2025-11-06 21:29:29.070958: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:29:29.082629: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762460969.096559 1801386 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762460969.100872 1801386 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762460969.111409 1801386 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762460969.111430 1801386 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762460969.111432 1801386 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762460969.111434 1801386 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:29:29.114578: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/tune/impl/tuner_internal.py:144: RayDeprecationWarning: The `RunConfig` class should be imported from `ray.tune` when passing it to the Tuner. Please update your imports. See this issue for more context and migration options: https://github.com/ray-project/ray/issues/49454. Disable these warnings by setting the environment variable: RAY_TRAIN_ENABLE_V2_MIGRATION_WARNINGS=0
  _log_deprecation_warning(
2025-11-06 21:29:34,075	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-06 21:29:34,740	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-06 21:29:34,803	INFO trial.py:182 -- Creating a new dirname dir_4ece9_dbe8 because trial dirname 'dir_4ece9' already exists.
2025-11-06 21:29:34,807	INFO trial.py:182 -- Creating a new dirname dir_4ece9_8f26 because trial dirname 'dir_4ece9' already exists.
2025-11-06 21:29:34,809	INFO trial.py:182 -- Creating a new dirname dir_4ece9_dc12 because trial dirname 'dir_4ece9' already exists.
2025-11-06 21:29:34,813	INFO trial.py:182 -- Creating a new dirname dir_4ece9_eef1 because trial dirname 'dir_4ece9' already exists.
2025-11-06 21:29:34,815	INFO trial.py:182 -- Creating a new dirname dir_4ece9_dadf because trial dirname 'dir_4ece9' already exists.
2025-11-06 21:29:34,817	INFO trial.py:182 -- Creating a new dirname dir_4ece9_e66b because trial dirname 'dir_4ece9' already exists.
2025-11-06 21:29:34,819	INFO trial.py:182 -- Creating a new dirname dir_4ece9_63d8 because trial dirname 'dir_4ece9' already exists.
2025-11-06 21:29:34,821	INFO trial.py:182 -- Creating a new dirname dir_4ece9_bfdc because trial dirname 'dir_4ece9' already exists.
2025-11-06 21:29:34,823	INFO trial.py:182 -- Creating a new dirname dir_4ece9_f029 because trial dirname 'dir_4ece9' already exists.
2025-11-06 21:29:34,826	INFO trial.py:182 -- Creating a new dirname dir_4ece9_761f because trial dirname 'dir_4ece9' already exists.
2025-11-06 21:29:34,828	INFO trial.py:182 -- Creating a new dirname dir_4ece9_bf40 because trial dirname 'dir_4ece9' already exists.
2025-11-06 21:29:34,831	INFO trial.py:182 -- Creating a new dirname dir_4ece9_7a32 because trial dirname 'dir_4ece9' already exists.
2025-11-06 21:29:34,835	INFO trial.py:182 -- Creating a new dirname dir_4ece9_fdcc because trial dirname 'dir_4ece9' already exists.
2025-11-06 21:29:34,840	INFO trial.py:182 -- Creating a new dirname dir_4ece9_45bb because trial dirname 'dir_4ece9' already exists.
2025-11-06 21:29:34,848	INFO trial.py:182 -- Creating a new dirname dir_4ece9_c252 because trial dirname 'dir_4ece9' already exists.
2025-11-06 21:29:34,852	INFO trial.py:182 -- Creating a new dirname dir_4ece9_47fd because trial dirname 'dir_4ece9' already exists.
2025-11-06 21:29:34,856	INFO trial.py:182 -- Creating a new dirname dir_4ece9_446f because trial dirname 'dir_4ece9' already exists.
2025-11-06 21:29:34,862	INFO trial.py:182 -- Creating a new dirname dir_4ece9_1620 because trial dirname 'dir_4ece9' already exists.
2025-11-06 21:29:34,869	INFO trial.py:182 -- Creating a new dirname dir_4ece9_0943 because trial dirname 'dir_4ece9' already exists.
1 GPU(s) detected and VRAM set to crossover mode..
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Training a non-convolutional model.
Se lanza la búsqueda de hiperparámetros óptimos del modelo
╭──────────────────────────────────────────────────────────────────────╮
│ Configuration for experiment     BalancedRF_hyperparameters_tuning   │
├──────────────────────────────────────────────────────────────────────┤
│ Search algorithm                 BasicVariantGenerator               │
│ Scheduler                        AsyncHyperBandScheduler             │
│ Number of trials                 20                                  │
╰──────────────────────────────────────────────────────────────────────╯

View detailed results here: /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_BRF_acc_superclasses_CPA_METs/BalancedRF_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-06_21-29-33_363682_1801386/artifacts/2025-11-06_21-29-34/BalancedRF_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-06 21:29:35. Total running time: 0s
Logical resource usage: 14.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       n_estimators     max_depth     min_samples_split     min_samples_leaf   max_features       random_state │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_4ece9    PENDING               489             5                    28                   14   sqrt                       8461 │
│ trial_4ece9    PENDING               461             7                    31                   17   0.3                        6180 │
│ trial_4ece9    PENDING               319             6                    28                   19   sqrt                       3970 │
│ trial_4ece9    PENDING               431             5                    39                   19   sqrt                       2075 │
│ trial_4ece9    PENDING               448             7                    56                   15   0.3                        1638 │
│ trial_4ece9    PENDING               292             6                    38                   17   0.3                        3025 │
│ trial_4ece9    PENDING               396             5                    21                   27   sqrt                       2728 │
│ trial_4ece9    PENDING               430             6                    56                   20   0.3                         701 │
│ trial_4ece9    PENDING               368             5                    40                   16   0.3                         473 │
│ trial_4ece9    PENDING               218             6                    37                   27   sqrt                       9792 │
│ trial_4ece9    PENDING               251             6                    43                   22   0.3                        1102 │
│ trial_4ece9    PENDING               226             6                    35                   21   sqrt                        550 │
│ trial_4ece9    PENDING               329             7                    44                   16   sqrt                       5343 │
│ trial_4ece9    PENDING               265             6                    49                   14   0.3                        1033 │
│ trial_4ece9    PENDING               255             7                    48                   16   sqrt                       1971 │
│ trial_4ece9    PENDING               474             5                    26                   13   sqrt                       2718 │
│ trial_4ece9    PENDING               204             7                    23                   27   0.3                        8090 │
│ trial_4ece9    PENDING               253             6                    54                   21   0.3                        3995 │
│ trial_4ece9    PENDING               382             7                    55                   15   sqrt                        216 │
│ trial_4ece9    PENDING               495             6                    23                   22   sqrt                       1555 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_4ece9 started with configuration:
╭──────────────────────────────────╮
│ Trial trial_4ece9 config         │
├──────────────────────────────────┤
│ max_depth                      5 │
│ max_features                 0.3 │
│ min_samples_leaf              16 │
│ min_samples_split             40 │
│ n_estimators                 368 │
│ random_state                 473 │
╰──────────────────────────────────╯
Trial trial_4ece9 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_4ece9 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               17 │
│ min_samples_split              31 │
│ n_estimators                  461 │
│ random_state                 6180 │
╰───────────────────────────────────╯
Trial trial_4ece9 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_4ece9 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               27 │
│ min_samples_split              37 │
│ n_estimators                  218 │
│ random_state                 9792 │
╰───────────────────────────────────╯
Trial trial_4ece9 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_4ece9 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               19 │
│ min_samples_split              39 │
│ n_estimators                  431 │
│ random_state                 2075 │
╰───────────────────────────────────╯
Trial trial_4ece9 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_4ece9 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               27 │
│ min_samples_split              21 │
│ n_estimators                  396 │
│ random_state                 2728 │
╰───────────────────────────────────╯
Trial trial_4ece9 started with configuration:
╭──────────────────────────────────╮
│ Trial trial_4ece9 config         │
├──────────────────────────────────┤
│ max_depth                      6 │
│ max_features                 0.3 │
│ min_samples_leaf              20 │
│ min_samples_split             56 │
│ n_estimators                 430 │
│ random_state                 701 │
╰──────────────────────────────────╯
Trial trial_4ece9 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_4ece9 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               14 │
│ min_samples_split              28 │
│ n_estimators                  489 │
│ random_state                 8461 │
╰───────────────────────────────────╯
Trial trial_4ece9 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_4ece9 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               27 │
│ min_samples_split              23 │
│ n_estimators                  204 │
│ random_state                 8090 │
╰───────────────────────────────────╯
Trial trial_4ece9 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_4ece9 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               21 │
│ min_samples_split              35 │
│ n_estimators                  226 │
│ random_state                  550 │
╰───────────────────────────────────╯
Trial trial_4ece9 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_4ece9 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               14 │
│ min_samples_split              49 │
│ n_estimators                  265 │
│ random_state                 1033 │
╰───────────────────────────────────╯
Trial trial_4ece9 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_4ece9 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               19 │
│ min_samples_split              28 │
│ n_estimators                  319 │
│ random_state                 3970 │
╰───────────────────────────────────╯
Trial trial_4ece9 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_4ece9 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               15 │
│ min_samples_split              56 │
│ n_estimators                  448 │
│ random_state                 1638 │
╰───────────────────────────────────╯
Trial trial_4ece9 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_4ece9 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               13 │
│ min_samples_split              26 │
│ n_estimators                  474 │
│ random_state                 2718 │
╰───────────────────────────────────╯
Trial trial_4ece9 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_4ece9 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               22 │
│ min_samples_split              43 │
│ n_estimators                  251 │
│ random_state                 1102 │
╰───────────────────────────────────╯
Trial trial_4ece9 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_4ece9 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               21 │
│ min_samples_split              54 │
│ n_estimators                  253 │
│ random_state                 3995 │
╰───────────────────────────────────╯
Trial trial_4ece9 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_4ece9 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               16 │
│ min_samples_split              48 │
│ n_estimators                  255 │
│ random_state                 1971 │
╰───────────────────────────────────╯
Trial trial_4ece9 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_4ece9 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               17 │
│ min_samples_split              38 │
│ n_estimators                  292 │
│ random_state                 3025 │
╰───────────────────────────────────╯
Trial trial_4ece9 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_4ece9 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               22 │
│ min_samples_split              23 │
│ n_estimators                  495 │
│ random_state                 1555 │
╰───────────────────────────────────╯
Trial trial_4ece9 started with configuration:
[36m(train_brf_ray_tune pid=1803057)[0m 2025-11-06 21:29:38.096541: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
[36m(train_brf_ray_tune pid=1803058)[0m 2025-11-06 21:29:38.137156: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
[36m(train_brf_ray_tune pid=1803058)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_brf_ray_tune pid=1803058)[0m E0000 00:00:1762460978.164206 1804198 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
[36m(train_brf_ray_tune pid=1803058)[0m E0000 00:00:1762460978.172941 1804198 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
[36m(train_brf_ray_tune pid=1803058)[0m W0000 00:00:1762460978.191882 1804198 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
[36m(train_brf_ray_tune pid=1803058)[0m W0000 00:00:1762460978.191913 1804198 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
[36m(train_brf_ray_tune pid=1803058)[0m W0000 00:00:1762460978.191916 1804198 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
[36m(train_brf_ray_tune pid=1803058)[0m W0000 00:00:1762460978.191919 1804198 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
[36m(train_brf_ray_tune pid=1803058)[0m 2025-11-06 21:29:38.197609: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
[36m(train_brf_ray_tune pid=1803058)[0m To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
[36m(train_brf_ray_tune pid=1803057)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
[36m(train_brf_ray_tune pid=1803057)[0m   return fit_method(estimator, *args, **kwargs)
[36m(train_brf_ray_tune pid=1803057)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=1803058)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[36m(train_brf_ray_tune pid=1803054)[0m 2025-11-06 21:29:38.463065: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.[32m [repeated 19x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.)[0m
[36m(train_brf_ray_tune pid=1803054)[0m 2025-11-06 21:29:38.485071: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=1803055)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=1803055)[0m E0000 00:00:1762460978.548328 1804317 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=1803055)[0m E0000 00:00:1762460978.556454 1804317 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=1803055)[0m W0000 00:00:1762460978.575928 1804317 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.[32m [repeated 76x across cluster][0m
[36m(train_brf_ray_tune pid=1803055)[0m 2025-11-06 21:29:38.582156: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=1803055)[0m To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=1803051)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=1803051)[0m   return fit_method(estimator, *args, **kwargs)[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=1803051)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=1803052)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    7.6s[32m [repeated 20x across cluster][0m
[36m(train_brf_ray_tune pid=1803057)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   13.0s[32m [repeated 10x across cluster][0m
[36m(train_brf_ray_tune pid=1803058)[0m [Parallel(n_jobs=-1)]: Done 218 out of 218 | elapsed:   13.4s finished
[36m(train_brf_ray_tune pid=1803058)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=1803058)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454
[36m(train_brf_ray_tune pid=1803058)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=1803013)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.6s[32m [repeated 21x across cluster][0m
[36m(train_brf_ray_tune pid=1803052)[0m [Parallel(n_jobs=-1)]: Done 396 out of 396 | elapsed:   18.8s finished[32m [repeated 9x across cluster][0m
[36m(train_brf_ray_tune pid=1803013)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 5x across cluster][0m
[36m(train_brf_ray_tune pid=1803013)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=1803013)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
╭───────────────────────────────────╮
│ Trial trial_4ece9 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               16 │
│ min_samples_split              44 │
│ n_estimators                  329 │
│ random_state                 5343 │
╰───────────────────────────────────╯
Trial trial_4ece9 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_4ece9 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               15 │
│ min_samples_split              55 │
│ n_estimators                  382 │
│ random_state                  216 │
╰───────────────────────────────────╯

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:29:57. Total running time: 22s
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             19.3308 │
│ time_total_s                 19.3308 │
│ training_iteration                 1 │
│ test_accuracy                0.61618 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:29:57. Total running time: 22s

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:29:58. Total running time: 23s
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             20.2428 │
│ time_total_s                 20.2428 │
│ training_iteration                 1 │
│ test_accuracy                0.61748 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:29:58. Total running time: 23s

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:29:59. Total running time: 24s
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             21.4327 │
│ time_total_s                 21.4327 │
│ training_iteration                 1 │
│ test_accuracy                0.62006 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:29:59. Total running time: 24s

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:30:02. Total running time: 28s
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             25.0558 │
│ time_total_s                 25.0558 │
│ training_iteration                 1 │
│ test_accuracy                0.61359 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:30:02. Total running time: 28s

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:30:02. Total running time: 28s
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             25.1971 │
│ time_total_s                 25.1971 │
│ training_iteration                 1 │
│ test_accuracy                0.61359 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:30:02. Total running time: 28s

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:30:03. Total running time: 28s
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             25.6977 │
│ time_total_s                 25.6977 │
│ training_iteration                 1 │
│ test_accuracy                 0.6246 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:30:03. Total running time: 28s

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:30:04. Total running time: 29s
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             26.4958 │
│ time_total_s                 26.4958 │
│ training_iteration                 1 │
│ test_accuracy                0.62265 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:30:04. Total running time: 29s

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:30:04. Total running time: 29s
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              26.518 │
│ time_total_s                  26.518 │
│ training_iteration                 1 │
│ test_accuracy                0.61748 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:30:04. Total running time: 29s

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:30:04. Total running time: 29s
[36m(train_brf_ray_tune pid=1803070)[0m [Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.2s[32m [repeated 49x across cluster][0m
[36m(train_brf_ray_tune pid=1803070)[0m [Parallel(n_jobs=20)]: Done 474 out of 474 | elapsed:    0.2s finished[32m [repeated 33x across cluster][0m
[36m(train_brf_ray_tune pid=1803057)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 25x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             26.7849 │
│ time_total_s                 26.7849 │
│ training_iteration                 1 │
│ test_accuracy                0.61553 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:30:04. Total running time: 29s

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:30:04. Total running time: 29s
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             26.7785 │
│ time_total_s                 26.7785 │
│ training_iteration                 1 │
│ test_accuracy                0.62201 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:30:04. Total running time: 29s

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:30:04. Total running time: 30s
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             26.7519 │
│ time_total_s                 26.7519 │
│ training_iteration                 1 │
│ test_accuracy                0.62589 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:30:04. Total running time: 30s

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:30:04. Total running time: 30s
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             26.9887 │
│ time_total_s                 26.9887 │
│ training_iteration                 1 │
│ test_accuracy                0.61812 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:30:04. Total running time: 30s

Trial status: 8 RUNNING | 12 TERMINATED
Current time: 2025-11-06 21:30:05. Total running time: 30s
Logical resource usage: 8.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         n_estimators     max_depth     min_samples_split     min_samples_leaf   max_features       random_state     iter     total time (s)     test_accuracy │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_4ece9    RUNNING                 489             5                    28                   14   sqrt                       8461                                               │
│ trial_4ece9    RUNNING                 461             7                    31                   17   0.3                        6180                                               │
│ trial_4ece9    RUNNING                 448             7                    56                   15   0.3                        1638                                               │
│ trial_4ece9    RUNNING                 430             6                    56                   20   0.3                         701                                               │
│ trial_4ece9    RUNNING                 368             5                    40                   16   0.3                         473                                               │
│ trial_4ece9    RUNNING                 474             5                    26                   13   sqrt                       2718                                               │
│ trial_4ece9    RUNNING                 382             7                    55                   15   sqrt                        216                                               │
│ trial_4ece9    RUNNING                 495             6                    23                   22   sqrt                       1555                                               │
│ trial_4ece9    TERMINATED              319             6                    28                   19   sqrt                       3970        1            25.0558          0.613592 │
│ trial_4ece9    TERMINATED              431             5                    39                   19   sqrt                       2075        1            26.7849          0.615534 │
│ trial_4ece9    TERMINATED              292             6                    38                   17   0.3                        3025        1            26.9887          0.618123 │
│ trial_4ece9    TERMINATED              396             5                    21                   27   sqrt                       2728        1            25.1971          0.613592 │
│ trial_4ece9    TERMINATED              218             6                    37                   27   sqrt                       9792        1            19.3308          0.616181 │
│ trial_4ece9    TERMINATED              251             6                    43                   22   0.3                        1102        1            26.4958          0.622654 │
│ trial_4ece9    TERMINATED              226             6                    35                   21   sqrt                        550        1            20.2428          0.617476 │
│ trial_4ece9    TERMINATED              329             7                    44                   16   sqrt                       5343        1            26.7519          0.62589  │
│ trial_4ece9    TERMINATED              265             6                    49                   14   0.3                        1033        1            26.7785          0.622006 │
│ trial_4ece9    TERMINATED              255             7                    48                   16   sqrt                       1971        1            21.4327          0.620065 │
│ trial_4ece9    TERMINATED              204             7                    23                   27   0.3                        8090        1            25.6977          0.624595 │
│ trial_4ece9    TERMINATED              253             6                    54                   21   0.3                        3995        1            26.518           0.617476 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:30:05. Total running time: 30s
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             27.0351 │
│ time_total_s                 27.0351 │
│ training_iteration                 1 │
│ test_accuracy                0.62589 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:30:05. Total running time: 30s

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:30:05. Total running time: 30s
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             27.7801 │
│ time_total_s                 27.7801 │
│ training_iteration                 1 │
│ test_accuracy                0.61036 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:30:05. Total running time: 30s

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:30:06. Total running time: 31s
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             28.4565 │
│ time_total_s                 28.4565 │
│ training_iteration                 1 │
│ test_accuracy                0.61553 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:30:06. Total running time: 31s

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:30:06. Total running time: 31s
[36m(train_brf_ray_tune pid=1803057)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 13x across cluster][0m
[36m(train_brf_ray_tune pid=1803057)[0m   _log_deprecation_warning([32m [repeated 13x across cluster][0m
2025-11-06 21:30:08,568	INFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_BRF_acc_superclasses_CPA_METs/BalancedRF_hyperparameters_tuning' in 0.0082s.
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             28.8569 │
│ time_total_s                 28.8569 │
│ training_iteration                 1 │
│ test_accuracy                0.61294 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:30:06. Total running time: 31s

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:30:07. Total running time: 32s
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             29.2929 │
│ time_total_s                 29.2929 │
│ training_iteration                 1 │
│ test_accuracy                0.61553 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:30:07. Total running time: 32s

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:30:07. Total running time: 33s
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             30.1696 │
│ time_total_s                 30.1696 │
│ training_iteration                 1 │
│ test_accuracy                0.61683 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:30:07. Total running time: 33s

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:30:08. Total running time: 33s
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             30.5538 │
│ time_total_s                 30.5538 │
│ training_iteration                 1 │
│ test_accuracy                0.62977 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:30:08. Total running time: 33s

Trial trial_4ece9 finished iteration 1 at 2025-11-06 21:30:08. Total running time: 33s
╭──────────────────────────────────────╮
│ Trial trial_4ece9 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             30.8659 │
│ time_total_s                 30.8659 │
│ training_iteration                 1 │
│ test_accuracy                 0.6246 │
╰──────────────────────────────────────╯

Trial trial_4ece9 completed after 1 iterations at 2025-11-06 21:30:08. Total running time: 33s

Trial status: 20 TERMINATED
Current time: 2025-11-06 21:30:08. Total running time: 33s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.0s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         n_estimators     max_depth     min_samples_split     min_samples_leaf   max_features       random_state     iter     total time (s)     test_accuracy │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_4ece9    TERMINATED              489             5                    28                   14   sqrt                       8461        1            28.4565          0.615534 │
│ trial_4ece9    TERMINATED              461             7                    31                   17   0.3                        6180        1            30.8659          0.624595 │
│ trial_4ece9    TERMINATED              319             6                    28                   19   sqrt                       3970        1            25.0558          0.613592 │
│ trial_4ece9    TERMINATED              431             5                    39                   19   sqrt                       2075        1            26.7849          0.615534 │
│ trial_4ece9    TERMINATED              448             7                    56                   15   0.3                        1638        1            30.5538          0.629773 │
│ trial_4ece9    TERMINATED              292             6                    38                   17   0.3                        3025        1            26.9887          0.618123 │
│ trial_4ece9    TERMINATED              396             5                    21                   27   sqrt                       2728        1            25.1971          0.613592 │
│ trial_4ece9    TERMINATED              430             6                    56                   20   0.3                         701        1            30.1696          0.616828 │
│ trial_4ece9    TERMINATED              368             5                    40                   16   0.3                         473        1            28.8569          0.612945 │
│ trial_4ece9    TERMINATED              218             6                    37                   27   sqrt                       9792        1            19.3308          0.616181 │
│ trial_4ece9    TERMINATED              251             6                    43                   22   0.3                        1102        1            26.4958          0.622654 │
│ trial_4ece9    TERMINATED              226             6                    35                   21   sqrt                        550        1            20.2428          0.617476 │
│ trial_4ece9    TERMINATED              329             7                    44                   16   sqrt                       5343        1            26.7519          0.62589  │
│ trial_4ece9    TERMINATED              265             6                    49                   14   0.3                        1033        1            26.7785          0.622006 │
│ trial_4ece9    TERMINATED              255             7                    48                   16   sqrt                       1971        1            21.4327          0.620065 │
│ trial_4ece9    TERMINATED              474             5                    26                   13   sqrt                       2718        1            27.7801          0.610356 │
│ trial_4ece9    TERMINATED              204             7                    23                   27   0.3                        8090        1            25.6977          0.624595 │
│ trial_4ece9    TERMINATED              253             6                    54                   21   0.3                        3995        1            26.518           0.617476 │
│ trial_4ece9    TERMINATED              382             7                    55                   15   sqrt                        216        1            27.0351          0.62589  │
│ trial_4ece9    TERMINATED              495             6                    23                   22   sqrt                       1555        1            29.2929          0.615534 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'n_estimators': 448, 'max_depth': 7, 'min_samples_split': 56, 'min_samples_leaf': 15, 'max_features': 0.3, 'random_state': 1638}
Saved model to disk
[36m(train_brf_ray_tune pid=1803014)[0m [Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s[32m [repeated 31x across cluster][0m
[36m(train_brf_ray_tune pid=1803014)[0m [Parallel(n_jobs=20)]: Done 461 out of 461 | elapsed:    0.1s finished[32m [repeated 17x across cluster][0m
[36m(train_brf_ray_tune pid=1803014)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 9x across cluster][0m
[36m(train_brf_ray_tune pid=1803014)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=1803014)[0m   _log_deprecation_warning([32m [repeated 4x across cluster][0m
2025-11-06 21:30:21.590113: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:30:21.601302: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461021.614416 1806418 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461021.618600 1806418 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461021.628475 1806418 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461021.628528 1806418 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461021.628530 1806418 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461021.628532 1806418 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:30:21.631723: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
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Training a non-convolutional model.
Saved model to disk

=== EJECUCIÓN 1 ===

--- TRAIN (ejecución 1) ---

--- TEST (ejecución 1) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.59 [%]
Global accuracy score (test) = 61.62 [%]
Global F1 score (train) = 66.85 [%]
Global F1 score (test) = 62.19 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.45      0.45      0.45       400
MODERATE-INTENSITY       0.58      0.68      0.62       400
         SEDENTARY       0.68      0.69      0.69       400
VIGOROUS-INTENSITY       0.83      0.64      0.72       345

          accuracy                           0.62      1545
         macro avg       0.63      0.62      0.62      1545
      weighted avg       0.63      0.62      0.62      1545


Accuracy capturado en la ejecución 1: 61.62 [%]
F1-score capturado en la ejecución 1: 62.19 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
2025-11-06 21:30:36.814451: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:30:36.825833: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461036.839162 1806769 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461036.843326 1806769 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461036.853653 1806769 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461036.853680 1806769 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461036.853683 1806769 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461036.853685 1806769 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:30:36.856893: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
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Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 1. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.73 [%]
Global accuracy score (test) = 61.29 [%]
Global F1 score (train) = 66.91 [%]
Global F1 score (test) = 61.71 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.46      0.44      0.45       400
MODERATE-INTENSITY       0.56      0.69      0.62       400
         SEDENTARY       0.70      0.69      0.69       400
VIGOROUS-INTENSITY       0.79      0.64      0.71       345

          accuracy                           0.61      1545
         macro avg       0.63      0.61      0.62      1545
      weighted avg       0.62      0.61      0.61      1545


Accuracy capturado en la ejecución 2: 61.29 [%]
F1-score capturado en la ejecución 2: 61.71 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
2025-11-06 21:30:52.150260: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:30:52.162173: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461052.176498 1807126 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461052.180746 1807126 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461052.191415 1807126 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461052.191437 1807126 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461052.191439 1807126 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461052.191442 1807126 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:30:52.194631: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.0s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
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Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.94 [%]
Global accuracy score (test) = 62.46 [%]
Global F1 score (train) = 67.1 [%]
Global F1 score (test) = 62.68 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.48      0.43      0.46       400
MODERATE-INTENSITY       0.57      0.67      0.61       400
         SEDENTARY       0.69      0.76      0.72       400
VIGOROUS-INTENSITY       0.81      0.65      0.72       345

          accuracy                           0.62      1545
         macro avg       0.64      0.63      0.63      1545
      weighted avg       0.63      0.62      0.62      1545


Accuracy capturado en la ejecución 3: 62.46 [%]
F1-score capturado en la ejecución 3: 62.68 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
2025-11-06 21:31:07.412516: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:31:07.423585: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461067.436775 1807477 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461067.440836 1807477 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461067.450623 1807477 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461067.450642 1807477 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461067.450644 1807477 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461067.450646 1807477 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:31:07.453685: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 0. 0. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.95 [%]
Global accuracy score (test) = 62.65 [%]
Global F1 score (train) = 67.13 [%]
Global F1 score (test) = 62.94 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.47      0.45      0.46       400
MODERATE-INTENSITY       0.59      0.70      0.64       400
         SEDENTARY       0.69      0.71      0.70       400
VIGOROUS-INTENSITY       0.79      0.65      0.71       345

          accuracy                           0.63      1545
         macro avg       0.64      0.63      0.63      1545
      weighted avg       0.63      0.63      0.63      1545


Accuracy capturado en la ejecución 4: 62.65 [%]
F1-score capturado en la ejecución 4: 62.94 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
2025-11-06 21:31:22.634647: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:31:22.645964: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461082.659140 1807827 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461082.663153 1807827 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461082.673128 1807827 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461082.673146 1807827 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461082.673148 1807827 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461082.673150 1807827 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:31:22.676280: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.8s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 0. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.4 [%]
Global accuracy score (test) = 61.88 [%]
Global F1 score (train) = 66.58 [%]
Global F1 score (test) = 62.28 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.46      0.45      0.45       400
MODERATE-INTENSITY       0.58      0.71      0.64       400
         SEDENTARY       0.70      0.69      0.69       400
VIGOROUS-INTENSITY       0.79      0.64      0.71       345

          accuracy                           0.62      1545
         macro avg       0.63      0.62      0.62      1545
      weighted avg       0.63      0.62      0.62      1545


Accuracy capturado en la ejecución 5: 61.88 [%]
F1-score capturado en la ejecución 5: 62.28 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
2025-11-06 21:31:37.950810: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:31:37.962173: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461097.975661 1808178 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461097.979709 1808178 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461097.989907 1808178 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461097.989925 1808178 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461097.989927 1808178 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461097.989928 1808178 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:31:37.992975: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.0s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[0. 2. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 67.05 [%]
Global accuracy score (test) = 61.55 [%]
Global F1 score (train) = 67.22 [%]
Global F1 score (test) = 61.69 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.43      0.38      0.40       400
MODERATE-INTENSITY       0.58      0.73      0.64       400
         SEDENTARY       0.70      0.71      0.71       400
VIGOROUS-INTENSITY       0.81      0.65      0.72       345

          accuracy                           0.62      1545
         macro avg       0.63      0.62      0.62      1545
      weighted avg       0.62      0.62      0.61      1545


Accuracy capturado en la ejecución 6: 61.55 [%]
F1-score capturado en la ejecución 6: 61.69 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
2025-11-06 21:31:53.097369: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:31:53.108919: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461113.122430 1808531 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461113.126528 1808531 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461113.136762 1808531 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461113.136784 1808531 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461113.136787 1808531 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461113.136789 1808531 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:31:53.139946: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 1. 0. ... 3. 1. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.79 [%]
Global accuracy score (test) = 61.23 [%]
Global F1 score (train) = 66.86 [%]
Global F1 score (test) = 61.46 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.45      0.39      0.42       400
MODERATE-INTENSITY       0.55      0.71      0.62       400
         SEDENTARY       0.70      0.70      0.70       400
VIGOROUS-INTENSITY       0.80      0.65      0.72       345

          accuracy                           0.61      1545
         macro avg       0.63      0.61      0.61      1545
      weighted avg       0.62      0.61      0.61      1545


Accuracy capturado en la ejecución 7: 61.23 [%]
F1-score capturado en la ejecución 7: 61.46 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
2025-11-06 21:32:08.368509: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:32:08.379867: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461128.393352 1808957 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461128.397364 1808957 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461128.407394 1808957 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461128.407412 1808957 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461128.407414 1808957 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461128.407417 1808957 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:32:08.410465: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.1s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.0s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.43 [%]
Global accuracy score (test) = 62.65 [%]
Global F1 score (train) = 66.62 [%]
Global F1 score (test) = 62.83 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.48      0.43      0.46       400
MODERATE-INTENSITY       0.59      0.70      0.64       400
         SEDENTARY       0.68      0.72      0.70       400
VIGOROUS-INTENSITY       0.80      0.66      0.72       345

          accuracy                           0.63      1545
         macro avg       0.64      0.63      0.63      1545
      weighted avg       0.63      0.63      0.63      1545


Accuracy capturado en la ejecución 8: 62.65 [%]
F1-score capturado en la ejecución 8: 62.83 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
2025-11-06 21:32:23.568716: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:32:23.579942: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461143.593407 1809313 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461143.597582 1809313 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461143.607450 1809313 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461143.607469 1809313 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461143.607471 1809313 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461143.607473 1809313 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:32:23.610534: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.59 [%]
Global accuracy score (test) = 62.72 [%]
Global F1 score (train) = 66.71 [%]
Global F1 score (test) = 62.99 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.48      0.44      0.46       400
MODERATE-INTENSITY       0.58      0.69      0.63       400
         SEDENTARY       0.69      0.73      0.71       400
VIGOROUS-INTENSITY       0.81      0.65      0.72       345

          accuracy                           0.63      1545
         macro avg       0.64      0.63      0.63      1545
      weighted avg       0.63      0.63      0.63      1545


Accuracy capturado en la ejecución 9: 62.72 [%]
F1-score capturado en la ejecución 9: 62.99 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
2025-11-06 21:32:38.840097: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:32:38.851296: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461158.864589 1809669 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461158.868664 1809669 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461158.878558 1809669 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461158.878585 1809669 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461158.878588 1809669 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461158.878589 1809669 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:32:38.881721: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 0. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.64 [%]
Global accuracy score (test) = 62.33 [%]
Global F1 score (train) = 66.78 [%]
Global F1 score (test) = 62.4 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.47      0.41      0.44       400
MODERATE-INTENSITY       0.58      0.73      0.65       400
         SEDENTARY       0.69      0.71      0.70       400
VIGOROUS-INTENSITY       0.79      0.64      0.71       345

          accuracy                           0.62      1545
         macro avg       0.63      0.62      0.62      1545
      weighted avg       0.63      0.62      0.62      1545


Accuracy capturado en la ejecución 10: 62.33 [%]
F1-score capturado en la ejecución 10: 62.4 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
2025-11-06 21:32:54.096781: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:32:54.107937: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461174.120984 1810020 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461174.124966 1810020 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461174.134585 1810020 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461174.134603 1810020 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461174.134605 1810020 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461174.134606 1810020 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:32:54.137654: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.9 [%]
Global accuracy score (test) = 64.27 [%]
Global F1 score (train) = 67.02 [%]
Global F1 score (test) = 64.51 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.52      0.47      0.49       400
MODERATE-INTENSITY       0.58      0.72      0.64       400
         SEDENTARY       0.71      0.73      0.72       400
VIGOROUS-INTENSITY       0.81      0.65      0.72       345

          accuracy                           0.64      1545
         macro avg       0.66      0.64      0.65      1545
      weighted avg       0.65      0.64      0.64      1545


Accuracy capturado en la ejecución 11: 64.27 [%]
F1-score capturado en la ejecución 11: 64.51 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
2025-11-06 21:33:09.218972: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:33:09.230330: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461189.243342 1810370 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461189.247274 1810370 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461189.257097 1810370 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461189.257115 1810370 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461189.257117 1810370 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461189.257118 1810370 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:33:09.260094: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.0s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.68 [%]
Global accuracy score (test) = 62.2 [%]
Global F1 score (train) = 66.97 [%]
Global F1 score (test) = 62.77 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.45      0.45      0.45       400
MODERATE-INTENSITY       0.59      0.68      0.63       400
         SEDENTARY       0.69      0.71      0.70       400
VIGOROUS-INTENSITY       0.83      0.65      0.73       345

          accuracy                           0.62      1545
         macro avg       0.64      0.62      0.63      1545
      weighted avg       0.63      0.62      0.62      1545


Accuracy capturado en la ejecución 12: 62.2 [%]
F1-score capturado en la ejecución 12: 62.77 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
2025-11-06 21:33:24.479549: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:33:24.491030: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461204.504663 1810721 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461204.508692 1810721 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461204.518657 1810721 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461204.518677 1810721 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461204.518680 1810721 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461204.518681 1810721 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:33:24.521849: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.0s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 0. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 67.06 [%]
Global accuracy score (test) = 62.65 [%]
Global F1 score (train) = 67.18 [%]
Global F1 score (test) = 62.83 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.46      0.41      0.44       400
MODERATE-INTENSITY       0.58      0.69      0.63       400
         SEDENTARY       0.69      0.75      0.72       400
VIGOROUS-INTENSITY       0.82      0.65      0.72       345

          accuracy                           0.63      1545
         macro avg       0.64      0.63      0.63      1545
      weighted avg       0.63      0.63      0.62      1545


Accuracy capturado en la ejecución 13: 62.65 [%]
F1-score capturado en la ejecución 13: 62.83 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
2025-11-06 21:33:39.652142: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:33:39.663308: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461219.676229 1811070 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461219.680230 1811070 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461219.690146 1811070 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461219.690165 1811070 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461219.690167 1811070 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461219.690169 1811070 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:33:39.693199: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
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Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.58 [%]
Global accuracy score (test) = 62.01 [%]
Global F1 score (train) = 66.78 [%]
Global F1 score (test) = 62.35 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.45      0.44      0.45       400
MODERATE-INTENSITY       0.59      0.69      0.64       400
         SEDENTARY       0.70      0.71      0.70       400
VIGOROUS-INTENSITY       0.79      0.65      0.71       345

          accuracy                           0.62      1545
         macro avg       0.63      0.62      0.62      1545
      weighted avg       0.63      0.62      0.62      1545


Accuracy capturado en la ejecución 14: 62.01 [%]
F1-score capturado en la ejecución 14: 62.35 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
2025-11-06 21:33:54.896798: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:33:54.908360: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461234.921698 1811422 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461234.925675 1811422 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461234.935650 1811422 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461234.935689 1811422 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461234.935691 1811422 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461234.935693 1811422 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:33:54.938812: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
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Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 0. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.8 [%]
Global accuracy score (test) = 62.33 [%]
Global F1 score (train) = 66.94 [%]
Global F1 score (test) = 62.74 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.47      0.46      0.46       400
MODERATE-INTENSITY       0.58      0.69      0.63       400
         SEDENTARY       0.69      0.71      0.70       400
VIGOROUS-INTENSITY       0.81      0.64      0.72       345

          accuracy                           0.62      1545
         macro avg       0.64      0.62      0.63      1545
      weighted avg       0.63      0.62      0.62      1545


Accuracy capturado en la ejecución 15: 62.33 [%]
F1-score capturado en la ejecución 15: 62.74 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
2025-11-06 21:34:10.247157: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:34:10.258730: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461250.271824 1811782 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461250.275967 1811782 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461250.285709 1811782 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461250.285729 1811782 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461250.285731 1811782 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461250.285733 1811782 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:34:10.288868: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.46 [%]
Global accuracy score (test) = 62.72 [%]
Global F1 score (train) = 66.67 [%]
Global F1 score (test) = 63.17 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.47      0.47      0.47       400
MODERATE-INTENSITY       0.59      0.70      0.64       400
         SEDENTARY       0.71      0.70      0.70       400
VIGOROUS-INTENSITY       0.80      0.64      0.71       345

          accuracy                           0.63      1545
         macro avg       0.64      0.63      0.63      1545
      weighted avg       0.64      0.63      0.63      1545


Accuracy capturado en la ejecución 16: 62.72 [%]
F1-score capturado en la ejecución 16: 63.17 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
2025-11-06 21:34:25.646376: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:34:25.657550: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461265.670695 1812156 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461265.674673 1812156 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461265.684438 1812156 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461265.684458 1812156 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461265.684460 1812156 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461265.684462 1812156 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:34:25.687447: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.0s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.4 [%]
Global accuracy score (test) = 60.78 [%]
Global F1 score (train) = 66.54 [%]
Global F1 score (test) = 60.99 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.45      0.40      0.43       400
MODERATE-INTENSITY       0.55      0.69      0.61       400
         SEDENTARY       0.68      0.70      0.69       400
VIGOROUS-INTENSITY       0.78      0.65      0.71       345

          accuracy                           0.61      1545
         macro avg       0.62      0.61      0.61      1545
      weighted avg       0.61      0.61      0.61      1545


Accuracy capturado en la ejecución 17: 60.78 [%]
F1-score capturado en la ejecución 17: 60.99 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
2025-11-06 21:34:40.950546: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:34:40.961946: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461280.974905 1812505 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461280.978942 1812505 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461280.988669 1812505 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461280.988687 1812505 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461280.988696 1812505 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461280.988697 1812505 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:34:40.991747: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[1. 2. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.97 [%]
Global accuracy score (test) = 62.78 [%]
Global F1 score (train) = 67.16 [%]
Global F1 score (test) = 63.03 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.47      0.44      0.46       400
MODERATE-INTENSITY       0.59      0.71      0.65       400
         SEDENTARY       0.70      0.71      0.70       400
VIGOROUS-INTENSITY       0.80      0.65      0.71       345

          accuracy                           0.63      1545
         macro avg       0.64      0.63      0.63      1545
      weighted avg       0.63      0.63      0.63      1545


Accuracy capturado en la ejecución 18: 62.78 [%]
F1-score capturado en la ejecución 18: 63.03 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
2025-11-06 21:34:56.196808: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:34:56.208295: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461296.222224 1812857 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461296.226470 1812857 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461296.236446 1812857 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461296.236468 1812857 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461296.236471 1812857 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461296.236472 1812857 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:34:56.239646: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.73 [%]
Global accuracy score (test) = 61.75 [%]
Global F1 score (train) = 66.91 [%]
Global F1 score (test) = 62.16 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.46      0.45      0.46       400
MODERATE-INTENSITY       0.57      0.67      0.62       400
         SEDENTARY       0.70      0.71      0.71       400
VIGOROUS-INTENSITY       0.78      0.64      0.71       345

          accuracy                           0.62      1545
         macro avg       0.63      0.62      0.62      1545
      weighted avg       0.63      0.62      0.62      1545


Accuracy capturado en la ejecución 19: 61.75 [%]
F1-score capturado en la ejecución 19: 62.16 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
2025-11-06 21:35:11.508677: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:35:11.520133: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461311.533203 1813214 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461311.537404 1813214 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461311.547268 1813214 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461311.547287 1813214 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461311.547289 1813214 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461311.547291 1813214 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:35:11.550407: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.88 [%]
Global accuracy score (test) = 61.23 [%]
Global F1 score (train) = 67.14 [%]
Global F1 score (test) = 61.73 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.45      0.43      0.44       400
MODERATE-INTENSITY       0.57      0.67      0.62       400
         SEDENTARY       0.67      0.70      0.69       400
VIGOROUS-INTENSITY       0.83      0.64      0.72       345

          accuracy                           0.61      1545
         macro avg       0.63      0.61      0.62      1545
      weighted avg       0.62      0.61      0.61      1545


Accuracy capturado en la ejecución 20: 61.23 [%]
F1-score capturado en la ejecución 20: 61.73 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
2025-11-06 21:35:26.764447: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:35:26.775853: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461326.789368 1813566 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461326.793519 1813566 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461326.803343 1813566 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461326.803360 1813566 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461326.803363 1813566 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461326.803364 1813566 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:35:26.806567: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.0s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.39 [%]
Global accuracy score (test) = 62.2 [%]
Global F1 score (train) = 66.51 [%]
Global F1 score (test) = 62.28 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.49      0.43      0.46       400
MODERATE-INTENSITY       0.59      0.72      0.65       400
         SEDENTARY       0.65      0.69      0.67       400
VIGOROUS-INTENSITY       0.79      0.64      0.71       345

          accuracy                           0.62      1545
         macro avg       0.63      0.62      0.62      1545
      weighted avg       0.63      0.62      0.62      1545


Accuracy capturado en la ejecución 21: 62.2 [%]
F1-score capturado en la ejecución 21: 62.28 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
2025-11-06 21:35:41.925343: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:35:41.936991: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461341.950253 1813916 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461341.954479 1813916 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461341.964291 1813916 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461341.964309 1813916 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461341.964311 1813916 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461341.964313 1813916 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:35:41.967446: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.0s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 0. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 67.07 [%]
Global accuracy score (test) = 62.39 [%]
Global F1 score (train) = 67.34 [%]
Global F1 score (test) = 62.91 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.47      0.46      0.46       400
MODERATE-INTENSITY       0.57      0.70      0.63       400
         SEDENTARY       0.71      0.70      0.71       400
VIGOROUS-INTENSITY       0.82      0.64      0.72       345

          accuracy                           0.62      1545
         macro avg       0.64      0.62      0.63      1545
      weighted avg       0.64      0.62      0.63      1545


Accuracy capturado en la ejecución 22: 62.39 [%]
F1-score capturado en la ejecución 22: 62.91 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
2025-11-06 21:35:57.107069: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:35:57.118492: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461357.131900 1814275 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461357.136059 1814275 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461357.145899 1814275 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461357.145919 1814275 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461357.145921 1814275 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461357.145923 1814275 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:35:57.148969: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.0s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 1. 1. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 67.16 [%]
Global accuracy score (test) = 60.84 [%]
Global F1 score (train) = 67.37 [%]
Global F1 score (test) = 61.35 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.44      0.42      0.43       400
MODERATE-INTENSITY       0.56      0.67      0.61       400
         SEDENTARY       0.68      0.70      0.69       400
VIGOROUS-INTENSITY       0.82      0.64      0.72       345

          accuracy                           0.61      1545
         macro avg       0.63      0.61      0.61      1545
      weighted avg       0.62      0.61      0.61      1545


Accuracy capturado en la ejecución 23: 60.84 [%]
F1-score capturado en la ejecución 23: 61.35 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
2025-11-06 21:36:12.384037: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:36:12.395379: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461372.408549 1814627 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461372.412691 1814627 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461372.423021 1814627 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461372.423047 1814627 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461372.423049 1814627 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461372.423051 1814627 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:36:12.426219: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 0. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.81 [%]
Global accuracy score (test) = 62.39 [%]
Global F1 score (train) = 67.0 [%]
Global F1 score (test) = 62.7 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.47      0.43      0.45       400
MODERATE-INTENSITY       0.59      0.73      0.65       400
         SEDENTARY       0.68      0.69      0.69       400
VIGOROUS-INTENSITY       0.82      0.64      0.72       345

          accuracy                           0.62      1545
         macro avg       0.64      0.62      0.63      1545
      weighted avg       0.63      0.62      0.62      1545


Accuracy capturado en la ejecución 24: 62.39 [%]
F1-score capturado en la ejecución 24: 62.7 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
2025-11-06 21:36:27.781457: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:36:27.792890: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461387.806335 1814979 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461387.810528 1814979 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461387.820758 1814979 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461387.820778 1814979 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461387.820780 1814979 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461387.820782 1814979 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:36:27.823986: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
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Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[1. 1. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.71 [%]
Global accuracy score (test) = 63.11 [%]
Global F1 score (train) = 66.97 [%]
Global F1 score (test) = 63.49 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.50      0.46      0.48       400
MODERATE-INTENSITY       0.57      0.72      0.64       400
         SEDENTARY       0.68      0.69      0.69       400
VIGOROUS-INTENSITY       0.83      0.66      0.73       345

          accuracy                           0.63      1545
         macro avg       0.65      0.63      0.63      1545
      weighted avg       0.64      0.63      0.63      1545


Accuracy capturado en la ejecución 25: 63.11 [%]
F1-score capturado en la ejecución 25: 63.49 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
2025-11-06 21:36:43.171396: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:36:43.182508: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461403.195503 1815330 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461403.199430 1815330 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461403.209189 1815330 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461403.209209 1815330 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461403.209219 1815330 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461403.209220 1815330 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:36:43.212199: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.1s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 67.27 [%]
Global accuracy score (test) = 63.24 [%]
Global F1 score (train) = 67.51 [%]
Global F1 score (test) = 63.62 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.48      0.46      0.47       400
MODERATE-INTENSITY       0.59      0.71      0.64       400
         SEDENTARY       0.68      0.72      0.70       400
VIGOROUS-INTENSITY       0.84      0.64      0.73       345

          accuracy                           0.63      1545
         macro avg       0.65      0.63      0.64      1545
      weighted avg       0.64      0.63      0.63      1545


Accuracy capturado en la ejecución 26: 63.24 [%]
F1-score capturado en la ejecución 26: 63.62 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
2025-11-06 21:36:58.388246: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:36:58.399394: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461418.412338 1815683 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461418.416344 1815683 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461418.426186 1815683 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461418.426207 1815683 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461418.426209 1815683 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461418.426210 1815683 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:36:58.429193: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.76 [%]
Global accuracy score (test) = 62.39 [%]
Global F1 score (train) = 66.99 [%]
Global F1 score (test) = 62.73 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.46      0.42      0.44       400
MODERATE-INTENSITY       0.57      0.70      0.63       400
         SEDENTARY       0.71      0.73      0.72       400
VIGOROUS-INTENSITY       0.82      0.64      0.72       345

          accuracy                           0.62      1545
         macro avg       0.64      0.62      0.63      1545
      weighted avg       0.63      0.62      0.62      1545


Accuracy capturado en la ejecución 27: 62.39 [%]
F1-score capturado en la ejecución 27: 62.73 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
2025-11-06 21:37:13.633310: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:37:13.644697: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461433.658127 1816039 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461433.662230 1816039 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461433.671953 1816039 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461433.671974 1816039 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461433.671977 1816039 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461433.671978 1816039 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:37:13.675122: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.0s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 1. ... 3. 1. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.52 [%]
Global accuracy score (test) = 61.17 [%]
Global F1 score (train) = 66.72 [%]
Global F1 score (test) = 61.54 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.46      0.42      0.44       400
MODERATE-INTENSITY       0.55      0.69      0.62       400
         SEDENTARY       0.68      0.70      0.69       400
VIGOROUS-INTENSITY       0.81      0.64      0.71       345

          accuracy                           0.61      1545
         macro avg       0.63      0.61      0.62      1545
      weighted avg       0.62      0.61      0.61      1545


Accuracy capturado en la ejecución 28: 61.17 [%]
F1-score capturado en la ejecución 28: 61.54 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
2025-11-06 21:37:28.761061: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-06 21:37:28.772146: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1762461448.784948 1816393 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762461448.788939 1816393 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762461448.798674 1816393 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461448.798694 1816393 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461448.798696 1816393 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762461448.798698 1816393 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 21:37:28.801690: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.1s
[Parallel(n_jobs=-1)]: Done 448 out of 448 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 1. 1. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.68 [%]
Global accuracy score (test) = 62.59 [%]
Global F1 score (train) = 66.83 [%]
Global F1 score (test) = 62.82 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.47      0.42      0.44       400
MODERATE-INTENSITY       0.58      0.71      0.63       400
         SEDENTARY       0.70      0.72      0.71       400
VIGOROUS-INTENSITY       0.81      0.66      0.73       345

          accuracy                           0.63      1545
         macro avg       0.64      0.63      0.63      1545
      weighted avg       0.63      0.63      0.62      1545


Accuracy capturado en la ejecución 29: 62.59 [%]
F1-score capturado en la ejecución 29: 62.82 [%]

=== EJECUCIÓN 30 ===

--- TRAIN (ejecución 30) ---

--- TEST (ejecución 30) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13317, 42)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 0. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 66.95 [%]
Global accuracy score (test) = 62.46 [%]
Global F1 score (train) = 67.08 [%]
Global F1 score (test) = 62.74 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.46      0.43      0.44       400
MODERATE-INTENSITY       0.58      0.71      0.64       400
         SEDENTARY       0.70      0.72      0.71       400
VIGOROUS-INTENSITY       0.80      0.64      0.71       345

          accuracy                           0.62      1545
         macro avg       0.64      0.63      0.63      1545
      weighted avg       0.63      0.62      0.62      1545


Accuracy capturado en la ejecución 30: 62.46 [%]
F1-score capturado en la ejecución 30: 62.74 [%]

=== RESUMEN FINAL ===
Accuracies: [61.62, 61.29, 62.46, 62.65, 61.88, 61.55, 61.23, 62.65, 62.72, 62.33, 64.27, 62.2, 62.65, 62.01, 62.33, 62.72, 60.78, 62.78, 61.75, 61.23, 62.2, 62.39, 60.84, 62.39, 63.11, 63.24, 62.39, 61.17, 62.59, 62.46]
F1-scores: [62.19, 61.71, 62.68, 62.94, 62.28, 61.69, 61.46, 62.83, 62.99, 62.4, 64.51, 62.77, 62.83, 62.35, 62.74, 63.17, 60.99, 63.03, 62.16, 61.73, 62.28, 62.91, 61.35, 62.7, 63.49, 63.62, 62.73, 61.54, 62.82, 62.74]
Accuracy mean: 62.1960 | std: 0.7493
F1 mean: 62.5210 | std: 0.7314

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_BRF_acc_superclasses_CPA_METs/metrics_test.npz
