Ml-tooling

- Breaking change - BaseClassModel renamed to ModelData.
- Breaking change - model renamed to estimator
- Added Precision-Recall Curve
- Added option to give custom file name to .save_estimator()
- Instantiating with estimator is now optional - set estimator later using .init_estimator
- We have a logo! Added ML Tooling logo to docs

Bugfixes
- Now issues a warning when git is not installed.

Bugfixes
- Data for a class is changed from instance variable to class variable
- Grid search only copies data to workers once and reuses them across grid and folds.
- The Data Class now takes a random seed which it will receive from the BaseClass
- Disabled mem-maping in feature importance
- Added license file to package
- Updated requirements

Features
- Feature importances changed to use permutation instead of built-in for better estimates.

Bugfixes
- .train_estimator will now reset the result attribute to None, in order to
prevent users from mistakenly assuming the result is from the training
- Fixed bug in lift_score when using dataframes
- Fixed bug when training model and then scoring model
- Fixed bug where users could not save models if no result had been created, as would
happen if the user only called .train_estimator before saving.
- Default_metric is now the same metric as the one specified for the model in .config
- Each class inheriting from ModelData has an individual config
- Changed get_scorer_func to wrap sklearn's get_scorer
- Fixed bug when gridsearching twice

- Added Binarize Transformer
- Added ability to use keywords in FuncTransformer
- .predict now returns a dataframe indexed on input
- Updated dependencies

- Added gridsearch method to BaseClass. Gridsearch your model
and return a list of results for inspection
- Added ResultGroup - any method that returns a list of results now
returns a ResultGroup instead.
- Added logging
- Added ability to record runs as yaml