Major update

This release improved a number of areas:

• Huge performance improvements, especially if categorical variables were being imputed. These come from not predicting candidate data if we don't need to, using a much faster neighbors search, using numpy internally for indexing instead of pandas, and others.
• Ability to tune parameters of models, and use best parameters for mice.
• Improvements to code layout - got rid of ImputationSchema.
• Raw data is now stored as a numpy array to save space and improve indexing.
• Numpy arrays can be imputed, if you want to avoid pandas.
• Options of multiple build-in mean matching functions.
• Mean matching functions can handle most lightgbm objectives.