https://github.com/fmfn/BayesianOptimization

I tried using this Bayesian optimization. I needed a little ingenuity, so I'll leave it.

There is only maximize

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

From the README. It seemed that only maximize was provided as an optimization method. What I wanted to do was optimize the search ranking (I want to hit at the top of the ranking), so I simply added a minus to the ranking and optimized it.

optimizer = BayesianOptimization(
    f=foo,
    pbounds=pbounds,
)

It means that the return value of this foo is negative. There may be a way on the maximize or ʻoptimizer` side, but I couldn't find it.

The arguments of the function to be optimized are limited

def foo(x, y):
    return -x ** 2 - (y - 1) ** 2 + 1

This is the function to be optimized.

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

It is a mechanism that gives a range of arguments like this and goes to find the optimum value in it. At my point, I wanted to give another argument like foo (x, y, z), and if I let maximize feed it as it is, an error occurred. As a countermeasure, I made z a global variable and forcibly passed it. I don't know if it's correct.

Precautions when determining the composite ratio of two values

This has nothing to do with Bayesian optimization, but when deciding the optimal composition ratio of x and y, I first wrote as follows.

def foo(a):
   return a*x+y

so,

pbounds = {'a': (0, 100)}

What did you do? It's no good.

def foo(a):
   return a*x+(100-a)*y

thus

pbounds = {'a': (0, 100)}

This worked.

I tried using Bayesian Optimization in Python

There is only maximize

The arguments of the function to be optimized are limited

Precautions when determining the composite ratio of two values