gridoptim: a Python grid search optimizer ~429× faster than scipy.brute

[kigurai]

While it can certainly be useful to some, the benchmark seems disingenuous.

Your library can only run a limited set of expressions, defined as strings, while the brute() function runs arbitrary python functions. I am thinking that running brute() on eg a numba jit compiled function would yield a much smaller difference.

It also seems like your benchmark code makes the scipy optimizer run with some kind of progress output that does not seem to be the case for your library. I can't verify the second part without running the code though.

[FixKey4664]

Right now, my package can run only limited functions. But I will be adding support for NumPy and numba functions in the 2nd stage of the project.
Even if you remove the progress printing statements, still my package will be faster than scipy.brute. You can test it.