelExplainer and SamplingExplainer.
- Added `return_variances` to GradientExplainer for PyTorch courtesy of s6juncheng
- Now we use exceptions rather than assertions in TreeExplainer courtesy of ssaamm
- Fixed image_plot transpose issue courtesy of Jimbotsai
- Fix color bar axis attachment issue courtesy of Lasse Valentini Jensen
- Fix tensor attachment issue in PyTorch courtesy of gabrieltseng
- Fix color clipping ranges in summary_pot courtesy of joelostblom
- Address sklearn 0.22 API changes courtesy of lemon-yellow
- Ensure matplotlib is optional courtesy of imatiach-msft

This release is just intended to push better auto-deploy bundles out of travis and appveyor.