Shap

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.

This release includes:
- Support for sklearn isolation forest courtesy of JiechengZhao
- New check_additivity tests to ensure no errors in DeepExplainer and TreeExplainer
- Fix 861, 860
- Fix missing readme example html file
- Support for spark decision tree regressor courtesy of QuentinAmbard
- Better safe isinstance checking courtesy of parsatorb
- Fix eager execution in TF < 2 courtesy of bottydim

This release contains several new features and bug fixes:
- GradientExplainer now supports TensorFlow 2.0.
- We now do a lazy load of the plotting dependencies, which means a pip install no longer needs to also pull in matplotlib, skimage, and ipython. This should make installs much lighter, especially those that don't need plotting :)
- Added a new BruteForceExplainer for easy testing and comparison on small problems.
- Added a new partial_dependence_plot function. This function will be used to illustrate the close connections between partial dependence plots and SHAP values in future example notebooks.
- Handle the multiclass case with no intercept in LinearExplainer courtesy of gabrieltseng
- Some extras_require options during the pip install courtesy of AbdealiJK
- Other small bug fixes and updates

This release is primarily to remove a dependency on dill that was not in setup.py. It also includes:

- A typo fix in force.py courtesy of jonlwowski012
- Test code cleanup courtesy of jorgecarleitao

- Fix floating point rounding mismatches in recent sklearn versions of tree models
- An update to allow easier loading of custom tree ensemble models by TreeExplainer.
- `decision_plot` documentation updates courtesy of floidgilbert