Queue is empty while using Bayesian optimization

Question

I am using bayesian optimization to optimize the hyper parameters of a NN using python library "bayes_opt", but an error has raised

~\Miniconda3\envs\tensorflow\lib\site-packages\bayes_opt\bayesian_optimization.py in next(self) 25 if self.empty: ---> 26 raise StopIteration("Queue is empty, no more objects to retrieve.") 27 obj = self._queue[0]

StopIteration: Queue is empty, no more objects to retrieve.

My code is as following:

from bayes_opt import BayesianOptimization
import time

# Supress NaN warnings, see: https://stackoverflow.com/questions/34955158/what-might-be-the-cause-of-invalid-value-encountered-in-less-equal-in-numpy
import warnings
warnings.filterwarnings("ignore",category =RuntimeWarning)


# Bounded region of parameter space
pbounds = {'dropout': (0.0, 0.499),
           'lr': (0.0, 0.1),
           'neuronPct': (0.01, 1),
           'neuronShrink': (0.01, 1)
          } 

optimizer = BayesianOptimization(
    f=evaluate_network,
    pbounds=pbounds,
    verbose=2,  # verbose = 1 prints only when a maximum is observed, verbose = 0 is silent
    random_state=1,
)

start_time = time.time()
optimizer.maximize(init_points=10, n_iter=100,)
time_took = time.time() - start_time

print(optimizer.max)

and the output is as following:

|   iter    |  target   |  dropout  |    lr     | neuronPct | neuron... |
-------------------------------------------------------------------------
|  1        | -0.5718   |  0.2081   |  0.07203  |  0.01011  |  0.3093   |
|  2        | -0.5906   |  0.07323  |  0.009234 |  0.1944   |  0.3521   |
|  3        | -2.475    |  0.198    |  0.05388  |  0.425    |  0.6884   |
|  4        | -0.5708   |  0.102    |  0.08781  |  0.03711  |  0.6738   |
|  5        | -0.5749   |  0.2082   |  0.05587  |  0.149    |  0.2061   |
|  6        | -2.487    |  0.3996   |  0.09683  |  0.3203   |  0.6954   |
|  7        | -0.5669   |  0.4373   |  0.08946  |  0.09419  |  0.04866  |
|  8        | -0.5701   |  0.08475  |  0.08781  |  0.1074   |  0.4269   |
|  9        | -0.607    |  0.478    |  0.05332  |  0.695    |  0.3224   |
|  10       | -0.581    |  0.3426   |  0.08346  |  0.02811  |  0.7526   |
|  11       | -0.7295   |  0.1606   |  0.0      |  1.0      |  0.01     |
|  12       | -0.7131   |  0.499    |  0.0      |  0.607    |  0.01     |
|  13       | -0.7044   |  0.0      |  0.0      |  0.01     |  0.01     |
|  14       | -0.5872   |  0.04935  |  0.01347  |  0.4728   |  0.4246   |
|  15       | -0.5665   |  0.0      |  0.1      |  0.5566   |  0.01     |
|  16       | -0.9658   |  0.0      |  0.0      |  1.0      |  0.4938   |
|  17       | -0.9074   |  0.0      |  0.0      |  0.01     |  1.0      |
|  18       | -2.464    |  0.499    |  0.1      |  1.0      |  0.1975   |
|  19       | -0.9505   |  0.499    |  0.0      |  1.0      |  1.0      |
|  20       |  nan      |  0.0      |  0.1      |  1.0      |  1.0      |  

NOTE the "nan" at the last row of the output table
Thanks in advance.

Answer 1

Same thing just happened to me. I see that this is an old post, so no hope for a solution soon.

I managed to narrow it down to the following: if the pbounds interval is too small, this happens. I made the pbound interval slightly bigger and it stopped happening.

This worked for me, but I hope sometime in the future somebody fixes this. The error seems to be in sklearn.