hyper_param_optimizer.py 文件源码

def optimize(self, hyper_param_iterations, old_trials=None):
        hp_info = self.model_runner.hyper_parameter_info()

        if old_trials is None:
            trials = Trials()
        else:
            trials = old_trials

        if hp_info.get('fixed') is True:
            del hp_info['fixed']
            result = self.model_runner.train_and_cv_error(self.features, hp_info)
            trials.trials.append({
                'result': result,
                'misc': {'tid': 0, 'vals': hp_info},
            })

            return trials, hp_info

        best_hps = fmin(self.to_minimize,
                        hp_info,
                        algo=tpe.suggest,
                        max_evals=hyper_param_iterations,
                        trials=trials)

        filtered_hps = {}
        for hp in self.model_runner.hyper_parameter_info():
            try:
                filtered_hps[hp] = best_hps[hp]
            except KeyError:
                filtered_hps[hp] = None

        return trials, filtered_hps