Explainerdashboard

Version 0.4.0: upgrade bootstrap5, drop python 3.6 and 3.7 support and improved pipeline support
- Upgrades the dashboard to `bootstrap5` and `dash-bootstrap-components` `v1` (which is also based on bootstrap5), this
may break older custom dashboards that included bootstrap5 components from `dash-bootstrap-components<1`
- Support terminated for python `3.6` and `3.7` as the latest version of `scikit-learn` (1.1) dropped support as well
and explainerdashboard depends on the improved pipeline feature naming in `scikit-learn>=1.1`

New Features
- Better support for large datasets through dynamic server-side index dropdown option selection. This means that not all indexes have to be stored client side in the browser, but
get rather automatically updated as you start typing. This should help especially with large datasets with large number of indexes.
This new server-side dynamic index dropdowns get activated if the number of rows > `max_idxs_in_dropdown` (defaults to 1000).
- Both sklearn and imblearn Pipelines are now supported with automated feature names generated, as long as all the transformers have a `.get_feature_names_out()` method
- Adds `shap_kwargs` parameter to the explainers that allow you to pass additional kwargs to the shap values generating call, e.g. `shap_kwargs=dict(check_addivity=False)`
- Can now specify absolute path with `explainerfile_absolute_path` when dumping `dashboard.yaml` with `db.to_yaml(...)`

Bug Fixes
- Suppresses warnings when extracting final model from pipeline that was not fitted on a dataframe.
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Improvements
- No longer limiting werkzeug version due to upstream bug fixes of `dash` and `jupyter-dash`
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Other Changes
- Some dropdowns now better aligned.
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New Features
- Adds support for sklearn Pipelines that add new features (such as those including OneHotEncoder) as long as they support the new get_features_out() method. Not all estimators and transformers have this method implemented yet, but if all estimators in your pipeline do, then explainerdashboard will extract the final dataframe and the model from your pipelines. For now this does result in a lot of "this model was fitted on a numpy array but you provided a dataframe" warnings.

Bug Fixes
- Fixes a bug with sorting pdp features
- Pins werkzeug<=2.0.3 due to some new features that broke JupyterDash
- Changes use of pd.append that will be deprecated soon and is currently generated warnings.

Breaking Changes
- downgrades dash-bootstrap-components to <1 due to a long list of breaking changes in dbc v1
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New Features
- Export your ExplainerHub to static html with `hub.to_html()` and `hub.save_html()` methods
- Export your ExplainerHub to a zip file with static html exports with `to_zip()` method
- Manually add pre-calculated shap values with `explainer.set_shap_values()`
- Manually add pre-calculated shap interaction values with `explainer.set_shap_interaction_values()`

Bug Fixes
- Fixed bug with What if tab components static html export (missing `</div>`)
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Bumps dash requirement to 1.20 which introduced the new dcc.Download component used to download the static html from the dashboard.