Lucas/wip

[lucashervier]

Point of this PR is to close #47.

Attributions

In 65676ec is all the neccessary change towards Lime in order to make it works properly with tabular data as inputs. Mainly, we detect if inputs are tabular data or has channels in order to appropriately attributes the right default mapping function and the way to apply masks.

Considering Occlusion less changes were required (feea923). Only to adapt the way to build masks and to apply them depending of the inputs shape.

6899e89 make a few change considering KernelShap in order to perfectly fit Lime framework. Moreover, a trick was used in order to obtain the shape of a Ragged Tensor (instead of using len(interpret_samples[0])

Tests

The commit 0d7f3f4 create a generic test to see if most attribution methods works with tabular data in several configurations.

Note:

• For tabular data GradCAM and GradCAMPP are not applicable (because it works with a convulotional layer)
• At present, we did not find a smart way to apply Rise to tabular data

[fel-thomas]

As always, super good ;)
I let you fix my comments and rebase. I will try to pass on Lime soon to see if we can make it shorter.