Make only torch models (and Agarwal) threaded

[tmke8]

This has two main reasons:

1. It simplifies the code a lot. Algorithms are now either just threaded or just non-threaded (i.e. they run in the main context of the process). This will hopefully make it easier to implement new algorithms.
2. It is really only models that run on the GPU that benefit from running in their own thread. The problem with these models is that they can often hog the GPU if the process is not properly shut down. This is especially a problem when you run algorithms in a notebook. For the sklearn models this is not really an issue and running them explicitly parallel also doesn't have much of an effect, because they naturally use as many CPUs as they can.

closes #229

[tmke8]

(This removed about 500 lines of code 🎉 )

[tmke8]

The algorithms fall now into the following classes:

• PreAlgorithm:
  • Upsampler
• PreAlgorithmAsync:
  • Beutel
  • VFAE
  • Zemel
• InAlgorithm:
  • Kamiran
  • LR
  • LRProb
  • LRCV
  • MLP
  • SVM
• InAlgorithmAsync:
  • Agarwal