for _ in range(5):
    next_point = optimizer.suggest(utility)
    target = black_box_function(**next_point)
    optimizer.register(params=next_point, target=target)
    
    print(target, next_point)
print(optimizer.max)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-9-ff775cb8cb9c> in <module>
      1 for _ in range(5):
----> 2     next_point = optimizer.suggest(utility)
      3     target = black_box_function(**next_point)
      4     optimizer.register(params=next_point, target=target)
      5 

~/.venv/venv1/lib/python3.7/site-packages/bayes_opt/bayesian_optimization.py in suggest(self, utility_function)
    126         with warnings.catch_warnings():
    127             warnings.simplefilter("ignore")
--> 128             self._gp.fit(self._space.params, self._space.target)
    129 
    130         # Finding argmax of the acquisition function.

~/.venv/venv1/lib/python3.7/site-packages/sklearn/gaussian_process/_gpr.py in fit(self, X, y)
    232             optima = [(self._constrained_optimization(obj_func,
    233                                                       self.kernel_.theta,
--> 234                                                       self.kernel_.bounds))]
    235 
    236             # Additional runs are performed from log-uniform chosen initial

~/.venv/venv1/lib/python3.7/site-packages/sklearn/gaussian_process/_gpr.py in _constrained_optimization(self, obj_func, initial_theta, bounds)
    501             opt_res = scipy.optimize.minimize(
    502                 obj_func, initial_theta, method="L-BFGS-B", jac=True,
--> 503                 bounds=bounds)
    504             _check_optimize_result("lbfgs", opt_res)
    505             theta_opt, func_min = opt_res.x, opt_res.fun

~/.venv/venv1/lib/python3.7/site-packages/scipy/optimize/_minimize.py in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
    608     elif meth == 'l-bfgs-b':
    609         return _minimize_lbfgsb(fun, x0, args, jac, bounds,
--> 610                                 callback=callback, **options)
    611     elif meth == 'tnc':
    612         return _minimize_tnc(fun, x0, args, jac, bounds, callback=callback,

~/.venv/venv1/lib/python3.7/site-packages/scipy/optimize/lbfgsb.py in _minimize_lbfgsb(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, **unknown_options)
    343             # until the completion of the current minimization iteration.
    344             # Overwrite f and g:
--> 345             f, g = func_and_grad(x)
    346         elif task_str.startswith(b'NEW_X'):
    347             # new iteration

~/.venv/venv1/lib/python3.7/site-packages/scipy/optimize/lbfgsb.py in func_and_grad(x)
    293     else:
    294         def func_and_grad(x):
--> 295             f = fun(x, *args)
    296             g = jac(x, *args)
    297             return f, g

~/.venv/venv1/lib/python3.7/site-packages/scipy/optimize/optimize.py in function_wrapper(*wrapper_args)
    325     def function_wrapper(*wrapper_args):
    326         ncalls[0] += 1
--> 327         return function(*(wrapper_args + args))
    328 
    329     return ncalls, function_wrapper

~/.venv/venv1/lib/python3.7/site-packages/scipy/optimize/optimize.py in __call__(self, x, *args)
     63     def __call__(self, x, *args):
     64         self.x = numpy.asarray(x).copy()
---> 65         fg = self.fun(x, *args)
     66         self.jac = fg[1]
     67         return fg[0]

~/.venv/venv1/lib/python3.7/site-packages/sklearn/gaussian_process/_gpr.py in obj_func(theta, eval_gradient)
    223                 if eval_gradient:
    224                     lml, grad = self.log_marginal_likelihood(
--> 225                         theta, eval_gradient=True, clone_kernel=False)
    226                     return -lml, -grad
    227                 else:

~/.venv/venv1/lib/python3.7/site-packages/sklearn/gaussian_process/_gpr.py in log_marginal_likelihood(self, theta, eval_gradient, clone_kernel)
    474             y_train = y_train[:, np.newaxis]
    475 
--> 476         alpha = cho_solve((L, True), y_train)  # Line 3
    477 
    478         # Compute log-likelihood (compare line 7)

~/.venv/venv1/lib/python3.7/site-packages/scipy/linalg/decomp_cholesky.py in cho_solve(c_and_lower, b, overwrite_b, check_finite)
    194     (c, lower) = c_and_lower
    195     if check_finite:
--> 196         b1 = asarray_chkfinite(b)
    197         c = asarray_chkfinite(c)
    198     else:

~/.venv/venv1/lib/python3.7/site-packages/numpy/lib/function_base.py in asarray_chkfinite(a, dtype, order)
    497     if a.dtype.char in typecodes['AllFloat'] and not np.isfinite(a).all():
    498         raise ValueError(
--> 499             "array must not contain infs or NaNs")
    500     return a
    501 

ValueError: array must not contain infs or NaNs