NotUniqueError encountered for constrained problems.

[iffanh]

Describe the bug
Using bayesian-optimization==1.4.3 I'd like to run a constrained optimization problem. However, I encountered bayes_opt.util.NotUniqueError even though I have set allow_duplicate_points=True

I checked bayesian_optimization.py and observe that allow_duplicate_points=True is not imposed for generally constrained problems. Adding this argument to the TargetSpace class (line 149 in bayesian_optimization.py) resulted in cycling effect, with no visible progress on the iterates. See the stack below:

|   iter    |  target   |  allowed  |     x     |     y     |
-------------------------------------------------------------
| 4         | 0.9135    | True      | 2.195     | -1.897    |
| 7         | 1.524     | True      | 2.017     | 2.993     |
| 15        | 1.541     | True      | 2.0       | 2.88      |
| 16        | 1.556     | True      | 2.0       | -3.0      |
Data point [ 2. -3.] is not unique. 1 duplicates registered. Continuing ...
Data point [2. 3.] is not unique. 2 duplicates registered. Continuing ...
Data point [ 2. -3.] is not unique. 3 duplicates registered. Continuing ...
Data point [2. 3.] is not unique. 4 duplicates registered. Continuing ...
Data point [ 2. -3.] is not unique. 5 duplicates registered. Continuing ...
Data point [2. 3.] is not unique. 6 duplicates registered. Continuing ...
Data point [ 2. -3.] is not unique. 7 duplicates registered. Continuing ...
Data point [2. 3.] is not unique. 8 duplicates registered. Continuing ...
Data point [ 2. -3.] is not unique. 9 duplicates registered. Continuing ...
Data point [2. 3.] is not unique. 10 duplicates registered. Continuing ...
Data point [ 2. -3.] is not unique. 11 duplicates registered. Continuing ...
Data point [ 2. -3.] is not unique. 12 duplicates registered. Continuing ...
Data point [ 2. -3.] is not unique. 13 duplicates registered. Continuing ...
Data point [2. 3.] is not unique. 14 duplicates registered. Continuing ...
Data point [2. 3.] is not unique. 15 duplicates registered. Continuing ...
Data point [2. 3.] is not unique. 16 duplicates registered. Continuing ...
Data point [ 2. -3.] is not unique. 17 duplicates registered. Continuing ...
Data point [2. 3.] is not unique. 18 duplicates registered. Continuing ...
Data point [ 2. -3.] is not unique. 19 duplicates registered. Continuing ...
Data point [2. 3.] is not unique. 20 duplicates registered. Continuing ...
Data point [2. 3.] is not unique. 21 duplicates registered. Continuing ...
Data point [ 2. -3.] is not unique. 22 duplicates registered. Continuing ...
Data point [ 2. -3.] is not unique. 23 duplicates registered. Continuing ...
Data point [2. 3.] is not unique. 24 duplicates registered. Continuing ...
Data point [ 2. -3.] is not unique. 25 duplicates registered. Continuing ...
Data point [2. 3.] is not unique. 26 duplicates registered. Continuing ...
Data point [ 2. -3.] is not unique. 27 duplicates registered. Continuing ...
Data point [2. 3.] is not unique. 28 duplicates registered. Continuing ...
Data point [ 2. -3.] is not unique. 29 duplicates registered. Continuing ...

To Reproduce
To reproduce, I use one of the examples given in the repository and changed n_iter to 100.

Ex:

from bayes_opt import BayesianOptimization
import matplotlib.pyplot as plt
from scipy.optimize import NonlinearConstraint


def target_function(x, y):
    # Gardner is looking for the minimum, but this packages looks for maxima, thus the sign switch
    return np.cos(2*x)*np.cos(y) + np.sin(x)

def constraint_function_2_dim(x, y):
    return np.array([
        - np.cos(x) * np.cos(y) + np.sin(x) * np.sin(y),
        - np.cos(x) * np.cos(-y) + np.sin(x) * np.sin(-y)])
# Bounded region of parameter space
pbounds = {'x': (2, 4), 'y': (-3, 3)}

constraint_lower = np.array([-np.inf, -np.inf])
constraint_upper = np.array([0.6, 0.6])

constraint = NonlinearConstraint(constraint_function_2_dim, constraint_lower, constraint_upper)
optimizer = BayesianOptimization(
    f=target_function,
    constraint=constraint,
    pbounds=pbounds,
    allow_duplicate_points=True,
    verbose=1, # verbose = 1 prints only when a maximum is observed, verbose = 0 is silent
    random_state=1,
)

optimizer.maximize(
    init_points=2,
    n_iter=100 ## I changed this one
)

Expected behavior
The iterates should converge to the maximum, regardless whether it is allowed to have duplicate points.

Environment (please complete the following information):

• OS: Ubuntu
• python Version 3.8.10
• numpy Version 1.24.4
• scipy Version 1.10.1
• bayesian-optimization Version 1.4.3

[till-m]

Could you maybe check if this was fixed with #437? I think we haven't made a release since that PR, but you can install from master here.

E: If I understand correctly, you implemented exactly what the PR does. Could it be that you have simply found the maximum?

Sorry, I am on my phone, so I can't run anything currently.

[iffanh]

Hi, I checked and the commit that was fixed in #437 was exactly what I tested too in my local env.
Ideally, I would want the algorithm itself to detect if such maximum has been found and terminate the program.

Perhaps I should raise another ticket for this?

[till-m]

Something like this has also been raised in #381. I'm still of the opinion I expressed there, i.e. that .maximize should be kept simple and that we should leave these things to the user (see #373 for how to do this). OTOH I know that other people disagree with me here and I would be open to adding it, generally speaking.

[iffanh]

I could see such automatic detection become very useful for problems with expensive evaluation.

Anyway, I'll close this ticket as it will be fixed in the next release (I presume). Thanks for answering!