gp output scaler

[simonsung06]

Added output scaler option for GP's

There is repeated code for the Pydantic validator so we probably need to think about a better way to do this in the future.

[jduerholt]

Hi Simon,

I think I have an idea, all data models in which we added the scaler show the following inheritance:

`class SingleTaskGPSurrogate(BotorchSurrogate, TrainableSurrogate):``

What do you think about creating a new data model TrainableBotorchSurrogate, add the scaler stuff incl. the validator there and inherit from there?

There is also the possibility to reuse validators, maybe you can have a look there, in pydantic2 it should be also easier to reuse.

Best,

Johannes

[simonsung06]

Hi Johannes. That is a possible idea about would be suitable for now I think. I'm not sure about how pydantic2 works though and how it changes things...

For now I'll make your suggested change and commit it when i get the chance

[simonsung06]

Hi @jduerholt,

I've made the suggested change by adding a new TrainableBotorchSurrogate data model.

In addition, the random forest data model was also modified because it originally inherits from BotorchSurrogate and TrainableSurrogate too. Whilst this is not completely necessary, I changed it to inherit TrainableBotorchSurrogate so RandomForestSurrogate will now also take scaler and output_scalar keywords arguments, which both default to ScalerEnum.IDENTITY. This meant changing the loads and dumps functions so that torch type scalers could be dumped and loaded if they are used.

[jduerholt]

Thx, I will have a look over the course of the week!

[jduerholt]

Looks good to me. Many thanks!