WIP: Counterfactual instances

[jklaise]

No description provided.

[jklaise]

This pull request will implement an improved counterfactual search algorithm based on Wachter et al. (2017).

TODO:

• Incorporate distance loss into the TF graph for speed
• Support TF and Keras models by directly back-propagating through to get prediction gradients (requires TF version of sorting and getting the highest non-test class probability for target_class=other)
• Fix bug in learning rate scheduling (global step updating but learning rate is constant)
• Optimize search speed / sensible hyperparameter choices

Optional:

• Use FISTA for optimization
• Optional .fit method if training set is available - scale distance by MADs (use functools.partial to make it a 2-argument distance)
• Option to offer different losses instead of L1

[jklaise]

One more thing to fix in the core algorithm - there seems to be an off-by-one error in the outer loop where lambda reaches values where it has no hope of finding a solution.