Functional test of regression

Must have

• review data creation
• review regularizatiopn
• create tf and torch model
• use framework everywhere
• create use existing objects for fmw list
• check data type conversion in dataset > do I need it
• deal with framework-specific multiprocessing issues
• Make sure run_declearn_baseline works
• Ensure proper seeding of all frameworks
• Global metrics storing for tests, use r2
• Transform into actual Assert loss value

Nice to have

• further simplify regression and make client distribution diff

Issues met during implementation, might require patching elsewhere:

• Torch : numpy data gets cast to flaot64, while default weights in torch are float32
• Tensorflow : Naming of wights vs gradients in compute_batch_gradients

Remarks :

• Errors can be hard to trace - the error message is sent back but not the exact location, requiring quite a bit of detective work to find the exact failing point
• Logger is very verbose and one gets lost in the flow of information - a priority system + a verbose controller in the logger would be great
• The original version of the examples bypassed declearn optimization and used the penalty arg of sklearn.linear_model.SGDRegressor for l2 regularization instead of our regularizer. It would be nice to catch those automaticaaly and raise something, because the risk is to do it twice.