Nomenclature for DoE Strategies

[jduerholt]

@dlinzner-bcs @bertiqwerty @Osburg @KappatC @evo-bschiller @evo-nlueck

We are planning to provide users of our internal web platform that makes BoFire available via a GUI with the possibility to also use strategies that are meant for problem initialization like the RandomStrategy and the DoEStrategy. Furthermore we also want to integrate factorial and fractional factorial plans via the capability from pyDoE3 (I tend to just copy the needed fuctionality out of the library and not create a dependency).

As already discussed in issues #314 and #307, we have to perform some cleanup, which I want to summarize here and dicuss some open questions:

• Make the other objectives available in the DoEStrategy data model despite the space-filling objective.
• In addition, also make the formula string available, if a user wants to exactly specify it.
• Wrap the functionality regarding the spacefilling objective into a UniversalSampler strategy as this does not need any model equation under the hood Sampling under nonlinear equality constraints #314.
• Create a strategy for classical factorial designs based on this module (https://github.com/relf/pyDOE3/blob/master/pyDOE3/doe_factorial.py).
• So for me the question would be how to name the different strategy? Keep in mind that we have already a strategy called FactorialStrategy which just generates the complete combinatorics for fully combinatorical/discrete domains (https://github.com/experimental-design/bofire/blob/main/bofire/data_models/strategies/factorial.py).

My proposal would be the following:

• Do not rename FactorialStrategy.
• Name strategy for the functionality of classical 2 level (fractional) factorial designs FactorialDoEStrategy.
• Rename DoEStrategy to ModelDoEStrategy to indicate that it goes into the direction of model based DoE.

Furthermore, we have to think about the nomenclature between strategies that we name Sampler and those which we name as Strategy, also this is inconcistent. We have for example the PolytopeSampler, RejectionSampler and the RandomStrategy which makes use of the two samplers ...

What do you think?

Best,

Johannes

[dlinzner-bcs]

Hi Johannes! Thank you for these neat ideas to complement our DoE capabilities. I completely agree that it would be nice to have non-model based DoE schemes available.

I agree with most of you proposals. Why not just call the new DoE strategy FactorialDoEStrategy instead and keep the other name? I am even not entirely sure, if it is not enough to stick with DoEStrategy, and just communicate what we want via the objective, as it is handled for the space-filling case now.

I actually think the best option would be to change to DoEObjective data models that just takes the fields they need. The workflow would be like

   domain = Domain(
        inputs=all_inputs,
        outputs=[ContinuousOutput(key="y")],
        constraints=all_constraints,
    )
    objective = data_models.doe_objectives.DOptimalityObjective(domain = domain, formula="linear")
   # objective = data_models.doe_objectivesSpaceFillingObjective(domain = domain)
    data_model = data_models.DoEStrategy(
        domain=domain,
        objective=objective,
        optimization_strategy="iterative",
    )
    strategy = DoEStrategy(data_model=data_model)
    candidates = strategy.ask(
        candidate_count=n_experiments
    )

A understanding question:
"In addition, also make the formula string available, if a user wants to exactly specify it." - is this related to DoE, or BO? DoE has this feature.

I have to read up on this UniversalSampler sounds fancy ;)