Batch sizes do not work in Bayesian Optimization Loop

The test_bayesian_optimization_loop.py code here throws an error for me when batch size > 1.

I placed the following code into a Python file and ran it.

import GPy
import numpy as np

from emukit.bayesian_optimization.acquisitions import ExpectedImprovement
from emukit.bayesian_optimization.loops import BayesianOptimizationLoop
from emukit.core.continuous_parameter import ContinuousParameter
from emukit.core.interfaces import IModel
from emukit.core.loop import FixedIterationsStoppingCondition, UserFunctionWrapper
from emukit.core.parameter_space import ParameterSpace
from emukit.model_wrappers.gpy_model_wrappers import GPyModelWrapper

def f(x):
    return x ** 2

batch_size = 3
n_init = 5
n_iterations = 5

x_init = np.random.rand(n_init, 1)
y_init = np.random.rand(n_init, 1)

# Make GPy model
gpy_model = GPy.models.GPRegression(x_init, y_init)
model = GPyModelWrapper(gpy_model)

space = ParameterSpace([ContinuousParameter("x", 0, 1)])
acquisition = ExpectedImprovement(model)

# Make loop and collect points
bo = BayesianOptimizationLoop(model=model, space=space, acquisition=acquisition, batch_size=batch_size)
bo.run_loop(UserFunctionWrapper(f), FixedIterationsStoppingCondition(n_iterations))

# Check we got the correct number of points
assert bo.loop_state.X.shape[0] == n_iterations * batch_size + n_init

# Check the obtained results
results = bo.get_results()

assert results.minimum_location.shape[0] == 1
assert results.best_found_value_per_iteration.shape[0] == n_iterations * batch_size + n_init

It gives me the following error:

 ~/opt/miniconda3/envs/delivery-sim/lib/python3.9/site-packages/emukit/bayesian_optimization/acquisitions/log_acquisition.py:25: RuntimeWarning:divide by zero encountered in log

Traceback (most recent call last):
  File "~/Dev/eco-deliveries/Delivery_problem/test.py", line 31, in <module>
    bo.run_loop(UserFunctionWrapper(f), FixedIterationsStoppingCondition(n_iterations))
  File "/Users/Alex/opt/miniconda3/envs/delivery-sim/lib/python3.9/site-packages/emukit/core/loop/outer_loop.py", line 92, in run_loop
    new_x = self.candidate_point_calculator.compute_next_points(self.loop_state, context)
  File "~/opt/miniconda3/envs/delivery-sim/lib/python3.9/site-packages/emukit/bayesian_optimization/local_penalization_calculator.py", line 85, in compute_next_points
    lipschitz_constant = _estimate_lipschitz_constant(self.parameter_space, self.model)
  File "~/opt/miniconda3/envs/delivery-sim/lib/python3.9/site-packages/emukit/bayesian_optimization/local_penalization_calculator.py", line 112, in _estimate_lipschitz_constant
    lipschitz_constant = -res.fun[0]
TypeError: 'float' object is not subscriptable

The error is thrown at this line because res.fun is not subscriptable. I’m running SciPy version 1.8.0.

Any thoughts?

[apaleyes]

Thanks @iRove108 , managed to reproduce now. Indeed the bug only concerns scipy 1.8.0, and code works with 1.7.3

[apaleyes]

The fix is now merged, thanks @iRove108