Datatype level classes

[bjherger]

Currently, a single datatype will have information in multiple locations:

Current state

Required

• Automater.init for passing in variable list
• constants.py for default pipeline
• constants.py for input handler
• constants.py for input handler lookup

Optional: Output data type

• constants.py Suggested loss
• Automater._create_output_nub for creating an output nub
• Automater.inverse_transform_output for inverse transforming the output data type.

Future state

This is absurd, and difficult to support / maintain. Another path might be to create an interface class, VariableTypeHandler, which includes the following methods:

• init
• default_transformation_pipeline
• input_nub_generator
• output_nub_generator (optional)
• output_inverse_transform (optional)
• output_suggested_loss (optional)

[bjherger]

Changes to make:

• Create DataType interface
• Update mappers to accept DT interface
• Update input nub to accept DT interface
• Update out nub to accept DT interface
• Update inverse transform to accept DT interface
• Update suggested loss to accept DT interface

[bjherger]

TODO

• Numerical tests
• Finish _valid_configurations_check

[bjherger]

Fixed in #84