I have the following model:

I have the following model:

I am using fmin_slsqp to find the weights that minimize mean squared error. The weights need to be positive. For each pair of X and y, it takes ~10 seconds. (Each X is (10, 1000) and y is (10,)). I have 8000 pairs that need to be calculated:(

I am trying to solve the following problem. I have a sample of stores selected by geographies, and I’d like to select 20% of the sample in such a way that the fraction of sales per geography for the selection mimics that of the overall sales distribution by geography. I tried several iterations of the code below, but for some reason the ‘selection’ never changes inside the objective function, so test_fraction is always the same and there’s no real optimization. I have no idea what I am doing wrong. Could someone please help?

I have fitted data (from a pandas dataframe) to a function