Explainerdashboard

Breaking Changes
- split explainerdashboard.yaml into a explainer.yaml and dashboard.yaml
- generate with explainer.to_yaml("explainer.yaml") and db.to_yaml("dashboard.yaml")


- Changed UI of the explainerdashboard CLI to reflect this
- This change makes it possible to have automatic rebuilds and redeploys
when an modelfile, datafile or configuration file changes. See deployment documentation.

New Features
- Load an ExplainerDashboard from a configuration file with the classmethod .from_config()
e.g. : `ExplainerDashboard.from_config("dashboard.yaml")`

small bug fix in dashboard.to_yaml

New Features
- explainer.dump() to store explainer, explainer.from_file() to load
explainer from file
- Explainer.to_yaml() and ExplainerDashboard.to_yaml() can store the
configuration of your explainer/dashboard to file.
- explainerdashboard CLI:
- Start an explainerdashboard from the command-line!
- start default dashboard from stored explainer : `$ explainerdashboard run explainer.joblib`
- start full configured dashboard from config: `$ explainerdashboard run explainerdashboard.yaml`
- build explainer based on input files defined in .yaml
(model.pkl, data.csv, etc): `$ explainerdashboard build explainerdashboard.yaml`
- includes new ascii logo :)


Improvements
- If idxs is not passed use X.index instead
- explainer.idxs performance enhancements
- added whatif component and tab to InlineExplainer
- added cumulative precision component to InlineExplainer

Improvements
- more straightforward imports: `from explainerdashboard import ClassifierExplainer, RegressionExplainer, ExplainerDashboard, InlineExplainer`
- all custom imports (such as ExplainerComponents, Composites, Tabs, etc)
combined under `explainerdashboard.custom`:
`from explainerdashboard.custom import *`

New Features
- New dashboard tab: WhatIfComponent/WhatIfComposite/WhatIfTab: allows you
to explore whatif scenario's by editing multiple features and observing
shap contributions and pdp plots. Switch off with ExplainerDashboard
parameter whatif=False.
- New login functionality: you can restrict access to your dashboard by passing
a list of `[login, password]` pairs:
`ExplainerDashboard(explainer, logins=[['login1', 'password1'], ['login2', 'password2']]).run()`
- Added 'target' parameter to explainer, to make more descriptive plots.
e.g. by setting target='Fare', will show 'Predicted Fare' instead of
simply 'Prediction' in various plots.
- in detailed shap/interaction summary plots, can now click on single
shap value for a particular feature, and have that index highlighted
for all features.
- autodetecting Google colab environment and setting mode='external'
(and suggesting so for jupyter notebook environments)
- confusion matrix now showing both percentage and counts
- Added classifier model performance summary component
- Added cumulative precision component


Improvements
- added documentation on how to deploy to heroku
- Cleaned up modebars for figures
- ClassifierExplainer asserts predict_proba attribute of model
- with model_output='logodds' still display probability in prediction summary

Other Changes
- removed monkeypatching shap_explainer note
-