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				| @ -492,9 +492,7 @@ def run( | ||||
| 
 | ||||
|                 SearchAlgorithm = BlendSearch | ||||
|                 logger.info( | ||||
|                     "Using search algorithm {}.".format( | ||||
|                         SearchAlgorithm.__class__.__name__ | ||||
|                     ) | ||||
|                     "Using search algorithm {}.".format(SearchAlgorithm.__name__) | ||||
|                 ) | ||||
|             except ImportError: | ||||
|                 SearchAlgorithm = CFO | ||||
| @ -504,9 +502,7 @@ def run( | ||||
|             metric = metric or DEFAULT_METRIC | ||||
|         else: | ||||
|             SearchAlgorithm = CFO | ||||
|             logger.info( | ||||
|                 "Using search algorithm {}.".format(SearchAlgorithm.__class__.__name__) | ||||
|             ) | ||||
|             logger.info("Using search algorithm {}.".format(SearchAlgorithm.__name__)) | ||||
|             metric = lexico_objectives["metrics"][0] or DEFAULT_METRIC | ||||
|         search_alg = SearchAlgorithm( | ||||
|             metric=metric, | ||||
| @ -675,14 +671,14 @@ def run( | ||||
|                     num_trials = 0 | ||||
|                     if time_budget_s is None: | ||||
|                         time_budget_s = np.inf | ||||
|                     fail = 0 | ||||
|                     ub = ( | ||||
|                     num_failures = 0 | ||||
|                     upperbound_num_failures = ( | ||||
|                         len(evaluated_rewards) if evaluated_rewards else 0 | ||||
|                     ) + max_failure | ||||
|                     while ( | ||||
|                         time.time() - time_start < time_budget_s | ||||
|                         and (num_samples < 0 or num_trials < num_samples) | ||||
|                         and fail < ub | ||||
|                         and num_failures < upperbound_num_failures | ||||
|                     ): | ||||
|                         while len(_runner.running_trials) < n_concurrent_trials: | ||||
|                             # suggest trials for spark | ||||
| @ -690,9 +686,9 @@ def run( | ||||
|                             if trial_next: | ||||
|                                 num_trials += 1 | ||||
|                             else: | ||||
|                                 fail += 1  # break with ub consecutive failures | ||||
|                                 logger.debug(f"consecutive failures is {fail}") | ||||
|                                 if fail >= ub: | ||||
|                                 num_failures += 1  # break with upperbound_num_failures consecutive failures | ||||
|                                 logger.debug(f"consecutive failures is {num_failures}") | ||||
|                                 if num_failures >= upperbound_num_failures: | ||||
|                                     break | ||||
|                         trials_to_run = _runner.running_trials | ||||
|                         if not trials_to_run: | ||||
| @ -730,7 +726,7 @@ def run( | ||||
|                                     ) | ||||
|                                     report(_metric=result) | ||||
|                             _runner.stop_trial(trial_to_run) | ||||
|                         fail = 0 | ||||
|                         num_failures = 0 | ||||
|                     analysis = ExperimentAnalysis( | ||||
|                         _runner.get_trials(), | ||||
|                         metric=metric, | ||||
| @ -766,12 +762,14 @@ def run( | ||||
|         num_trials = 0 | ||||
|         if time_budget_s is None: | ||||
|             time_budget_s = np.inf | ||||
|         fail = 0 | ||||
|         ub = (len(evaluated_rewards) if evaluated_rewards else 0) + max_failure | ||||
|         num_failures = 0 | ||||
|         upperbound_num_failures = ( | ||||
|             len(evaluated_rewards) if evaluated_rewards else 0 | ||||
|         ) + max_failure | ||||
|         while ( | ||||
|             time.time() - time_start < time_budget_s | ||||
|             and (num_samples < 0 or num_trials < num_samples) | ||||
|             and fail < ub | ||||
|             and num_failures < upperbound_num_failures | ||||
|         ): | ||||
|             trial_to_run = _runner.step() | ||||
|             if trial_to_run: | ||||
| @ -789,10 +787,11 @@ def run( | ||||
|                     else: | ||||
|                         report(_metric=result) | ||||
|                 _runner.stop_trial(trial_to_run) | ||||
|                 fail = 0 | ||||
|                 num_failures = 0 | ||||
|             else: | ||||
|                 fail += 1  # break with ub consecutive failures | ||||
|         if fail == ub: | ||||
|                 # break with upperbound_num_failures consecutive failures | ||||
|                 num_failures += 1 | ||||
|         if num_failures == upperbound_num_failures: | ||||
|             logger.warning( | ||||
|                 f"fail to sample a trial for {max_failure} times in a row, stopping." | ||||
|             ) | ||||
|  | ||||
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