About `skmodel.PortfolioSelection`

[Mamba413]

1. Replace seed with random_state? The latter is a convention parameter in scikit-learn.
2. Replace hist with cov_matrix? See this: https://pyportfolioopt.readthedocs.io/en/latest/_modules/pypfopt/efficient_frontier/efficient_frontier.html#EfficientFrontier.__init__
3. Replace k with s might be better because we use s in the other places when illustrating sparsity constraint optimization.
4. Replace lambda_ with lambda?
5. Have you tested whether it is really compatible with scikit-learn? For example, you may test whether sklearn.model_selection.GridSearchCV can be used for selecting lambda?

[chenpnn]

Thanks for your practical suggestions!

• I have replaced the original parameter seed by random_state, hist by cov_matrix, k by s and lambda_ by alpha.

• I also tested its compatibility with sklearn.model_selection.GridSearchCV and it runs well for selecting alpha.

• More other compatibility tests will be made later.

• See more details in PortfolioSelection.