no succinct way to aggregate optimization results

When looking at the impact of media channels after training a model, there is a very nice function that returns a dataframe summary of the results (i.e., summary_table). The same can be achieved for the optimization results with a series of separate calls (e.g., budget_optimizer._get_delta_data with metrics such as "spend" and "incremental impact"), but it would be helpful to have an optimization summary function so this could all be done in one call. I am imagining that there would be the baseline spend, the optimized spend, the delta, the incremental, and measures of confidence for each channel as well as some indication of what kind of optimization was run (e.g., fixed budget) and for what time periods.

Start by comparing the existing summary_table function with the budget_optimizer._get_delta_data calls described in the issue. Define the optimization summary's required fields, confidence measures, optimization type, and time-period representation, then identify the relevant model and optimizer tests before implementing and validating the one-call result.

Written by the indexing model from the issue text.