Ml-wrappers

- add code scanning pipeline using CodeQL
- update gitignore file for python
- add code coverage to ml-wrappers repository
- fix namespaces on doc strings for DatasetWrapper
- fix imports in model init file to include WrappedClassificationModel and WrappedRegressionModel

- fix dataset wrapper to support more input types

- continuous integration setup
- move tests to top-level folder
- update main readme and add specification docs
- add release process doc for ml-wrappers repository
- fix issues with circular dependencies in ml-wrappers
- rename test directory to tests
- add python 3.9 tests
- add supported type check in DatasetWrapper
- separate out python linting into separate workflow
- move test_dataset_wrapper.py from test/ to tests/
- suppress tensorflow warnings that may sometimes occur on import

- refactor timestamp featurizer and fix model init file

- fix dataset wrapper folder init file

Initial release of ml-wrappers package

The Machine Learning Wrappers SDK provides a unified wrapper for various ML frameworks - to have one uniform scikit-learn format predict and predict_proba functions.

Highlights of the package include:

A dataset wrapper to handle scipy sparse, pandas and numpy datasets in a uniform manner.
A model wrapper to handle models from various frameworks uniformly, including scikit-learn, tensorflow, pytorch, lightgbm and xgboost
Please see the github website for the documentation and sample notebooks:
https://github.com/microsoft/ml-wrappers