code: def suggest(self, utility_function):
          
          if len(self._space) == 0:
              return self._space.array_to_params(self._space.random_sample())
  
          # Sklearn's GP throws a large number of warnings at times, but
          # we don't really need to see them here.
          with warnings.catch_warnings():
              warnings.simplefilter("ignore")
              self._gp.fit(self._space.params, self._space.target)
  
          # Finding argmax of the acquisition function.
          suggestion = acq_max(
              ac=utility_function.utility,
              gp=self._gp,
              y_max=self._space.target.max(),
              bounds=self._space.bounds,
              random_state=self._random_state
          )
  
          return self._space.array_to_params(suggestion)
docs: Most promissing point to probe next