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experiment.log
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HOW MUCH OF EACH DATASET SHOULD WE ADD (BASELINE WITH IADS-E-NOMUSIC SHUFFLED LABELS)?
2024-03-14 17:19:40: Loading data
2024-03-14 17:19:54: Ratio: 1.0IADS-E-nomusic + 0.0PMEmo
2024-03-14 17:19:54: Label: AroMN
2024-03-14 17:19:54: [Clustering]
2024-03-14 17:19:55: [Ended]
2024-03-14 17:19:55: [Clustering]
2024-03-14 17:19:55: [Ended]
2024-03-14 17:19:55: Tuning AutoML
[WARNING] [2024-03-14 17:20:01,647:Client-AutoML(8229):b21605cf-e21e-11ee-811e-b499bab8ef8a] Time limit for a single run is higher than total time limit. Capping the limit for a single run to the total time given to SMAC (17996.952372)
[WARNING] [2024-03-14 17:20:01,647:Client-AutoML(8229):b21605cf-e21e-11ee-811e-b499bab8ef8a] Capping the per_run_time_limit to 8998.0 to have time for a least 2 models in each process.
[WARNING] [2024-03-14 17:20:23,950:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-14 17:20:32,785:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-14 17:20:34,376:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-14 17:20:40,368:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:20:41,906:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:20:44,993:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:21:02,882:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:21:16,378:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:23:21,126:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:23:46,736:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:24:09,348:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:24:32,804:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:24:56,174:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:25:01,795:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:25:49,622:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:25:52,639:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:26:44,140:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:27:07,778:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:27:13,721:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:27:47,337:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:28:31,482:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:29:01,995:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:29:18,678:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:29:46,733:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:30:16,182:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:32:17,635:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:33:59,226:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 17:34:34,321:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
Needed time for optimizing the model: 18307.816620349884
2024-03-14 22:25:03: Cross-validating best estimator
Needed time for cross-validating the best model: 1942.0432975292206
2024-03-14 22:58:25: ___________________
2024-03-14 22:58:25: Obtained metrics for IADS-E-nomusic
2024-03-14 22:58:25: r2, RMSE, MAE
2024-03-14 22:58:25: 7.59e-03 ± 8.06e-03
2024-03-14 22:58:25: 4.75e-01 ± 1.14e-02
2024-03-14 22:58:25: 4.13e-01 ± 1.10e-02
2024-03-14 22:58:25: ___________________
2024-03-14 22:58:25: Obtained metrics for PMEmo
2024-03-14 22:58:25: r2, RMSE, MAE
2024-03-14 22:58:25: -1.63e-01 ± 8.90e-02
2024-03-14 22:58:25: 4.30e-01 ± 1.05e-02
2024-03-14 22:58:25: 3.67e-01 ± 1.15e-02
2024-03-14 22:58:25:
--------
2024-03-14 23:03:26: Label: ValMN
2024-03-14 23:03:26: [Clustering]
2024-03-14 23:03:26: [Ended]
2024-03-14 23:03:26: [Clustering]
2024-03-14 23:03:27: [Ended]
2024-03-14 23:03:27: Tuning AutoML
[WARNING] [2024-03-14 23:05:11,758:Client-AutoML(8229):af955452-e24e-11ee-811e-b499bab8ef8a] Time limit for a single run is higher than total time limit. Capping the limit for a single run to the total time given to SMAC (17996.950727)
[WARNING] [2024-03-14 23:05:11,758:Client-AutoML(8229):af955452-e24e-11ee-811e-b499bab8ef8a] Capping the per_run_time_limit to 8998.0 to have time for a least 2 models in each process.
[WARNING] [2024-03-14 23:05:34,100:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-14 23:05:41,254:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-14 23:05:55,568:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 23:05:57,482:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 23:06:03,929:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 23:06:21,377:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 23:06:52,751:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 23:07:18,347:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-14 23:07:26,856:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
Needed time for optimizing the model: 18401.54528617859
2024-03-15 04:10:09: Cross-validating best estimator
Needed time for cross-validating the best model: 869.9578125476837
2024-03-15 04:25:39: ___________________
2024-03-15 04:25:39: Obtained metrics for IADS-E-nomusic
2024-03-15 04:25:39: r2, RMSE, MAE
2024-03-15 04:25:39: 8.46e-03 ± 9.73e-03
2024-03-15 04:25:39: 4.95e-01 ± 1.17e-02
2024-03-15 04:25:39: 4.26e-01 ± 1.43e-02
2024-03-15 04:25:39: ___________________
2024-03-15 04:25:39: Obtained metrics for PMEmo
2024-03-15 04:25:39: r2, RMSE, MAE
2024-03-15 04:25:39: -3.18e-01 ± 1.49e-01
2024-03-15 04:25:39: 4.36e-01 ± 1.99e-02
2024-03-15 04:25:39: 3.75e-01 ± 2.71e-02
2024-03-15 04:25:39:
--------
--------
2024-03-15 04:30:39: Ratio: 1.0IADS-E-nomusic + 0.25PMEmo
2024-03-15 04:30:39: Label: AroMN
2024-03-15 04:30:39: [Clustering]
2024-03-15 04:30:40: [Ended]
2024-03-15 04:30:40: [Clustering]
2024-03-15 04:30:40: [Ended]
2024-03-15 04:30:40: Tuning AutoML
[WARNING] [2024-03-15 04:40:56,849:Client-AutoML(8229):65e3a6c8-e27c-11ee-811e-b499bab8ef8a] Time limit for a single run is higher than total time limit. Capping the limit for a single run to the total time given to SMAC (17996.639142)
[WARNING] [2024-03-15 04:40:56,850:Client-AutoML(8229):65e3a6c8-e27c-11ee-811e-b499bab8ef8a] Capping the per_run_time_limit to 8998.0 to have time for a least 2 models in each process.
[WARNING] [2024-03-15 04:41:29,087:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-15 04:41:36,041:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-15 04:41:39,422:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-15 04:41:41,303:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 04:41:42,908:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 04:41:44,699:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 04:42:13,337:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 04:42:20,701:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 04:42:44,497:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
Needed time for optimizing the model: 18927.789124011993
2024-03-15 09:46:08: Cross-validating best estimator
Needed time for cross-validating the best model: 718.9537653923035
2024-03-15 09:59:07: ___________________
2024-03-15 09:59:07: Obtained metrics for IADS-E-nomusic
2024-03-15 09:59:07: r2, RMSE, MAE
2024-03-15 09:59:07: 6.05e-02 ± 1.55e-02
2024-03-15 09:59:07: 4.62e-01 ± 1.22e-02
2024-03-15 09:59:07: 3.91e-01 ± 1.07e-02
2024-03-15 09:59:07: ___________________
2024-03-15 09:59:07: Obtained metrics for PMEmo
2024-03-15 09:59:07: r2, RMSE, MAE
2024-03-15 09:59:07: 2.85e-01 ± 7.12e-02
2024-03-15 09:59:07: 3.37e-01 ± 2.47e-02
2024-03-15 09:59:07: 2.57e-01 ± 2.33e-02
2024-03-15 09:59:07:
--------
2024-03-15 10:04:07: Label: ValMN
2024-03-15 10:04:08: [Clustering]
2024-03-15 10:04:08: [Ended]
2024-03-15 10:04:08: [Clustering]
2024-03-15 10:04:09: [Ended]
2024-03-15 10:04:09: Tuning AutoML
[WARNING] [2024-03-15 10:06:35,045:Client-AutoML(8229):fbe19c35-e2aa-11ee-811e-b499bab8ef8a] Time limit for a single run is higher than total time limit. Capping the limit for a single run to the total time given to SMAC (17996.643800)
[WARNING] [2024-03-15 10:06:35,045:Client-AutoML(8229):fbe19c35-e2aa-11ee-811e-b499bab8ef8a] Capping the per_run_time_limit to 8998.0 to have time for a least 2 models in each process.
[WARNING] [2024-03-15 10:06:58,374:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-15 10:07:06,478:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-15 10:07:09,392:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-15 10:07:11,080:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-15 10:07:13,667:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 10:07:14,916:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 10:07:45,524:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 10:07:51,110:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
Needed time for optimizing the model: 18456.30779314041
2024-03-15 15:11:45: Cross-validating best estimator
Needed time for cross-validating the best model: 3354.8247361183167
2024-03-15 16:08:40: ___________________
2024-03-15 16:08:40: Obtained metrics for IADS-E-nomusic
2024-03-15 16:08:40: r2, RMSE, MAE
2024-03-15 16:08:40: 3.66e-02 ± 4.39e-02
2024-03-15 16:08:40: 4.88e-01 ± 5.74e-03
2024-03-15 16:08:40: 4.10e-01 ± 1.04e-02
2024-03-15 16:08:40: ___________________
2024-03-15 16:08:40: Obtained metrics for PMEmo
2024-03-15 16:08:40: r2, RMSE, MAE
2024-03-15 16:08:40: 4.26e-01 ± 5.58e-02
2024-03-15 16:08:40: 2.88e-01 ± 6.70e-03
2024-03-15 16:08:40: 2.24e-01 ± 1.23e-02
2024-03-15 16:08:40:
--------
--------
2024-03-15 16:13:40: Ratio: 1.0IADS-E-nomusic + 0.5PMEmo
2024-03-15 16:13:40: Label: AroMN
2024-03-15 16:13:40: [Clustering]
2024-03-15 16:13:41: [Ended]
2024-03-15 16:13:41: [Clustering]
2024-03-15 16:13:42: [Ended]
2024-03-15 16:13:42: Tuning AutoML
[WARNING] [2024-03-15 16:13:46,384:Client-AutoML(8229):9becd1bb-e2de-11ee-811e-b499bab8ef8a] Time limit for a single run is higher than total time limit. Capping the limit for a single run to the total time given to SMAC (17996.574397)
[WARNING] [2024-03-15 16:13:46,384:Client-AutoML(8229):9becd1bb-e2de-11ee-811e-b499bab8ef8a] Capping the per_run_time_limit to 8998.0 to have time for a least 2 models in each process.
[WARNING] [2024-03-15 16:14:13,809:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-15 16:14:23,983:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-15 16:14:26,836:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-15 16:14:32,102:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 16:14:40,451:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 16:14:42,956:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 16:15:04,406:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 16:15:19,199:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 16:16:26,521:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 16:16:58,073:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 16:18:05,733:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
Needed time for optimizing the model: 18313.9528567791
2024-03-15 21:18:56: Cross-validating best estimator
Needed time for cross-validating the best model: 4481.586375713348
2024-03-15 22:34:37: ___________________
2024-03-15 22:34:37: Obtained metrics for IADS-E-nomusic
2024-03-15 22:34:37: r2, RMSE, MAE
2024-03-15 22:34:37: 6.47e-02 ± 2.01e-02
2024-03-15 22:34:37: 4.61e-01 ± 1.50e-02
2024-03-15 22:34:37: 3.84e-01 ± 1.22e-02
2024-03-15 22:34:37: ___________________
2024-03-15 22:34:37: Obtained metrics for PMEmo
2024-03-15 22:34:37: r2, RMSE, MAE
2024-03-15 22:34:37: 6.16e-01 ± 5.54e-02
2024-03-15 22:34:37: 2.47e-01 ± 2.28e-02
2024-03-15 22:34:37: 1.87e-01 ± 1.86e-02
2024-03-15 22:34:37:
--------
2024-03-15 22:39:37: Label: ValMN
2024-03-15 22:39:37: [Clustering]
2024-03-15 22:39:38: [Ended]
2024-03-15 22:39:38: [Clustering]
2024-03-15 22:39:39: [Ended]
2024-03-15 22:39:39: Tuning AutoML
[WARNING] [2024-03-15 22:40:12,949:Client-AutoML(8229):86a0618b-e314-11ee-811e-b499bab8ef8a] Time limit for a single run is higher than total time limit. Capping the limit for a single run to the total time given to SMAC (17996.400138)
[WARNING] [2024-03-15 22:40:12,949:Client-AutoML(8229):86a0618b-e314-11ee-811e-b499bab8ef8a] Capping the per_run_time_limit to 8998.0 to have time for a least 2 models in each process.
[WARNING] [2024-03-15 22:40:35,532:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-15 22:40:48,706:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-15 22:40:52,137:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 22:40:57,192:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 22:41:05,036:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 22:41:25,187:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 22:41:39,723:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-15 22:42:29,864:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
Needed time for optimizing the model: 18346.126491069794
2024-03-16 03:45:25: Cross-validating best estimator
Needed time for cross-validating the best model: 1366.1530587673187
2024-03-16 04:09:11: ___________________
2024-03-16 04:09:11: Obtained metrics for IADS-E-nomusic
2024-03-16 04:09:11: r2, RMSE, MAE
2024-03-16 04:09:11: 6.42e-02 ± 4.07e-02
2024-03-16 04:09:11: 4.81e-01 ± 1.69e-02
2024-03-16 04:09:11: 3.95e-01 ± 1.57e-02
2024-03-16 04:09:11: ___________________
2024-03-16 04:09:11: Obtained metrics for PMEmo
2024-03-16 04:09:11: r2, RMSE, MAE
2024-03-16 04:09:11: 5.78e-01 ± 3.58e-02
2024-03-16 04:09:11: 2.47e-01 ± 1.05e-02
2024-03-16 04:09:11: 1.82e-01 ± 9.85e-03
2024-03-16 04:09:11:
--------
--------
2024-03-16 04:14:11: Ratio: 1.0IADS-E-nomusic + 0.75PMEmo
2024-03-16 04:14:11: Label: AroMN
2024-03-16 04:14:11: [Clustering]
2024-03-16 04:14:12: [Ended]
2024-03-16 04:14:12: [Clustering]
2024-03-16 04:14:13: [Ended]
2024-03-16 04:14:13: Tuning AutoML
[WARNING] [2024-03-16 04:15:56,236:Client-AutoML(8229):439dae75-e343-11ee-811e-b499bab8ef8a] Time limit for a single run is higher than total time limit. Capping the limit for a single run to the total time given to SMAC (17996.254218)
[WARNING] [2024-03-16 04:15:56,236:Client-AutoML(8229):439dae75-e343-11ee-811e-b499bab8ef8a] Capping the per_run_time_limit to 8998.0 to have time for a least 2 models in each process.
[WARNING] [2024-03-16 04:16:25,076:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-16 04:16:38,571:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-16 04:16:42,812:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-16 04:16:45,400:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-16 04:16:51,717:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-16 04:16:56,479:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
Needed time for optimizing the model: 18493.528491020203
2024-03-16 09:22:26: Cross-validating best estimator
Needed time for cross-validating the best model: 5477.6959636211395
2024-03-16 10:54:44: ___________________
2024-03-16 10:54:44: Obtained metrics for IADS-E-nomusic
2024-03-16 10:54:44: r2, RMSE, MAE
2024-03-16 10:54:44: 1.01e-01 ± 2.07e-02
2024-03-16 10:54:44: 4.52e-01 ± 1.18e-02
2024-03-16 10:54:44: 3.68e-01 ± 1.09e-02
2024-03-16 10:54:44: ___________________
2024-03-16 10:54:44: Obtained metrics for PMEmo
2024-03-16 10:54:44: r2, RMSE, MAE
2024-03-16 10:54:44: 6.10e-01 ± 4.63e-02
2024-03-16 10:54:44: 2.49e-01 ± 1.78e-02
2024-03-16 10:54:44: 1.88e-01 ± 1.44e-02
2024-03-16 10:54:44:
--------
2024-03-16 10:59:44: Label: ValMN
2024-03-16 10:59:44: [Clustering]
2024-03-16 10:59:45: [Ended]
2024-03-16 10:59:45: [Clustering]
2024-03-16 10:59:45: [Ended]
2024-03-16 10:59:45: Tuning AutoML
[WARNING] [2024-03-16 11:01:05,760:Client-AutoML(8229):eb1f9ecf-e37b-11ee-811e-b499bab8ef8a] Time limit for a single run is higher than total time limit. Capping the limit for a single run to the total time given to SMAC (17996.086061)
[WARNING] [2024-03-16 11:01:05,760:Client-AutoML(8229):eb1f9ecf-e37b-11ee-811e-b499bab8ef8a] Capping the per_run_time_limit to 8998.0 to have time for a least 2 models in each process.
[WARNING] [2024-03-16 11:01:30,714:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-16 11:01:42,170:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-16 11:01:49,520:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-16 11:01:52,079:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-16 11:02:27,621:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-16 11:02:46,476:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-16 11:04:09,668:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
Needed time for optimizing the model: 18375.023040533066
2024-03-16 16:06:01: Cross-validating best estimator
Needed time for cross-validating the best model: 2936.6234033107758
2024-03-16 16:55:57: ___________________
2024-03-16 16:55:57: Obtained metrics for IADS-E-nomusic
2024-03-16 16:55:57: r2, RMSE, MAE
2024-03-16 16:55:57: 9.88e-02 ± 2.53e-02
2024-03-16 16:55:57: 4.72e-01 ± 1.09e-02
2024-03-16 16:55:57: 3.80e-01 ± 7.30e-03
2024-03-16 16:55:57: ___________________
2024-03-16 16:55:57: Obtained metrics for PMEmo
2024-03-16 16:55:57: r2, RMSE, MAE
2024-03-16 16:55:57: 6.24e-01 ± 4.07e-02
2024-03-16 16:55:57: 2.33e-01 ± 1.00e-02
2024-03-16 16:55:57: 1.71e-01 ± 8.37e-03
2024-03-16 16:55:57:
--------
--------
2024-03-16 17:00:57: Ratio: 1.0IADS-E-nomusic + 1.0PMEmo
2024-03-16 17:00:57: Label: AroMN
2024-03-16 17:00:57: [Clustering]
2024-03-16 17:00:58: [Ended]
2024-03-16 17:00:58: [Clustering]
2024-03-16 17:00:59: [Ended]
2024-03-16 17:00:59: Tuning AutoML
[WARNING] [2024-03-16 17:01:23,028:Client-AutoML(8229):615e579e-e3ae-11ee-811e-b499bab8ef8a] Time limit for a single run is higher than total time limit. Capping the limit for a single run to the total time given to SMAC (17995.886216)
[WARNING] [2024-03-16 17:01:23,028:Client-AutoML(8229):615e579e-e3ae-11ee-811e-b499bab8ef8a] Capping the per_run_time_limit to 8997.0 to have time for a least 2 models in each process.
[WARNING] [2024-03-16 17:01:45,493:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-16 17:02:08,271:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-16 17:02:11,666:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
Needed time for optimizing the model: 18327.565472602844
2024-03-16 22:06:26: Cross-validating best estimator
Needed time for cross-validating the best model: 1632.6093633174896
2024-03-16 22:34:39: ___________________
2024-03-16 22:34:39: Obtained metrics for IADS-E-nomusic
2024-03-16 22:34:39: r2, RMSE, MAE
2024-03-16 22:34:39: 1.51e-01 ± 1.03e-02
2024-03-16 22:34:39: 4.39e-01 ± 1.27e-02
2024-03-16 22:34:39: 3.49e-01 ± 1.27e-02
2024-03-16 22:34:39: ___________________
2024-03-16 22:34:39: Obtained metrics for PMEmo
2024-03-16 22:34:39: r2, RMSE, MAE
2024-03-16 22:34:39: 5.63e-01 ± 4.80e-02
2024-03-16 22:34:39: 2.64e-01 ± 1.96e-02
2024-03-16 22:34:39: 1.99e-01 ± 1.88e-02
2024-03-16 22:34:39:
--------
2024-03-16 22:39:39: Label: ValMN
2024-03-16 22:39:39: [Clustering]
2024-03-16 22:39:40: [Ended]
2024-03-16 22:39:40: [Clustering]
2024-03-16 22:39:41: [Ended]
2024-03-16 22:39:41: Tuning AutoML
[WARNING] [2024-03-16 22:39:50,149:Client-AutoML(8229):b2316e2f-e3dd-11ee-811e-b499bab8ef8a] Time limit for a single run is higher than total time limit. Capping the limit for a single run to the total time given to SMAC (17996.023232)
[WARNING] [2024-03-16 22:39:50,150:Client-AutoML(8229):b2316e2f-e3dd-11ee-811e-b499bab8ef8a] Capping the per_run_time_limit to 8998.0 to have time for a least 2 models in each process.
[WARNING] [2024-03-16 22:40:15,100:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-16 22:40:28,434:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-16 22:40:30,778:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-16 22:40:38,245:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-16 22:40:42,105:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-16 22:40:48,587:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-16 22:41:17,253:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-16 22:41:35,149:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-16 22:41:42,619:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
Needed time for optimizing the model: 18312.771928548813
2024-03-17 03:44:53: Cross-validating best estimator
Needed time for cross-validating the best model: 2800.1159811019897
2024-03-17 04:32:34: ___________________
2024-03-17 04:32:34: Obtained metrics for IADS-E-nomusic
2024-03-17 04:32:34: r2, RMSE, MAE
2024-03-17 04:32:34: 1.49e-01 ± 1.78e-02
2024-03-17 04:32:34: 4.58e-01 ± 1.24e-02
2024-03-17 04:32:34: 3.60e-01 ± 1.30e-02
2024-03-17 04:32:34: ___________________
2024-03-17 04:32:34: Obtained metrics for PMEmo
2024-03-17 04:32:34: r2, RMSE, MAE
2024-03-17 04:32:34: 5.76e-01 ± 3.95e-02
2024-03-17 04:32:34: 2.47e-01 ± 9.26e-03
2024-03-17 04:32:34: 1.85e-01 ± 9.95e-03
2024-03-17 04:32:34:
--------
--------
2024-03-17 04:37:34: Ratio: 1.0PMEmo + 0.0IADS-E-nomusic
2024-03-17 04:37:34: Label: AroMN
2024-03-17 04:37:34: [Clustering]
2024-03-17 04:37:35: [Ended]
2024-03-17 04:37:35: [Clustering]
2024-03-17 04:37:35: [Ended]
2024-03-17 04:37:35: Tuning AutoML
[WARNING] [2024-03-17 04:38:51,721:Client-AutoML(8229):b210bc37-e40f-11ee-811e-b499bab8ef8a] Time limit for a single run is higher than total time limit. Capping the limit for a single run to the total time given to SMAC (17996.840634)
[WARNING] [2024-03-17 04:38:51,722:Client-AutoML(8229):b210bc37-e40f-11ee-811e-b499bab8ef8a] Capping the per_run_time_limit to 8998.0 to have time for a least 2 models in each process.
[WARNING] [2024-03-17 04:39:20,564:Client-EnsembleBuilder] No runs were available to build an ensemble from
Needed time for optimizing the model: 18395.098955869675
2024-03-17 09:44:10: Cross-validating best estimator
2024-03-17 09:51:06: An error has been caught in function 'full_experiment', process 'MainProcess' (1114398), thread 'MainThread' (139646976743232):
Traceback (most recent call last):
File "/home/fedes/.pyenv/versions/3.9.16/lib/python3.9/runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
│ │ └ {'__name__': '__main__', '__doc__': None, '__package__': 'music_sound_emotions', '__loader__': <_frozen_importlib_external.So...
│ └ <code object <module> at 0x7f0216c3ca80, file "/home/fedes/MusicSoundEmotions/music_sound_emotions/experiments.py", line 1>
└ <function _run_code at 0x7f0216f79790>
File "/home/fedes/.pyenv/versions/3.9.16/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
│ └ {'__name__': '__main__', '__doc__': None, '__package__': 'music_sound_emotions', '__loader__': <_frozen_importlib_external.So...
└ <code object <module> at 0x7f0216c3ca80, file "/home/fedes/MusicSoundEmotions/music_sound_emotions/experiments.py", line 1>
File "/home/fedes/MusicSoundEmotions/music_sound_emotions/experiments.py", line 58, in <module>
full_experiment(obj)
│ └ Main(order=('IADS-E', 'PMEmo'), p=0.5, only_automl=True, noised='IADS-E', _Main__complementary_ratios=False)
└ <function full_experiment at 0x7f0216c239d0>
> File "/home/fedes/MusicSoundEmotions/music_sound_emotions/experiments.py", line 36, in full_experiment
obj.tune_and_validate(label)
│ │ └ 'AroMN'
│ └ <function Main.tune_and_validate at 0x7f01b8220820>
└ Main(order=('IADS-E', 'PMEmo'), p=0.5, only_automl=True, noised='IADS-E', _Main__complementary_ratios=False)
File "/home/fedes/MusicSoundEmotions/music_sound_emotions/validation.py", line 216, in tune_and_validate
data1_res, data2_res = cross_validate(
└ <function cross_validate at 0x7f0216778b80>
File "/home/fedes/MusicSoundEmotions/music_sound_emotions/validation.py", line 49, in cross_validate
y_b_cap = model_.predict(X[test_b])
│ │ │ └ array([ 629, 630, 640, 644, 647, 653, 654, 656, 661, 664, 666,
│ │ │ 672, 678, 679, 683, 688, 695, 698, 71...
│ │ └ array([[5.764924e+00, 1.428223e-01, 0.000000e+00, ..., 5.718811e+01,
│ │ 1.023215e+02, 5.711489e+01],
│ │ [6.710737e+0...
│ └ <function AutoSklearnRegressor.predict at 0x7f01ba5fe3a0>
└ AutoSklearnRegressor(ensemble_class=<class 'autosklearn.ensembles.ensemble_selection.EnsembleSelection'>,
...
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/autosklearn/estimators.py", line 1611, in predict
return super().predict(X, batch_size=batch_size, n_jobs=n_jobs)
│ │ └ 1
│ └ None
└ array([[6.775934e+00, 2.312312e-01, 0.000000e+00, ..., 5.574514e+01,
9.711882e+01, 4.710252e+01],
[8.229504e+0...
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/autosklearn/estimators.py", line 798, in predict
return self.automl_.predict(X, batch_size=batch_size, n_jobs=n_jobs)
│ │ │ │ │ └ 1
│ │ │ │ └ None
│ │ │ └ array([[6.775934e+00, 2.312312e-01, 0.000000e+00, ..., 5.574514e+01,
│ │ │ 9.711882e+01, 4.710252e+01],
│ │ │ [8.229504e+0...
│ │ └ <function AutoML.predict at 0x7f01ba5f8550>
│ └ <unprintable AutoMLRegressor object>
└ AutoSklearnRegressor(ensemble_class=<class 'autosklearn.ensembles.ensemble_selection.EnsembleSelection'>,
...
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/autosklearn/automl.py", line 1466, in predict
all_predictions = joblib.Parallel(n_jobs=n_jobs)(
│ │ └ 1
│ └ <class 'joblib.parallel.Parallel'>
└ <module 'joblib' from '/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/joblib/__init__.py'>
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/joblib/parallel.py", line 1088, in __call__
while self.dispatch_one_batch(iterator):
│ │ └ <generator object AutoML.predict.<locals>.<genexpr> at 0x7f0090cd93c0>
│ └ <function Parallel.dispatch_one_batch at 0x7f01d02871f0>
└ Parallel(n_jobs=1)
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/joblib/parallel.py", line 901, in dispatch_one_batch
self._dispatch(tasks)
│ │ └ <joblib.parallel.BatchedCalls object at 0x7f01b79ca7c0>
│ └ <function Parallel._dispatch at 0x7f01d02870d0>
└ Parallel(n_jobs=1)
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/joblib/parallel.py", line 819, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
│ │ │ │ └ <joblib.parallel.BatchCompletionCallBack object at 0x7f01b78ff0a0>
│ │ │ └ <joblib.parallel.BatchedCalls object at 0x7f01b79ca7c0>
│ │ └ <function SequentialBackend.apply_async at 0x7f01d0283160>
│ └ <joblib._parallel_backends.SequentialBackend object at 0x7f01b79cad60>
└ Parallel(n_jobs=1)
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/joblib/_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
│ └ <joblib.parallel.BatchedCalls object at 0x7f01b79ca7c0>
└ <class 'joblib._parallel_backends.ImmediateResult'>
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/joblib/_parallel_backends.py", line 597, in __init__
self.results = batch()
│ └ <joblib.parallel.BatchedCalls object at 0x7f01b79ca7c0>
└ <joblib._parallel_backends.ImmediateResult object at 0x7f01b79ca940>
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/joblib/parallel.py", line 288, in __call__
return [func(*args, **kwargs)
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/joblib/parallel.py", line 288, in <listcomp>
return [func(*args, **kwargs)
│ │ └ {'model': SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'selec...
│ └ ()
└ <function _model_predict at 0x7f01badc5820>
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/autosklearn/automl.py", line 194, in _model_predict
prediction = predict_func(X_)
│ └ array([[6.775934e+00, 2.312312e-01, 0.000000e+00, ..., 5.574514e+01,
│ 9.711882e+01, 4.710252e+01],
│ [8.229504e+0...
└ <bound method SimpleRegressionPipeline.predict of SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', '...
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/autosklearn/pipeline/regression.py", line 109, in predict
y = super().predict(X, batch_size=batch_size)
│ └ None
└ array([[6.775934e+00, 2.312312e-01, 0.000000e+00, ..., 5.574514e+01,
9.711882e+01, 4.710252e+01],
[8.229504e+0...
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/autosklearn/pipeline/base.py", line 182, in predict
return super().predict(X).astype(self._output_dtype)
│ │ └ <class 'numpy.float32'>
│ └ SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'select_percenti...
└ array([[6.775934e+00, 2.312312e-01, 0.000000e+00, ..., 5.574514e+01,
9.711882e+01, 4.710252e+01],
[8.229504e+0...
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/sklearn/utils/metaestimators.py", line 120, in <lambda>
out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)
│ │ │ │ │ │ └ {}
│ │ │ │ │ └ (array([[6.775934e+00, 2.312312e-01, 0.000000e+00, ..., 5.574514e+01,
│ │ │ │ │ 9.711882e+01, 4.710252e+01],
│ │ │ │ │ [8.229504e+...
│ │ │ │ └ SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'select_percenti...
│ │ │ └ <function Pipeline.predict at 0x7f01bad41160>
│ │ └ <sklearn.utils.metaestimators._IffHasAttrDescriptor object at 0x7f01bad39cd0>
│ └ {}
└ (array([[6.775934e+00, 2.312312e-01, 0.000000e+00, ..., 5.574514e+01,
9.711882e+01, 4.710252e+01],
[8.229504e+...
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/sklearn/pipeline.py", line 418, in predict
Xt = transform.transform(Xt)
│ │ └ array([[6.775934e+00, 2.312312e-01, 0.000000e+00, ..., 5.574514e+01,
│ │ 9.711882e+01, 4.710252e+01],
│ │ [8.229504e+0...
│ └ <function DataPreprocessorChoice.transform at 0x7f01ba9d7af0>
└ <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f01b7888f40>
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/autosklearn/pipeline/components/data_preprocessing/__init__.py", line 152, in transform
return self.choice.transform(X)
│ │ │ └ array([[6.775934e+00, 2.312312e-01, 0.000000e+00, ..., 5.574514e+01,
│ │ │ 9.711882e+01, 4.710252e+01],
│ │ │ [8.229504e+0...
│ │ └ <function FeatTypeSplit.transform at 0x7f01ba9d75e0>
│ └ FeatTypeSplit(column_transformer=ColumnTransformer(sparse_threshold=0.0,
│ t...
└ <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f01b7888f40>
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py", line 226, in transform
return self.column_transformer.transform(X)
│ │ │ └ array([[6.775934e+00, 2.312312e-01, 0.000000e+00, ..., 5.574514e+01,
│ │ │ 9.711882e+01, 4.710252e+01],
│ │ │ [8.229504e+0...
│ │ └ <function ColumnTransformer.transform at 0x7f01bacd0b80>
│ └ ColumnTransformer(sparse_threshold=0.0,
│ transformers=[('numerical_transformer',
│ ...
└ FeatTypeSplit(column_transformer=ColumnTransformer(sparse_threshold=0.0,
t...
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py", line 564, in transform
Xs = self._fit_transform(X, None, _transform_one, fitted=True)
│ │ │ └ <function _transform_one at 0x7f01bad41af0>
│ │ └ array([[6.775934e+00, 2.312312e-01, 0.000000e+00, ..., 5.574514e+01,
│ │ 9.711882e+01, 4.710252e+01],
│ │ [8.229504e+0...
│ └ <function ColumnTransformer._fit_transform at 0x7f01bacd09d0>
└ ColumnTransformer(sparse_threshold=0.0,
transformers=[('numerical_transformer',
...
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/sklearn/compose/_column_transformer.py", line 434, in _fit_transform
return Parallel(n_jobs=self.n_jobs)(
│ │ └ None
│ └ ColumnTransformer(sparse_threshold=0.0,
│ transformers=[('numerical_transformer',
│ ...
└ <class 'joblib.parallel.Parallel'>
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/joblib/parallel.py", line 1085, in __call__
if self.dispatch_one_batch(iterator):
│ │ └ <generator object ColumnTransformer._fit_transform.<locals>.<genexpr> at 0x7f0071fac970>
│ └ <function Parallel.dispatch_one_batch at 0x7f01d02871f0>
└ Parallel(n_jobs=1)
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/joblib/parallel.py", line 901, in dispatch_one_batch
self._dispatch(tasks)
│ │ └ <joblib.parallel.BatchedCalls object at 0x7f001afcda90>
│ └ <function Parallel._dispatch at 0x7f01d02870d0>
└ Parallel(n_jobs=1)
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/joblib/parallel.py", line 819, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
│ │ │ │ └ <joblib.parallel.BatchCompletionCallBack object at 0x7f01b79f72b0>
│ │ │ └ <joblib.parallel.BatchedCalls object at 0x7f001afcda90>
│ │ └ <function SequentialBackend.apply_async at 0x7f01d0283160>
│ └ <joblib._parallel_backends.SequentialBackend object at 0x7f001afcd8e0>
└ Parallel(n_jobs=1)
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/joblib/_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
│ └ <joblib.parallel.BatchedCalls object at 0x7f001afcda90>
└ <class 'joblib._parallel_backends.ImmediateResult'>
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/joblib/_parallel_backends.py", line 597, in __init__
self.results = batch()
│ └ <joblib.parallel.BatchedCalls object at 0x7f001afcda90>
└ <joblib._parallel_backends.ImmediateResult object at 0x7f01b79f7f40>
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/joblib/parallel.py", line 288, in __call__
return [func(*args, **kwargs)
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/joblib/parallel.py", line 288, in <listcomp>
return [func(*args, **kwargs)
│ │ └ {'transformer': NumericalPreprocessingPipeline({'imputation:strategy': 'median', 'rescaling:__choice__': 'power_transformer'}...
│ └ ()
└ <sklearn.utils.fixes._FuncWrapper object at 0x7f001afcd5b0>
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/sklearn/utils/fixes.py", line 222, in __call__
return self.function(*args, **kwargs)
│ │ │ └ {'transformer': NumericalPreprocessingPipeline({'imputation:strategy': 'median', 'rescaling:__choice__': 'power_transformer'}...
│ │ └ ()
│ └ <function _transform_one at 0x7f01bad41af0>
└ <sklearn.utils.fixes._FuncWrapper object at 0x7f001afcd5b0>
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/sklearn/pipeline.py", line 733, in _transform_one
res = transformer.transform(X)
│ │ └ array([[6.775934e+00, 2.312312e-01, 0.000000e+00, ..., 5.574514e+01,
│ │ 9.711882e+01, 4.710252e+01],
│ │ [8.229504e+0...
│ └ <property object at 0x7f01bad3fa40>
└ NumericalPreprocessingPipeline({'imputation:strategy': 'median', 'rescaling:__choice__': 'power_transformer'},
dataset_proper...
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/sklearn/pipeline.py", line 560, in _transform
Xt = transform.transform(Xt)
│ │ └ array([[24.7786984 , 0.22144378, 3.33102276, ..., 0.82519865,
│ │ 0.81218497, 0.70723552],
│ │ [34.4252338 , 0.8...
│ └ <function RescalingChoice.transform at 0x7f01bac871f0>
└ <autosklearn.pipeline.components.data_preprocessing.rescaling.RescalingChoice object at 0x7f01efb29eb0>
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/__init__.py", line 94, in transform
return self.choice.transform(X)
│ │ │ └ array([[24.7786984 , 0.22144378, 3.33102276, ..., 0.82519865,
│ │ │ 0.81218497, 0.70723552],
│ │ │ [34.4252338 , 0.8...
│ │ └ <function Rescaling.transform at 0x7f01bac7b3a0>
│ └ <unprintable PowerTransformerComponent object>
└ <autosklearn.pipeline.components.data_preprocessing.rescaling.RescalingChoice object at 0x7f01efb29eb0>
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py", line 36, in transform
transformed_X = self.preprocessor.transform(X)
│ │ │ └ array([[24.7786984 , 0.22144378, 3.33102276, ..., 0.82519865,
│ │ │ 0.81218497, 0.70723552],
│ │ │ [34.4252338 , 0.8...
│ │ └ <function PowerTransformer.transform at 0x7f01bed0fb80>
│ └ PowerTransformer(copy=False)
└ <unprintable PowerTransformerComponent object>
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_data.py", line 3107, in transform
X = self._scaler.transform(X)
│ │ │ └ array([[24.7786984 , 0.22144378, 3.33102276, ..., 0.82519865,
│ │ │ 0.81218497, 0.70723552],
│ │ │ [34.4252338 , 0.8...
│ │ └ <function StandardScaler.transform at 0x7f01bed07550>
│ └ StandardScaler(copy=False)
└ PowerTransformer(copy=False)
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/sklearn/preprocessing/_data.py", line 883, in transform
X = self._validate_data(X, reset=False,
│ │ └ array([[24.7786984 , 0.22144378, 3.33102276, ..., 0.82519865,
│ │ 0.81218497, 0.70723552],
│ │ [34.4252338 , 0.8...
│ └ <function BaseEstimator._validate_data at 0x7f01bf67daf0>
└ StandardScaler(copy=False)
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/sklearn/base.py", line 421, in _validate_data
X = check_array(X, **check_params)
│ │ └ {'accept_sparse': 'csr', 'copy': False, 'estimator': StandardScaler(copy=False), 'dtype': (<class 'numpy.float64'>, <class 'n...
│ └ array([[24.7786984 , 0.22144378, 3.33102276, ..., 0.82519865,
│ 0.81218497, 0.70723552],
│ [34.4252338 , 0.8...
└ <function check_array at 0x7f01bf66cee0>
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/sklearn/utils/validation.py", line 63, in inner_f
return f(*args, **kwargs)
│ │ └ {'accept_sparse': 'csr', 'copy': False, 'estimator': StandardScaler(copy=False), 'dtype': (<class 'numpy.float64'>, <class 'n...
│ └ (array([[24.7786984 , 0.22144378, 3.33102276, ..., 0.82519865,
│ 0.81218497, 0.70723552],
│ [34.4252338 , 0....
└ <function check_array at 0x7f01bf66cdc0>
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/sklearn/utils/validation.py", line 720, in check_array
_assert_all_finite(array,
│ └ array([[24.7786984 , 0.22144378, 3.33102276, ..., 0.82519865,
│ 0.81218497, 0.70723552],
│ [34.4252338 , 0.8...
└ <function _assert_all_finite at 0x7f01bf66c4c0>
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/sklearn/utils/validation.py", line 103, in _assert_all_finite
raise ValueError(
ValueError: Input contains infinity or a value too large for dtype('float64').
--------
2024-03-17 09:56:32: Label: ValMN
2024-03-17 09:56:33: [Clustering]
2024-03-17 09:56:33: [Ended]
2024-03-17 09:56:33: [Clustering]
2024-03-17 09:56:34: [Ended]
2024-03-17 09:56:34: Tuning AutoML
[WARNING] [2024-03-17 09:56:48,624:Client-AutoML(8229):419eeedd-e43c-11ee-811e-b499bab8ef8a] Time limit for a single run is higher than total time limit. Capping the limit for a single run to the total time given to SMAC (17996.655512)
[WARNING] [2024-03-17 09:56:48,625:Client-AutoML(8229):419eeedd-e43c-11ee-811e-b499bab8ef8a] Capping the per_run_time_limit to 8998.0 to have time for a least 2 models in each process.
[WARNING] [2024-03-17 09:57:14,552:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-17 09:57:23,185:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-17 09:57:25,111:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-17 09:57:29,652:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-17 09:57:34,082:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-17 09:57:44,214:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-17 09:58:01,740:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-17 09:58:24,723:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
[WARNING] [2024-03-17 09:58:49,786:Client-EnsembleBuilder] No models better than random - using Dummy losses!
Models besides current dummy model: 0
Dummy models: 1
Needed time for optimizing the model: 18316.314100027084
2024-03-17 15:01:50: Cross-validating best estimator
Needed time for cross-validating the best model: 934.9014627933502
2024-03-17 15:18:25: ___________________
2024-03-17 15:18:25: Obtained metrics for PMEmo
2024-03-17 15:18:25: r2, RMSE, MAE
2024-03-17 15:18:25: 5.39e-01 ± 4.10e-02
2024-03-17 15:18:25: 2.20e-01 ± 1.96e-02
2024-03-17 15:18:25: 1.73e-01 ± 1.55e-02
2024-03-17 15:18:25: ___________________
2024-03-17 15:18:25: Obtained metrics for IADS-E-nomusic
2024-03-17 15:18:25: r2, RMSE, MAE
2024-03-17 15:18:25: 8.70e-02 ± 2.04e-02
2024-03-17 15:18:25: 4.66e-01 ± 1.24e-02
2024-03-17 15:18:25: 3.58e-01 ± 2.48e-02
2024-03-17 15:18:25:
--------
--------
2024-03-17 15:23:25: Ratio: 1.0PMEmo + 0.25IADS-E-nomusic
2024-03-17 15:23:25: Label: AroMN
2024-03-17 15:23:26: [Clustering]
2024-03-17 15:23:26: [Ended]
2024-03-17 15:23:26: [Clustering]
2024-03-17 15:23:27: [Ended]
2024-03-17 15:23:27: Tuning AutoML
[WARNING] [2024-03-17 15:23:52,255:Client-AutoML(8229):ebe9ac80-e469-11ee-811e-b499bab8ef8a] Time limit for a single run is higher than total time limit. Capping the limit for a single run to the total time given to SMAC (17996.259992)
[WARNING] [2024-03-17 15:23:52,256:Client-AutoML(8229):ebe9ac80-e469-11ee-811e-b499bab8ef8a] Capping the per_run_time_limit to 8998.0 to have time for a least 2 models in each process.
[WARNING] [2024-03-17 15:24:18,519:Client-EnsembleBuilder] No runs were available to build an ensemble from
[WARNING] [2024-03-17 15:24:29,033:Client-EnsembleBuilder] No runs were available to build an ensemble from
Needed time for optimizing the model: 18346.40551829338
2024-03-17 20:29:13: Cross-validating best estimator
Needed time for cross-validating the best model: 3058.678600549698
2024-03-17 21:21:12: ___________________
2024-03-17 21:21:12: Obtained metrics for PMEmo
2024-03-17 21:21:12: r2, RMSE, MAE
2024-03-17 21:21:12: 7.22e-01 ± 2.59e-02
2024-03-17 21:21:12: 1.94e-01 ± 8.77e-03
2024-03-17 21:21:12: 1.48e-01 ± 1.20e-02
2024-03-17 21:21:12: ___________________
2024-03-17 21:21:12: Obtained metrics for IADS-E-nomusic
2024-03-17 21:21:12: r2, RMSE, MAE
2024-03-17 21:21:12: 3.80e-02 ± 8.16e-02
2024-03-17 21:21:12: 4.58e-01 ± 1.94e-02
2024-03-17 21:21:12: 3.54e-01 ± 1.39e-02
2024-03-17 21:21:12:
--------
2024-03-17 21:26:12: Label: ValMN
2024-03-17 21:26:12: [Clustering]
2024-03-17 21:26:13: [Ended]
2024-03-17 21:26:13: [Clustering]
2024-03-17 21:26:14: [Ended]
2024-03-17 21:26:14: Tuning AutoML
[WARNING] [2024-03-17 21:26:21,628:Client-AutoML(8229):99f3814f-e49c-11ee-811e-b499bab8ef8a] Time limit for a single run is higher than total time limit. Capping the limit for a single run to the total time given to SMAC (17996.240389)
[WARNING] [2024-03-17 21:26:21,628:Client-AutoML(8229):99f3814f-e49c-11ee-811e-b499bab8ef8a] Capping the per_run_time_limit to 8998.0 to have time for a least 2 models in each process.
[WARNING] [2024-03-17 21:26:47,365:Client-EnsembleBuilder] No runs were available to build an ensemble from
Needed time for optimizing the model: 18321.13203382492
2024-03-18 02:31:35: Cross-validating best estimator
Needed time for cross-validating the best model: 8097.185059547424
2024-03-18 04:47:32: ___________________
2024-03-18 04:47:32: Obtained metrics for PMEmo
2024-03-18 04:47:32: r2, RMSE, MAE
2024-03-18 04:47:32: 5.62e-01 ± 3.23e-02
2024-03-18 04:47:32: 2.14e-01 ± 1.58e-02
2024-03-18 04:47:32: 1.68e-01 ± 1.21e-02
2024-03-18 04:47:32: ___________________
2024-03-18 04:47:32: Obtained metrics for IADS-E-nomusic
2024-03-18 04:47:32: r2, RMSE, MAE
2024-03-18 04:47:32: 4.07e-02 ± 5.74e-02
2024-03-18 04:47:32: 4.77e-01 ± 1.58e-02
2024-03-18 04:47:32: 3.71e-01 ± 2.43e-02
2024-03-18 04:47:32:
--------
--------
2024-03-18 04:52:32: Ratio: 1.0PMEmo + 0.5IADS-E-nomusic
2024-03-18 04:52:32: Label: AroMN
2024-03-18 04:52:33: [Clustering]
2024-03-18 04:52:33: [Ended]
2024-03-18 04:52:33: [Clustering]
2024-03-18 04:52:34: [Ended]
2024-03-18 04:52:34: Tuning AutoML
[WARNING] [2024-03-18 04:54:33,813:Client-AutoML(8229):f446901d-e4da-11ee-811e-b499bab8ef8a] Time limit for a single run is higher than total time limit. Capping the limit for a single run to the total time given to SMAC (17995.878639)
[WARNING] [2024-03-18 04:54:33,814:Client-AutoML(8229):f446901d-e4da-11ee-811e-b499bab8ef8a] Capping the per_run_time_limit to 8997.0 to have time for a least 2 models in each process.
[WARNING] [2024-03-18 04:55:04,478:Client-EnsembleBuilder] No runs were available to build an ensemble from
Needed time for optimizing the model: 18437.10965681076
2024-03-18 09:59:51: Cross-validating best estimator
Needed time for cross-validating the best model: 6309.36469578743
2024-03-18 11:46:01: ___________________
2024-03-18 11:46:01: Obtained metrics for PMEmo
2024-03-18 11:46:01: r2, RMSE, MAE
2024-03-18 11:46:01: 7.48e-01 ± 3.08e-02
2024-03-18 11:46:01: 1.85e-01 ± 7.79e-03
2024-03-18 11:46:01: 1.39e-01 ± 8.36e-03
2024-03-18 11:46:01: ___________________
2024-03-18 11:46:01: Obtained metrics for IADS-E-nomusic
2024-03-18 11:46:01: r2, RMSE, MAE
2024-03-18 11:46:01: 4.11e-02 ± 3.28e-02
2024-03-18 11:46:01: 4.58e-01 ± 1.01e-02
2024-03-18 11:46:01: 3.54e-01 ± 1.16e-02
2024-03-18 11:46:01:
--------
2024-03-18 11:51:01: Label: ValMN
2024-03-18 11:51:01: [Clustering]
2024-03-18 11:51:02: [Ended]
2024-03-18 11:51:02: [Clustering]
2024-03-18 11:51:02: [Ended]
2024-03-18 11:51:02: Tuning AutoML
[WARNING] [2024-03-18 11:55:47,144:Client-AutoML(8229):69efcadd-e515-11ee-811e-b499bab8ef8a] Time limit for a single run is higher than total time limit. Capping the limit for a single run to the total time given to SMAC (17996.231049)
[WARNING] [2024-03-18 11:55:47,144:Client-AutoML(8229):69efcadd-e515-11ee-811e-b499bab8ef8a] Capping the per_run_time_limit to 8998.0 to have time for a least 2 models in each process.
[WARNING] [2024-03-18 11:56:15,093:Client-EnsembleBuilder] No runs were available to build an ensemble from
Needed time for optimizing the model: 18603.255586862564
2024-03-18 17:01:06: Cross-validating best estimator
2024-03-18 17:20:47: An error has been caught in function 'full_experiment', process 'MainProcess' (1114398), thread 'MainThread' (139646976743232):
Traceback (most recent call last):
File "/home/fedes/.pyenv/versions/3.9.16/lib/python3.9/runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
│ │ └ {'__name__': '__main__', '__doc__': None, '__package__': 'music_sound_emotions', '__loader__': <_frozen_importlib_external.So...
│ └ <code object <module> at 0x7f0216c3ca80, file "/home/fedes/MusicSoundEmotions/music_sound_emotions/experiments.py", line 1>
└ <function _run_code at 0x7f0216f79790>
File "/home/fedes/.pyenv/versions/3.9.16/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
│ └ {'__name__': '__main__', '__doc__': None, '__package__': 'music_sound_emotions', '__loader__': <_frozen_importlib_external.So...
└ <code object <module> at 0x7f0216c3ca80, file "/home/fedes/MusicSoundEmotions/music_sound_emotions/experiments.py", line 1>
File "/home/fedes/MusicSoundEmotions/music_sound_emotions/experiments.py", line 58, in <module>
full_experiment(obj)
│ └ Main(order=('IADS-E', 'PMEmo'), p=0.5, only_automl=True, noised='IADS-E', _Main__complementary_ratios=False)
└ <function full_experiment at 0x7f0216c239d0>
> File "/home/fedes/MusicSoundEmotions/music_sound_emotions/experiments.py", line 36, in full_experiment
obj.tune_and_validate(label)
│ │ └ 'ValMN'
│ └ <function Main.tune_and_validate at 0x7f01b8220820>
└ Main(order=('IADS-E', 'PMEmo'), p=0.5, only_automl=True, noised='IADS-E', _Main__complementary_ratios=False)
File "/home/fedes/MusicSoundEmotions/music_sound_emotions/validation.py", line 216, in tune_and_validate
data1_res, data2_res = cross_validate(
└ <function cross_validate at 0x7f0216778b80>
File "/home/fedes/MusicSoundEmotions/music_sound_emotions/validation.py", line 49, in cross_validate
y_b_cap = model_.predict(X[test_b])
│ │ │ └ array([ 629, 630, 640, 644, 647, 653, 654, 656, 661, 664, 666,
│ │ │ 672, 678, 679, 683, 688, 695, 698, 71...
│ │ └ array([[5.764924e+00, 1.428223e-01, 0.000000e+00, ..., 5.718811e+01,
│ │ 1.023215e+02, 5.711489e+01],
│ │ [6.710737e+0...
│ └ <function AutoSklearnRegressor.predict at 0x7f01ba5fe3a0>
└ AutoSklearnRegressor(ensemble_class=<class 'autosklearn.ensembles.ensemble_selection.EnsembleSelection'>,
...
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/autosklearn/estimators.py", line 1611, in predict
return super().predict(X, batch_size=batch_size, n_jobs=n_jobs)
│ │ └ 1
│ └ None
└ array([[6.775934e+00, 2.312312e-01, 0.000000e+00, ..., 5.574514e+01,
9.711882e+01, 4.710252e+01],
[8.229504e+0...
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/autosklearn/estimators.py", line 798, in predict
return self.automl_.predict(X, batch_size=batch_size, n_jobs=n_jobs)
│ │ │ │ │ └ 1
│ │ │ │ └ None
│ │ │ └ array([[6.775934e+00, 2.312312e-01, 0.000000e+00, ..., 5.574514e+01,
│ │ │ 9.711882e+01, 4.710252e+01],
│ │ │ [8.229504e+0...
│ │ └ <function AutoML.predict at 0x7f01ba5f8550>
│ └ <unprintable AutoMLRegressor object>
└ AutoSklearnRegressor(ensemble_class=<class 'autosklearn.ensembles.ensemble_selection.EnsembleSelection'>,
...
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/autosklearn/automl.py", line 1466, in predict
all_predictions = joblib.Parallel(n_jobs=n_jobs)(
│ │ └ 1
│ └ <class 'joblib.parallel.Parallel'>
└ <module 'joblib' from '/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/joblib/__init__.py'>
File "/home/fedes/MusicSoundEmotions/.venv/lib/python3.9/site-packages/joblib/parallel.py", line 1088, in __call__
while self.dispatch_one_batch(iterator):
│ │ └ <generator object AutoML.predict.<locals>.<genexpr> at 0x7f007a1ca7b0>