def run(self):
start = time.time()
trials = Trials()
best = fmin(self._obj, self.model_param_space._build_space(), tpe.suggest, self.max_evals, trials)
best_params = space_eval(self.model_param_space._build_space(), best)
best_params = self.model_param_space._convert_int_param(best_params)
trial_rmses = np.asarray(trials.losses(), dtype=float)
best_ind = np.argmin(trial_rmses)
best_rmse_mean = trial_rmses[best_ind]
best_rmse_std = trials.trial_attachments(trials.trials[best_ind])["std"]
self.logger.info("-"*50)
self.logger.info("Best RMSE")
self.logger.info(" Mean: %.6f"%best_rmse_mean)
self.logger.info(" std: %.6f"%best_rmse_std)
self.logger.info("Best param")
self.task._print_param_dict(best_params)
end = time.time()
_sec = end - start
_min = int(_sec/60.)
self.logger.info("Time")
if _min > 0:
self.logger.info(" %d mins"%_min)
else:
self.logger.info(" %d secs"%_sec)
self.logger.info("-"*50)
#------------------------ Main -------------------------
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