Most promissing point to probe next

def suggest(self, utility_function):
        """Most promissing point to probe next"""
        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)