GilesStrong/lumin: Micro update

Important changes Online documentation now available at https://lumin.readthedocs.io Breaking Additions bin_binary_class_pred Ability to only consider classes rather than samples when computing bin edges Ability to add pure signal bins if normalisation uncertainty would be below some value plot_bottleneck_weighted_inputs method for interpretting bottleneck blocks in MultiBlock Online documentation: https://lumin.readthedocs.io Default optimiser notice Can now pass arbitary optimisers to the 'opt' value in opt_args. Optimisers still interpretable from strings. Expanded advanced model building example to include more interpretation examples and diagrams of network architectures Removals weak decorators for losses Fixes CatEmbedder.from_fy using features ignored by FoldYielder bottleneck_sz_masks to bottleneck_masks in MultiBlock SWA crahsing when evaluating targets of type long, when loss expects a float (model.evaluate now converts to float when objective is not multiclass classification) Doc string fixes Fixed model being moved to device after instantiating optimiser (sometimes leads to an error). Models now moved to device in ModelBuilder.get_model rather than in Model.__init__ Changes Depreciations Comment