Call python packages from Julia

General Usage
,
1

Hi, with the help of PyCall.jl, I can easily run a script of python, but if I want to replace the function defined in python with the function defined in julia, it reminds me cannot find the sub-function, can anybody help me to have a look, because in the future, I want to use quite complex sub-function written in julia, here is a simple example


function black_box_function(x, y)   # with this function, doesn't work
    return -x^2 - (y - 1)^2 + 1
end

using PyCall

py"""
from bayes_opt import BayesianOptimization

#def black_box_function(x, y):          # with this function, it works well
    #return -x ** 2 - (y - 1) ** 2 + 1

pbounds = {'x': (2, 4), 'y': (-3, 3)}

optimizer = BayesianOptimization(
    f=black_box_function,
    pbounds=pbounds,
    random_state=1,
)

optimizer.maximize(
    init_points=2,
    n_iter=3,
)

print(optimizer.max)

"""
2

You need to “interpolate” the variable to send it to the Python side, something like f=$black_box_function, assuming you defined black_box_function in Julia as a function.

Fwiw I believe there are Bayesian optimization packages for Julia as well that might be interesting to check out.

3

Yeah. I checked julia already, but it’s still under developing, that’s why I go for python

4

Hi, I find a way that works, hope that can give some helps for other people, here is my code


function black_box_function(;x, y)   # here the ';' is because of the difference of parameters transfer, see this please[https://discourse.julialang.org/t/call-julia-function-from-python/75202](https://discourse.julialang.org/t/call-julia-function-from-python/75202)

    return -x^2 - (y - 1)^2 + 1
end

using PyCall

py"""
pbounds = {'x': (2, 4), 'y': (-3, 3)}
"""
temp=pyimport("bayes_opt")

optimizer = temp.BayesianOptimization(
    f=black_box_function,
    pbounds=py"pbounds",
    random_state=1,
)

optimizer.maximize(
    init_points=2,
    n_iter=3,
)

print(optimizer.max)