optimizer.py 文件源码

def maximize(self, score_optimum=None, realize=True):
        """
        Find the next best hyper-parameter setting to optimizer.

        Parameters
        ----------
        score_optimum: float
            An optional score to use inside the EI formula instead of the optimizer's current_best_score

        realize: bool
            Whether or not to give a more realistic estimate of the EI (default=True)

        Returns
        -------
        best_setting: dict
            The setting with the highest expected improvement

        best_score: float
            The highest EI (per second)
        """

        start = time.time()

        # Select a sample of parameters
        sampled_params = ParameterSampler(self.param_distributions, self.draw_samples)

        # Set score optimum
        if score_optimum is None:
            score_optimum = self.current_best_score

        # Determine the best parameters
        best_setting, best_score = self._maximize_on_sample(sampled_params, score_optimum)

        if self.local_search:
            best_setting, best_score = self._local_search(best_setting, best_score, score_optimum,
                                                          max_steps=self.ls_max_steps)

        if realize:
            best_setting, best_score = self._realize(best_setting, best_score, score_optimum)

        # Store running time
        running_time = (time.time() - start) / self.simulate_speedup
        self.maximize_times.append(running_time)

        return best_setting, best_score