add pipeline data in results
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				|  | @ -686,9 +686,13 @@ def configuration_recommendation(recommendation_input): | ||||||
|     params = JSONUtil.loads(session.hyperparameters) |     params = JSONUtil.loads(session.hyperparameters) | ||||||
| 
 | 
 | ||||||
|     if target_data['bad'] is True: |     if target_data['bad'] is True: | ||||||
|  |         if session.tuning_session == 'randomly_generate': | ||||||
|  |             info = 'Randomly generated' | ||||||
|  |         else: | ||||||
|  |             info = 'WARNING: no training data, the config is generated by LHS' | ||||||
|         target_data_res = create_and_save_recommendation( |         target_data_res = create_and_save_recommendation( | ||||||
|             recommended_knobs=target_data['config_recommend'], result=newest_result, |             recommended_knobs=target_data['config_recommend'], result=newest_result, | ||||||
|             status='bad', info='WARNING: no training data, the config is generated by LHS', |             status='bad', info=info, | ||||||
|             pipeline_run=target_data['pipeline_run']) |             pipeline_run=target_data['pipeline_run']) | ||||||
|         LOG.debug('%s: Skipping configuration recommendation.\nData:\n%s\n\n', |         LOG.debug('%s: Skipping configuration recommendation.\nData:\n%s\n\n', | ||||||
|                   AlgorithmType.name(algorithm), target_data) |                   AlgorithmType.name(algorithm), target_data) | ||||||
|  |  | ||||||
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