I had an issue a few days ago (#581) where I wondered whether I could use BaseEstimator's set_params-like syntax when defining spaces and points with BayesSearchCV. That is paramName or subObject__subObjectParamName as keys to a dictionary-defined space. It turns out you can, so hooray.

However, I realized I can not actually use BayesSearchCV because my data is too large to fit in memory, so there is no way to call .fit(data). As a workaround, I have defined my own objective function which handles batch-reads and does its own cross validation and returns a score. But now I am finding out that the X_minimize functions only take lists as their dimensions parameter, which is wildly inconvenient because it means I can't just call estimator.set_params(**point) inside my objective function, and all the spaces I defined based on dictionaries can't work.

I suggest updating all X_minimize functions to support points and spaces defined as dictionaries rather than requiring lists. The list-solution seems to me an outdated one.