Interpret-community

- add an explanation adapter to integrate our explanations with other frameworks
- update interpret-community to interpret-core 0.2.6
- change explainers tabular explainer runs based on gpu flag
- fix model wrapper to handle a pytorch binary classification model that only outputs probabilities for positive class
- add test for old explanation dashboard and interpret dashboard
- fix nightly build breaking due to new scikit-learn package, which breaks model function serialization, by always serializing the model directly instead of the function if a model was passed to us

BREAKING CHANGE: function wrap_model now just returns the wrapped model

- Removed old ExplanationDashboard. The old namespace still exists but widget won't display anything and will print a warning now. Please use the ExplanationDashboard from the raiwidgets package instead.

Please install raiwidgets from pypi by running:

pip install --upgrade raiwidgets

The dashboard can be run with the same parameters in the new namespace:

from raiwidgets import ExplanationDashboard

For more information on the new widget please see: https://github.com/microsoft/responsible-ai-widgets

- Fixed raw aggregated explanation failing to compute when transformations passed to mimic explainer and include_local=False

- emergency hotfix to pin numba to less than 0.54.0 to fix shap failures
- update to check correct version of RAPIDS
- update GPU SHAP for kmeans sampling code from cuML
- fix explanation dashboard failing to run on dataset with boolean target labels

- fix aggregation logic for raw global importance values to be consistent with local importance values

- fix error when creating a raw explanation from an engineered explanation that does not have a DatasetsMixin

- Update interpret-core to 0.2.5
- Add predicted and predicted probabilities to raw explanations to allow model performance tab and other tabs to show information related to y_pred/y_pred_proba for raw explanations
- Update cuML version for GPUKernelExplainer
- Remove shap DenseData from supported input data
- Use scipy logit() function in mimic explainer to fix divide by zero error