sklearn-style interface?

[asross]

I was thinking about trying this method for a project, but the interface for running it (directories of text files and CSVs) seems pretty tricky to use / non-standard for Python users -- most people have their input data in numpy or pandas files, and don't want to parse through a complicated directory structure to inspect results.

It seems like it would be pretty straightforward, however, to create a scikit-learn-style wrapper class with a fit(X,y) method that:

• takes in np.ndarrays for the inputs and target (and optionally operations + variable names as lists or tuples of strings)
• creates a directory with all of these variables saved in the current format aifeynman supports
• invokes aifeynman.run_aifeynman with the appropriate arguments + paths
• exposes one-line helper methods and properties (on an instance of the wrapper class) to load different discovered equations from the results directory in a convenient form (e.g. giving SymPy/string expressions for different results along the Pareto frontier, as well as easy ways to evaluate them on test data)

Overall, I think creating an easy-to-use API for this method would do a ton to increase its impact. I was able to get other libraries like PySR and gplearn running in minutes, but this feels like it would take a good bit longer to figure out / set up.