2025-11-07 09:45:44.986509: 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`.
2025-11-07 09:45:44.998441: 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 00:00:1762505145.012708 2343183 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505145.017080 2343183 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505145.027738 2343183 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505145.027766 2343183 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505145.027768 2343183 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505145.027770 2343183 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:45:45.030981: 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(
2025-11-07 09:45:50,120	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-07 09:45:50,768	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-07 09:45:50,834	INFO trial.py:182 -- Creating a new dirname dir_29c56_deeb because trial dirname 'dir_29c56' already exists.
2025-11-07 09:45:50,838	INFO trial.py:182 -- Creating a new dirname dir_29c56_d966 because trial dirname 'dir_29c56' already exists.
2025-11-07 09:45:50,841	INFO trial.py:182 -- Creating a new dirname dir_29c56_fbeb because trial dirname 'dir_29c56' already exists.
2025-11-07 09:45:50,843	INFO trial.py:182 -- Creating a new dirname dir_29c56_c356 because trial dirname 'dir_29c56' already exists.
2025-11-07 09:45:50,845	INFO trial.py:182 -- Creating a new dirname dir_29c56_e9ec because trial dirname 'dir_29c56' already exists.
2025-11-07 09:45:50,848	INFO trial.py:182 -- Creating a new dirname dir_29c56_3fd5 because trial dirname 'dir_29c56' already exists.
2025-11-07 09:45:50,851	INFO trial.py:182 -- Creating a new dirname dir_29c56_e6da because trial dirname 'dir_29c56' already exists.
2025-11-07 09:45:50,854	INFO trial.py:182 -- Creating a new dirname dir_29c56_7642 because trial dirname 'dir_29c56' already exists.
2025-11-07 09:45:50,857	INFO trial.py:182 -- Creating a new dirname dir_29c56_1a9b because trial dirname 'dir_29c56' already exists.
2025-11-07 09:45:50,859	INFO trial.py:182 -- Creating a new dirname dir_29c56_377c because trial dirname 'dir_29c56' already exists.
2025-11-07 09:45:50,862	INFO trial.py:182 -- Creating a new dirname dir_29c56_db94 because trial dirname 'dir_29c56' already exists.
2025-11-07 09:45:50,865	INFO trial.py:182 -- Creating a new dirname dir_29c56_3fa5 because trial dirname 'dir_29c56' already exists.
2025-11-07 09:45:50,868	INFO trial.py:182 -- Creating a new dirname dir_29c56_4499 because trial dirname 'dir_29c56' already exists.
2025-11-07 09:45:50,873	INFO trial.py:182 -- Creating a new dirname dir_29c56_4d2f because trial dirname 'dir_29c56' already exists.
2025-11-07 09:45:50,877	INFO trial.py:182 -- Creating a new dirname dir_29c56_19b2 because trial dirname 'dir_29c56' already exists.
2025-11-07 09:45:50,880	INFO trial.py:182 -- Creating a new dirname dir_29c56_1f59 because trial dirname 'dir_29c56' already exists.
2025-11-07 09:45:50,884	INFO trial.py:182 -- Creating a new dirname dir_29c56_854e because trial dirname 'dir_29c56' already exists.
2025-11-07 09:45:50,887	INFO trial.py:182 -- Creating a new dirname dir_29c56_c54d because trial dirname 'dir_29c56' already exists.
2025-11-07 09:45:50,895	INFO trial.py:182 -- Creating a new dirname dir_29c56_6db6 because trial dirname 'dir_29c56' 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_C/case_C_BRF_acc_superclasses_CPA_METs/BalancedRF_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-07_09-45-49_312112_2343183/artifacts/2025-11-07_09-45-50/BalancedRF_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-07 09:45:51. Total running time: 0s
Logical resource usage: 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_29c56    PENDING               472             7                    46                   27   0.3                        3830 │
│ trial_29c56    PENDING               289             5                    24                   10   sqrt                       5437 │
│ trial_29c56    PENDING               274             7                    35                   21   sqrt                       3006 │
│ trial_29c56    PENDING               202             5                    48                   20   sqrt                       7062 │
│ trial_29c56    PENDING               421             7                    30                   15   sqrt                       7003 │
│ trial_29c56    PENDING               389             7                    36                   11   sqrt                       6507 │
│ trial_29c56    PENDING               233             7                    53                   16   0.3                        1397 │
│ trial_29c56    PENDING               368             5                    31                   16   0.3                         696 │
│ trial_29c56    PENDING               484             5                    55                   26   0.3                        8841 │
│ trial_29c56    PENDING               443             7                    36                   17   sqrt                       3295 │
│ trial_29c56    PENDING               316             6                    24                   29   0.3                        9541 │
│ trial_29c56    PENDING               268             6                    35                   20   sqrt                       4444 │
│ trial_29c56    PENDING               464             6                    52                   14   sqrt                       2459 │
│ trial_29c56    PENDING               455             7                    51                   20   0.3                        2511 │
│ trial_29c56    PENDING               279             6                    26                   29   sqrt                       8428 │
│ trial_29c56    PENDING               417             7                    37                   25   0.3                        9436 │
│ trial_29c56    PENDING               319             6                    39                   12   sqrt                       3806 │
│ trial_29c56    PENDING               457             7                    50                   13   0.3                        1941 │
│ trial_29c56    PENDING               343             7                    40                   18   0.3                        1911 │
│ trial_29c56    PENDING               202             6                    38                   25   0.3                        4081 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_29c56 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               20 │
│ min_samples_split              35 │
│ n_estimators                  268 │
│ random_state                 4444 │
╰───────────────────────────────────╯
Trial trial_29c56 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               15 │
│ min_samples_split              30 │
│ n_estimators                  421 │
│ random_state                 7003 │
╰───────────────────────────────────╯
Trial trial_29c56 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               29 │
│ min_samples_split              26 │
│ n_estimators                  279 │
│ random_state                 8428 │
╰───────────────────────────────────╯
Trial trial_29c56 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               11 │
│ min_samples_split              36 │
│ n_estimators                  389 │
│ random_state                 6507 │
╰───────────────────────────────────╯
Trial trial_29c56 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               25 │
│ min_samples_split              37 │
│ n_estimators                  417 │
│ random_state                 9436 │
╰───────────────────────────────────╯
Trial trial_29c56 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               12 │
│ min_samples_split              39 │
│ n_estimators                  319 │
│ random_state                 3806 │
╰───────────────────────────────────╯
Trial trial_29c56 started with configuration:
╭──────────────────────────────────╮
│ Trial trial_29c56 config         │
├──────────────────────────────────┤
│ max_depth                      5 │
│ max_features                 0.3 │
│ min_samples_leaf              16 │
│ min_samples_split             31 │
│ n_estimators                 368 │
│ random_state                 696 │
╰──────────────────────────────────╯
Trial trial_29c56 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               29 │
│ min_samples_split              24 │
│ n_estimators                  316 │
│ random_state                 9541 │
╰───────────────────────────────────╯
Trial trial_29c56 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               27 │
│ min_samples_split              46 │
│ n_estimators                  472 │
│ random_state                 3830 │
╰───────────────────────────────────╯
Trial trial_29c56 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               20 │
│ min_samples_split              48 │
│ n_estimators                  202 │
│ random_state                 7062 │
╰───────────────────────────────────╯
Trial trial_29c56 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               18 │
│ min_samples_split              40 │
│ n_estimators                  343 │
│ random_state                 1911 │
╰───────────────────────────────────╯
Trial trial_29c56 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               17 │
│ min_samples_split              36 │
│ n_estimators                  443 │
│ random_state                 3295 │
╰───────────────────────────────────╯
Trial trial_29c56 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               10 │
│ min_samples_split              24 │
│ n_estimators                  289 │
│ random_state                 5437 │
╰───────────────────────────────────╯
Trial trial_29c56 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               25 │
│ min_samples_split              38 │
│ n_estimators                  202 │
│ random_state                 4081 │
╰───────────────────────────────────╯
Trial trial_29c56 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               14 │
│ min_samples_split              52 │
│ n_estimators                  464 │
│ random_state                 2459 │
╰───────────────────────────────────╯
Trial trial_29c56 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               16 │
│ min_samples_split              53 │
│ n_estimators                  233 │
│ random_state                 1397 │
╰───────────────────────────────────╯
Trial trial_29c56 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               26 │
│ min_samples_split              55 │
│ n_estimators                  484 │
│ random_state                 8841 │
╰───────────────────────────────────╯
Trial trial_29c56 started with configuration:
[36m(train_brf_ray_tune pid=2344816)[0m 2025-11-07 09:45:54.217843: 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=2344824)[0m 2025-11-07 09:45:54.218723: 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=2344815)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_brf_ray_tune pid=2344815)[0m E0000 00:00:1762505154.183275 2345975 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=2344815)[0m E0000 00:00:1762505154.190967 2345975 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=2344815)[0m W0000 00:00:1762505154.211239 2345975 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=2344815)[0m W0000 00:00:1762505154.211292 2345975 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=2344815)[0m W0000 00:00:1762505154.211296 2345975 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=2344815)[0m W0000 00:00:1762505154.211299 2345975 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=2344815)[0m 2025-11-07 09:45:54.217473: 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=2344815)[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=2344815)[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=2344815)[0m   return fit_method(estimator, *args, **kwargs)
[36m(train_brf_ray_tune pid=2344815)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=2344815)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[36m(train_brf_ray_tune pid=2344844)[0m 2025-11-07 09:45:54.505949: 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=2344844)[0m 2025-11-07 09:45:54.527333: 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=2344844)[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=2344844)[0m E0000 00:00:1762505154.556030 2346115 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=2344844)[0m E0000 00:00:1762505154.564354 2346115 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=2344844)[0m W0000 00:00:1762505154.584310 2346115 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=2344844)[0m 2025-11-07 09:45:54.590139: 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=2344844)[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=2344832)[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=2344832)[0m   return fit_method(estimator, *args, **kwargs)[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=2344832)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=2344832)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    3.5s[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=2344840)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   10.9s[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=2344812)[0m [Parallel(n_jobs=-1)]: Done 202 out of 202 | elapsed:   13.6s finished
[36m(train_brf_ray_tune pid=2344812)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=2344812)[0m [Parallel(n_jobs=20)]: Done 202 out of 202 | elapsed:    0.5s finished
[36m(train_brf_ray_tune pid=2344812)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=2344812)[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=2344812)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=2344831)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   15.6s[32m [repeated 13x across cluster][0m
[36m(train_brf_ray_tune pid=2344815)[0m [Parallel(n_jobs=20)]: Done 268 out of 268 | elapsed:    0.7s finished[32m [repeated 9x across cluster][0m
[36m(train_brf_ray_tune pid=2344811)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 7x across cluster][0m
[36m(train_brf_ray_tune pid=2344814)[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 3x across cluster][0m
[36m(train_brf_ray_tune pid=2344814)[0m   _log_deprecation_warning([32m [repeated 3x across cluster][0m
[36m(train_brf_ray_tune pid=2344840)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.2s[32m [repeated 27x across cluster][0m
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               13 │
│ min_samples_split              50 │
│ n_estimators                  457 │
│ random_state                 1941 │
╰───────────────────────────────────╯
Trial trial_29c56 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               21 │
│ min_samples_split              35 │
│ n_estimators                  274 │
│ random_state                 3006 │
╰───────────────────────────────────╯
Trial trial_29c56 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29c56 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               20 │
│ min_samples_split              51 │
│ n_estimators                  455 │
│ random_state                 2511 │
╰───────────────────────────────────╯

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:13. Total running time: 22s
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             19.3324 │
│ time_total_s                 19.3324 │
│ training_iteration                 1 │
│ test_accuracy                0.65178 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:13. Total running time: 22s

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:15. Total running time: 25s
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             21.9867 │
│ time_total_s                 21.9867 │
│ training_iteration                 1 │
│ test_accuracy                0.66926 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:15. Total running time: 25s

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:16. Total running time: 25s
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             22.3909 │
│ time_total_s                 22.3909 │
│ training_iteration                 1 │
│ test_accuracy                0.66472 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:16. Total running time: 25s

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:16. Total running time: 26s
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             22.8765 │
│ time_total_s                 22.8765 │
│ training_iteration                 1 │
│ test_accuracy                0.64984 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:16. Total running time: 26s

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:18. Total running time: 27s
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             24.3887 │
│ time_total_s                 24.3887 │
│ training_iteration                 1 │
│ test_accuracy                0.69903 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:18. Total running time: 27s

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:19. Total running time: 28s
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              25.333 │
│ time_total_s                  25.333 │
│ training_iteration                 1 │
│ test_accuracy                0.67184 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:19. Total running time: 28s

Trial status: 14 RUNNING | 6 TERMINATED
Current time: 2025-11-07 09:46:21. Total running time: 30s
Logical resource usage: 14.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
[36m(train_brf_ray_tune pid=2344832)[0m [Parallel(n_jobs=20)]: Done 202 out of 202 | elapsed:    0.6s finished[32m [repeated 11x across cluster][0m
[36m(train_brf_ray_tune pid=2344816)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 8x across cluster][0m
[36m(train_brf_ray_tune pid=2344816)[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 5x across cluster][0m
[36m(train_brf_ray_tune pid=2344816)[0m   _log_deprecation_warning([32m [repeated 5x across cluster][0m
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         n_estimators     max_depth     min_samples_split     min_samples_leaf   max_features       random_state     iter     total time (s)     test_accuracy │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_29c56    RUNNING                 472             7                    46                   27   0.3                        3830                                               │
│ trial_29c56    RUNNING                 421             7                    30                   15   sqrt                       7003                                               │
│ trial_29c56    RUNNING                 389             7                    36                   11   sqrt                       6507                                               │
│ trial_29c56    RUNNING                 233             7                    53                   16   0.3                        1397                                               │
│ trial_29c56    RUNNING                 368             5                    31                   16   0.3                         696                                               │
│ trial_29c56    RUNNING                 484             5                    55                   26   0.3                        8841                                               │
│ trial_29c56    RUNNING                 443             7                    36                   17   sqrt                       3295                                               │
│ trial_29c56    RUNNING                 316             6                    24                   29   0.3                        9541                                               │
│ trial_29c56    RUNNING                 464             6                    52                   14   sqrt                       2459                                               │
│ trial_29c56    RUNNING                 455             7                    51                   20   0.3                        2511                                               │
│ trial_29c56    RUNNING                 417             7                    37                   25   0.3                        9436                                               │
│ trial_29c56    RUNNING                 457             7                    50                   13   0.3                        1941                                               │
│ trial_29c56    RUNNING                 343             7                    40                   18   0.3                        1911                                               │
│ trial_29c56    RUNNING                 202             6                    38                   25   0.3                        4081                                               │
│ trial_29c56    TERMINATED              289             5                    24                   10   sqrt                       5437        1            22.8765          0.649838 │
│ trial_29c56    TERMINATED              274             7                    35                   21   sqrt                       3006        1            24.3887          0.699029 │
│ trial_29c56    TERMINATED              202             5                    48                   20   sqrt                       7062        1            19.3324          0.65178  │
│ trial_29c56    TERMINATED              268             6                    35                   20   sqrt                       4444        1            22.3909          0.664725 │
│ trial_29c56    TERMINATED              279             6                    26                   29   sqrt                       8428        1            21.9867          0.669256 │
│ trial_29c56    TERMINATED              319             6                    39                   12   sqrt                       3806        1            25.333           0.671845 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:21. Total running time: 31s
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             27.9633 │
│ time_total_s                 27.9633 │
│ training_iteration                 1 │
│ test_accuracy                0.69644 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:21. Total running time: 31s

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:22. Total running time: 31s
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             28.3212 │
│ time_total_s                 28.3212 │
│ training_iteration                 1 │
│ test_accuracy                0.70939 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:22. Total running time: 31s

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:23. Total running time: 32s
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             29.2165 │
│ time_total_s                 29.2165 │
│ training_iteration                 1 │
│ test_accuracy                 0.7055 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:23. Total running time: 32s

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:24. Total running time: 33s
[36m(train_brf_ray_tune pid=2344839)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.1s[32m [repeated 29x across cluster][0m
[36m(train_brf_ray_tune pid=2344843)[0m [Parallel(n_jobs=-1)]: Done 343 out of 343 | elapsed:   28.5s finished[32m [repeated 21x across cluster][0m
[36m(train_brf_ray_tune pid=2344843)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 13x across cluster][0m
[36m(train_brf_ray_tune pid=2344843)[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 6x across cluster][0m
[36m(train_brf_ray_tune pid=2344843)[0m   _log_deprecation_warning([32m [repeated 6x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             30.2624 │
│ time_total_s                 30.2624 │
│ training_iteration                 1 │
│ test_accuracy                0.66019 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:24. Total running time: 33s

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:24. Total running time: 33s
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             30.6168 │
│ time_total_s                 30.6168 │
│ training_iteration                 1 │
│ test_accuracy                0.70291 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:24. Total running time: 33s

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:24. Total running time: 34s
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             30.8894 │
│ time_total_s                 30.8894 │
│ training_iteration                 1 │
│ test_accuracy                0.70744 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:24. Total running time: 34s

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:25. Total running time: 34s
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              31.264 │
│ time_total_s                  31.264 │
│ training_iteration                 1 │
│ test_accuracy                0.69644 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:25. Total running time: 34s

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:26. Total running time: 35s
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             32.0945 │
│ time_total_s                 32.0945 │
│ training_iteration                 1 │
│ test_accuracy                0.66861 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:26. Total running time: 35s

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:27. Total running time: 36s
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             33.3345 │
│ time_total_s                 33.3345 │
│ training_iteration                 1 │
│ test_accuracy                0.70744 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:27. Total running time: 36s

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:28. Total running time: 37s
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             34.8643 │
│ time_total_s                 34.8643 │
│ training_iteration                 1 │
│ test_accuracy                0.71392 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:28. Total running time: 37s

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:28. Total running time: 38s
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             34.8101 │
│ time_total_s                 34.8101 │
│ training_iteration                 1 │
│ test_accuracy                0.66084 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:28. Total running time: 38s

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:28. Total running time: 38s
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             34.9779 │
│ time_total_s                 34.9779 │
│ training_iteration                 1 │
│ test_accuracy                0.70809 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:28. Total running time: 38s

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:28. Total running time: 38s
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              34.993 │
│ time_total_s                  34.993 │
│ training_iteration                 1 │
│ test_accuracy                0.71197 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:28. Total running time: 38s

Trial trial_29c56 finished iteration 1 at 2025-11-07 09:46:28. Total running time: 38s
2025-11-07 09:46:29,019	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_C/case_C_BRF_acc_superclasses_CPA_METs/BalancedRF_hyperparameters_tuning' in 0.0127s.
/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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | elapsed:    0.1s finished
╭──────────────────────────────────────╮
│ Trial trial_29c56 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             34.9467 │
│ time_total_s                 34.9467 │
│ training_iteration                 1 │
│ test_accuracy                0.70097 │
╰──────────────────────────────────────╯

Trial trial_29c56 completed after 1 iterations at 2025-11-07 09:46:29. Total running time: 38s

Trial status: 20 TERMINATED
Current time: 2025-11-07 09:46:29. Total running time: 38s
Logical resource usage: 1.0/20 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_29c56    TERMINATED              472             7                    46                   27   0.3                        3830        1            34.9779          0.708091 │
│ trial_29c56    TERMINATED              289             5                    24                   10   sqrt                       5437        1            22.8765          0.649838 │
│ trial_29c56    TERMINATED              274             7                    35                   21   sqrt                       3006        1            24.3887          0.699029 │
│ trial_29c56    TERMINATED              202             5                    48                   20   sqrt                       7062        1            19.3324          0.65178  │
│ trial_29c56    TERMINATED              421             7                    30                   15   sqrt                       7003        1            30.6168          0.702913 │
│ trial_29c56    TERMINATED              389             7                    36                   11   sqrt                       6507        1            29.2165          0.705502 │
│ trial_29c56    TERMINATED              233             7                    53                   16   0.3                        1397        1            28.3212          0.709385 │
│ trial_29c56    TERMINATED              368             5                    31                   16   0.3                         696        1            30.2624          0.660194 │
│ trial_29c56    TERMINATED              484             5                    55                   26   0.3                        8841        1            34.8101          0.660841 │
│ trial_29c56    TERMINATED              443             7                    36                   17   sqrt                       3295        1            30.8894          0.707443 │
│ trial_29c56    TERMINATED              316             6                    24                   29   0.3                        9541        1            31.264           0.69644  │
│ trial_29c56    TERMINATED              268             6                    35                   20   sqrt                       4444        1            22.3909          0.664725 │
│ trial_29c56    TERMINATED              464             6                    52                   14   sqrt                       2459        1            32.0945          0.668608 │
│ trial_29c56    TERMINATED              455             7                    51                   20   0.3                        2511        1            34.9467          0.700971 │
│ trial_29c56    TERMINATED              279             6                    26                   29   sqrt                       8428        1            21.9867          0.669256 │
│ trial_29c56    TERMINATED              417             7                    37                   25   0.3                        9436        1            34.8643          0.713916 │
│ trial_29c56    TERMINATED              319             6                    39                   12   sqrt                       3806        1            25.333           0.671845 │
│ trial_29c56    TERMINATED              457             7                    50                   13   0.3                        1941        1            34.993           0.711974 │
│ trial_29c56    TERMINATED              343             7                    40                   18   0.3                        1911        1            33.3345          0.707443 │
│ trial_29c56    TERMINATED              202             6                    38                   25   0.3                        4081        1            27.9633          0.69644  │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'n_estimators': 417, 'max_depth': 7, 'min_samples_split': 37, 'min_samples_leaf': 25, 'max_features': 0.3, 'random_state': 9436}
Saved model to disk
[36m(train_brf_ray_tune pid=2344844)[0m [Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s[32m [repeated 42x across cluster][0m
[36m(train_brf_ray_tune pid=2344844)[0m [Parallel(n_jobs=20)]: Done 455 out of 455 | elapsed:    0.1s finished[32m [repeated 17x across cluster][0m
[36m(train_brf_ray_tune pid=2344844)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 10x across cluster][0m
[36m(train_brf_ray_tune pid=2344844)[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 5x across cluster][0m
[36m(train_brf_ray_tune pid=2344844)[0m   _log_deprecation_warning([32m [repeated 5x across cluster][0m
2025-11-07 09:46:41.933414: 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`.
2025-11-07 09:46:41.945070: 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 00:00:1762505201.958640 2348270 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505201.962877 2348270 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505201.972870 2348270 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505201.972893 2348270 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505201.972895 2348270 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505201.972896 2348270 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:46:41.976190: 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 417 out of 417 | elapsed:    2.5s 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 417 out of 417 | 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)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[0. 1. 0. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.17 [%]
Global accuracy score (test) = 69.0 [%]
Global F1 score (train) = 73.88 [%]
Global F1 score (test) = 69.24 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.57      0.49      0.53       400
MODERATE-INTENSITY       0.59      0.75      0.66       400
         SEDENTARY       0.82      0.80      0.81       400
VIGOROUS-INTENSITY       0.82      0.73      0.77       345

          accuracy                           0.69      1545
         macro avg       0.70      0.69      0.69      1545
      weighted avg       0.70      0.69      0.69      1545


Accuracy capturado en la ejecución 1: 69.0 [%]
F1-score capturado en la ejecución 1: 69.24 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
2025-11-07 09:46:57.161778: 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`.
2025-11-07 09:46:57.173565: 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 00:00:1762505217.187360 2348635 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505217.191651 2348635 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505217.201779 2348635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505217.201799 2348635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505217.201802 2348635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505217.201804 2348635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:46:57.204998: 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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | 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)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 0. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 75.0 [%]
Global accuracy score (test) = 70.81 [%]
Global F1 score (train) = 74.83 [%]
Global F1 score (test) = 70.89 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.60      0.48      0.54       400
MODERATE-INTENSITY       0.60      0.80      0.68       400
         SEDENTARY       0.83      0.82      0.83       400
VIGOROUS-INTENSITY       0.85      0.73      0.79       345

          accuracy                           0.71      1545
         macro avg       0.72      0.71      0.71      1545
      weighted avg       0.72      0.71      0.71      1545


Accuracy capturado en la ejecución 2: 70.81 [%]
F1-score capturado en la ejecución 2: 70.89 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
2025-11-07 09:47:12.286055: 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`.
2025-11-07 09:47:12.297613: 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 00:00:1762505232.311483 2348997 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505232.315653 2348997 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505232.325680 2348997 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505232.325703 2348997 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505232.325706 2348997 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505232.325708 2348997 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:47:12.328871: 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 417 out of 417 | elapsed:    2.5s 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 417 out of 417 | 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)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.7 [%]
Global accuracy score (test) = 70.1 [%]
Global F1 score (train) = 74.35 [%]
Global F1 score (test) = 70.25 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.60      0.47      0.53       400
MODERATE-INTENSITY       0.58      0.80      0.67       400
         SEDENTARY       0.82      0.81      0.81       400
VIGOROUS-INTENSITY       0.87      0.73      0.79       345

          accuracy                           0.70      1545
         macro avg       0.72      0.70      0.70      1545
      weighted avg       0.71      0.70      0.70      1545


Accuracy capturado en la ejecución 3: 70.1 [%]
F1-score capturado en la ejecución 3: 70.25 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
2025-11-07 09:47:27.452205: 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`.
2025-11-07 09:47:27.463789: 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 00:00:1762505247.477150 2349608 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505247.481480 2349608 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505247.491528 2349608 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505247.491550 2349608 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505247.491552 2349608 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505247.491554 2349608 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:47:27.494863: 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 417 out of 417 | elapsed:    2.5s 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 417 out of 417 | 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)
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)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 0. ... 2. 2. 1.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.68 [%]
Global accuracy score (test) = 67.96 [%]
Global F1 score (train) = 74.45 [%]
Global F1 score (test) = 68.22 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.58      0.46      0.51       400
MODERATE-INTENSITY       0.56      0.79      0.66       400
         SEDENTARY       0.83      0.74      0.79       400
VIGOROUS-INTENSITY       0.82      0.73      0.77       345

          accuracy                           0.68      1545
         macro avg       0.70      0.68      0.68      1545
      weighted avg       0.69      0.68      0.68      1545


Accuracy capturado en la ejecución 4: 67.96 [%]
F1-score capturado en la ejecución 4: 68.22 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
2025-11-07 09:47:42.518551: 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`.
2025-11-07 09:47:42.529875: 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 00:00:1762505262.542966 2349974 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505262.546968 2349974 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505262.556943 2349974 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505262.556961 2349974 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505262.556963 2349974 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505262.556972 2349974 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:47:42.560066: 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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | 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)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.76 [%]
Global accuracy score (test) = 70.29 [%]
Global F1 score (train) = 74.6 [%]
Global F1 score (test) = 70.21 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.60      0.46      0.52       400
MODERATE-INTENSITY       0.60      0.81      0.69       400
         SEDENTARY       0.82      0.81      0.82       400
VIGOROUS-INTENSITY       0.85      0.73      0.79       345

          accuracy                           0.70      1545
         macro avg       0.72      0.70      0.70      1545
      weighted avg       0.71      0.70      0.70      1545


Accuracy capturado en la ejecución 5: 70.29 [%]
F1-score capturado en la ejecución 5: 70.21 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
2025-11-07 09:47:57.481435: 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`.
2025-11-07 09:47:57.493110: 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 00:00:1762505277.506346 2350333 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505277.510510 2350333 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505277.520477 2350333 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505277.520511 2350333 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505277.520514 2350333 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505277.520515 2350333 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:47:57.523713: 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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | 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)
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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)
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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)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 0. 0. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.92 [%]
Global accuracy score (test) = 69.58 [%]
Global F1 score (train) = 74.69 [%]
Global F1 score (test) = 69.8 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.59      0.50      0.54       400
MODERATE-INTENSITY       0.60      0.78      0.68       400
         SEDENTARY       0.83      0.78      0.80       400
VIGOROUS-INTENSITY       0.82      0.73      0.77       345

          accuracy                           0.70      1545
         macro avg       0.71      0.70      0.70      1545
      weighted avg       0.70      0.70      0.70      1545


Accuracy capturado en la ejecución 6: 69.58 [%]
F1-score capturado en la ejecución 6: 69.8 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
2025-11-07 09:48:12.445202: 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`.
2025-11-07 09:48:12.456805: 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 00:00:1762505292.469918 2350682 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505292.474083 2350682 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505292.483973 2350682 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505292.483993 2350682 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505292.483995 2350682 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505292.483997 2350682 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:48:12.487293: 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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | 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)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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)
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)
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)
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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)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 0. 1. ... 1. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.32 [%]
Global accuracy score (test) = 69.84 [%]
Global F1 score (train) = 74.15 [%]
Global F1 score (test) = 69.99 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.59      0.50      0.54       400
MODERATE-INTENSITY       0.60      0.79      0.69       400
         SEDENTARY       0.82      0.78      0.80       400
VIGOROUS-INTENSITY       0.82      0.73      0.77       345

          accuracy                           0.70      1545
         macro avg       0.71      0.70      0.70      1545
      weighted avg       0.71      0.70      0.70      1545


Accuracy capturado en la ejecución 7: 69.84 [%]
F1-score capturado en la ejecución 7: 69.99 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
2025-11-07 09:48:27.435017: 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`.
2025-11-07 09:48:27.446509: 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 00:00:1762505307.459929 2351046 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505307.464244 2351046 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505307.474341 2351046 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505307.474364 2351046 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505307.474366 2351046 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505307.474367 2351046 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:48:27.477737: 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 417 out of 417 | elapsed:    2.5s 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 417 out of 417 | 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)
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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)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 0. 0. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.63 [%]
Global accuracy score (test) = 70.61 [%]
Global F1 score (train) = 74.33 [%]
Global F1 score (test) = 70.79 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.61      0.50      0.55       400
MODERATE-INTENSITY       0.60      0.82      0.69       400
         SEDENTARY       0.84      0.78      0.81       400
VIGOROUS-INTENSITY       0.84      0.73      0.78       345

          accuracy                           0.71      1545
         macro avg       0.72      0.71      0.71      1545
      weighted avg       0.72      0.71      0.71      1545


Accuracy capturado en la ejecución 8: 70.61 [%]
F1-score capturado en la ejecución 8: 70.79 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
2025-11-07 09:48:42.450836: 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`.
2025-11-07 09:48:42.462284: 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 00:00:1762505322.475953 2351397 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505322.480074 2351397 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505322.490329 2351397 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505322.490346 2351397 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505322.490349 2351397 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505322.490351 2351397 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:48:42.493581: 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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | 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)
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)
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)
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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)
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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)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 73.67 [%]
Global accuracy score (test) = 69.45 [%]
Global F1 score (train) = 73.06 [%]
Global F1 score (test) = 69.38 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.62      0.42      0.50       400
MODERATE-INTENSITY       0.56      0.82      0.67       400
         SEDENTARY       0.82      0.81      0.82       400
VIGOROUS-INTENSITY       0.86      0.73      0.79       345

          accuracy                           0.69      1545
         macro avg       0.72      0.70      0.69      1545
      weighted avg       0.71      0.69      0.69      1545


Accuracy capturado en la ejecución 9: 69.45 [%]
F1-score capturado en la ejecución 9: 69.38 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
2025-11-07 09:48:57.375451: 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`.
2025-11-07 09:48:57.386999: 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 00:00:1762505337.400054 2351749 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505337.404056 2351749 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505337.413838 2351749 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505337.413856 2351749 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505337.413858 2351749 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505337.413859 2351749 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:48:57.416913: 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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | 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)
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)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[0. 1. 0. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.54 [%]
Global accuracy score (test) = 69.51 [%]
Global F1 score (train) = 74.22 [%]
Global F1 score (test) = 69.41 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.60      0.44      0.51       400
MODERATE-INTENSITY       0.58      0.81      0.68       400
         SEDENTARY       0.81      0.80      0.81       400
VIGOROUS-INTENSITY       0.84      0.73      0.78       345

          accuracy                           0.70      1545
         macro avg       0.71      0.70      0.69      1545
      weighted avg       0.70      0.70      0.69      1545


Accuracy capturado en la ejecución 10: 69.51 [%]
F1-score capturado en la ejecución 10: 69.41 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
2025-11-07 09:49:12.330430: 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`.
2025-11-07 09:49:12.341613: 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 00:00:1762505352.355018 2352097 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505352.359075 2352097 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505352.369218 2352097 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505352.369244 2352097 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505352.369247 2352097 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505352.369248 2352097 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:49:12.372336: 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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | 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)
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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)
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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)
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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)
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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)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[0. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.73 [%]
Global accuracy score (test) = 69.64 [%]
Global F1 score (train) = 74.58 [%]
Global F1 score (test) = 69.78 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.61      0.49      0.54       400
MODERATE-INTENSITY       0.59      0.80      0.68       400
         SEDENTARY       0.81      0.77      0.79       400
VIGOROUS-INTENSITY       0.84      0.73      0.78       345

          accuracy                           0.70      1545
         macro avg       0.71      0.70      0.70      1545
      weighted avg       0.71      0.70      0.69      1545


Accuracy capturado en la ejecución 11: 69.64 [%]
F1-score capturado en la ejecución 11: 69.78 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
2025-11-07 09:49:27.330208: 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`.
2025-11-07 09:49:27.341609: 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 00:00:1762505367.354769 2352461 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505367.358809 2352461 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505367.369032 2352461 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505367.369052 2352461 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505367.369054 2352461 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505367.369055 2352461 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:49:27.372438: 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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | 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)
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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)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 0. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 73.91 [%]
Global accuracy score (test) = 68.87 [%]
Global F1 score (train) = 73.45 [%]
Global F1 score (test) = 68.63 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.59      0.41      0.48       400
MODERATE-INTENSITY       0.57      0.84      0.68       400
         SEDENTARY       0.83      0.78      0.80       400
VIGOROUS-INTENSITY       0.83      0.74      0.78       345

          accuracy                           0.69      1545
         macro avg       0.70      0.69      0.69      1545
      weighted avg       0.70      0.69      0.68      1545


Accuracy capturado en la ejecución 12: 68.87 [%]
F1-score capturado en la ejecución 12: 68.63 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
2025-11-07 09:49:42.361127: 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`.
2025-11-07 09:49:42.372686: 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 00:00:1762505382.386058 2352811 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505382.390297 2352811 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505382.400083 2352811 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505382.400104 2352811 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505382.400106 2352811 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505382.400107 2352811 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:49:42.403316: 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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | 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)
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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Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.36 [%]
Global accuracy score (test) = 71.0 [%]
Global F1 score (train) = 73.98 [%]
Global F1 score (test) = 70.54 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.66      0.41      0.51       400
MODERATE-INTENSITY       0.58      0.89      0.70       400
         SEDENTARY       0.83      0.81      0.82       400
VIGOROUS-INTENSITY       0.87      0.73      0.79       345

          accuracy                           0.71      1545
         macro avg       0.73      0.71      0.71      1545
      weighted avg       0.73      0.71      0.70      1545


Accuracy capturado en la ejecución 13: 71.0 [%]
F1-score capturado en la ejecución 13: 70.54 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
2025-11-07 09:49:57.338618: 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`.
2025-11-07 09:49:57.349913: 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 00:00:1762505397.362924 2353150 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505397.367048 2353150 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505397.377000 2353150 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505397.377019 2353150 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505397.377021 2353150 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505397.377023 2353150 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:49:57.380192: 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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | 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)
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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)
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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)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[0. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.3 [%]
Global accuracy score (test) = 69.06 [%]
Global F1 score (train) = 74.05 [%]
Global F1 score (test) = 69.59 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.56      0.48      0.52       400
MODERATE-INTENSITY       0.56      0.75      0.64       400
         SEDENTARY       0.85      0.81      0.83       400
VIGOROUS-INTENSITY       0.87      0.73      0.79       345

          accuracy                           0.69      1545
         macro avg       0.71      0.69      0.70      1545
      weighted avg       0.71      0.69      0.69      1545


Accuracy capturado en la ejecución 14: 69.06 [%]
F1-score capturado en la ejecución 14: 69.59 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
2025-11-07 09:50:12.209501: 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`.
2025-11-07 09:50:12.220633: 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 00:00:1762505412.233712 2353486 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505412.237690 2353486 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505412.247615 2353486 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505412.247633 2353486 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505412.247635 2353486 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505412.247637 2353486 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:50:12.250547: 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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | 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)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.88 [%]
Global accuracy score (test) = 70.23 [%]
Global F1 score (train) = 74.69 [%]
Global F1 score (test) = 70.35 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.61      0.49      0.54       400
MODERATE-INTENSITY       0.59      0.78      0.67       400
         SEDENTARY       0.81      0.81      0.81       400
VIGOROUS-INTENSITY       0.85      0.73      0.79       345

          accuracy                           0.70      1545
         macro avg       0.72      0.70      0.70      1545
      weighted avg       0.71      0.70      0.70      1545


Accuracy capturado en la ejecución 15: 70.23 [%]
F1-score capturado en la ejecución 15: 70.35 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
2025-11-07 09:50:27.073552: 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`.
2025-11-07 09:50:27.085012: 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 00:00:1762505427.098160 2353836 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505427.102215 2353836 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505427.112147 2353836 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505427.112166 2353836 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505427.112168 2353836 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505427.112169 2353836 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:50:27.115312: 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 417 out of 417 | elapsed:    2.3s 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 417 out of 417 | 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)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 75.66 [%]
Global accuracy score (test) = 68.48 [%]
Global F1 score (train) = 75.52 [%]
Global F1 score (test) = 68.74 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.58      0.49      0.53       400
MODERATE-INTENSITY       0.59      0.78      0.67       400
         SEDENTARY       0.82      0.74      0.78       400
VIGOROUS-INTENSITY       0.81      0.73      0.77       345

          accuracy                           0.68      1545
         macro avg       0.70      0.69      0.69      1545
      weighted avg       0.69      0.68      0.68      1545


Accuracy capturado en la ejecución 16: 68.48 [%]
F1-score capturado en la ejecución 16: 68.74 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
2025-11-07 09:50:41.894706: 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`.
2025-11-07 09:50:41.906588: 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 00:00:1762505441.920657 2354186 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505441.925034 2354186 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505441.934879 2354186 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505441.934898 2354186 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505441.934900 2354186 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505441.934901 2354186 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:50:41.938039: 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 417 out of 417 | elapsed:    2.5s 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 417 out of 417 | 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)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[0. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.76 [%]
Global accuracy score (test) = 70.87 [%]
Global F1 score (train) = 74.4 [%]
Global F1 score (test) = 70.9 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.62      0.47      0.54       400
MODERATE-INTENSITY       0.59      0.84      0.69       400
         SEDENTARY       0.84      0.80      0.82       400
VIGOROUS-INTENSITY       0.85      0.73      0.79       345

          accuracy                           0.71      1545
         macro avg       0.73      0.71      0.71      1545
      weighted avg       0.72      0.71      0.71      1545


Accuracy capturado en la ejecución 17: 70.87 [%]
F1-score capturado en la ejecución 17: 70.9 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
2025-11-07 09:50:56.836463: 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`.
2025-11-07 09:50:56.847772: 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 00:00:1762505456.861218 2354531 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505456.865274 2354531 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505456.875511 2354531 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505456.875533 2354531 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505456.875535 2354531 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505456.875537 2354531 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:50:56.878628: 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 417 out of 417 | elapsed:    2.5s 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 417 out of 417 | 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)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 0. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.06 [%]
Global accuracy score (test) = 69.06 [%]
Global F1 score (train) = 73.65 [%]
Global F1 score (test) = 69.1 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.59      0.43      0.50       400
MODERATE-INTENSITY       0.56      0.79      0.65       400
         SEDENTARY       0.82      0.82      0.82       400
VIGOROUS-INTENSITY       0.87      0.73      0.79       345

          accuracy                           0.69      1545
         macro avg       0.71      0.69      0.69      1545
      weighted avg       0.70      0.69      0.69      1545


Accuracy capturado en la ejecución 18: 69.06 [%]
F1-score capturado en la ejecución 18: 69.1 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
2025-11-07 09:51:11.832111: 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`.
2025-11-07 09:51:11.843436: 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 00:00:1762505471.856765 2354882 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505471.860800 2354882 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505471.870953 2354882 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505471.870974 2354882 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505471.870976 2354882 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505471.870978 2354882 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:51:11.874101: 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 417 out of 417 | elapsed:    2.5s 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 417 out of 417 | 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)
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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)
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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)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.59 [%]
Global accuracy score (test) = 70.16 [%]
Global F1 score (train) = 74.36 [%]
Global F1 score (test) = 70.15 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.60      0.47      0.52       400
MODERATE-INTENSITY       0.60      0.81      0.69       400
         SEDENTARY       0.81      0.81      0.81       400
VIGOROUS-INTENSITY       0.86      0.73      0.79       345

          accuracy                           0.70      1545
         macro avg       0.71      0.70      0.70      1545
      weighted avg       0.71      0.70      0.70      1545


Accuracy capturado en la ejecución 19: 70.16 [%]
F1-score capturado en la ejecución 19: 70.15 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
2025-11-07 09:51:26.855645: 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`.
2025-11-07 09:51:26.867101: 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 00:00:1762505486.880453 2355235 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505486.884522 2355235 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505486.894973 2355235 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505486.894993 2355235 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505486.894996 2355235 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505486.894997 2355235 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:51:26.898246: 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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | 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)
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)
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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.
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)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[0. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.99 [%]
Global accuracy score (test) = 69.84 [%]
Global F1 score (train) = 74.79 [%]
Global F1 score (test) = 69.92 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.60      0.48      0.53       400
MODERATE-INTENSITY       0.59      0.77      0.67       400
         SEDENTARY       0.81      0.82      0.82       400
VIGOROUS-INTENSITY       0.84      0.73      0.78       345

          accuracy                           0.70      1545
         macro avg       0.71      0.70      0.70      1545
      weighted avg       0.71      0.70      0.70      1545


Accuracy capturado en la ejecución 20: 69.84 [%]
F1-score capturado en la ejecución 20: 69.92 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
2025-11-07 09:51:41.801521: 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`.
2025-11-07 09:51:41.813027: 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 00:00:1762505501.826379 2355587 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505501.830700 2355587 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505501.840871 2355587 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505501.840892 2355587 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505501.840894 2355587 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505501.840896 2355587 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:51:41.844158: 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 417 out of 417 | elapsed:    2.5s 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 417 out of 417 | 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)
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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)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 0. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.73 [%]
Global accuracy score (test) = 68.8 [%]
Global F1 score (train) = 74.52 [%]
Global F1 score (test) = 68.76 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.59      0.44      0.51       400
MODERATE-INTENSITY       0.58      0.80      0.67       400
         SEDENTARY       0.81      0.79      0.80       400
VIGOROUS-INTENSITY       0.83      0.73      0.78       345

          accuracy                           0.69      1545
         macro avg       0.70      0.69      0.69      1545
      weighted avg       0.70      0.69      0.68      1545


Accuracy capturado en la ejecución 21: 68.8 [%]
F1-score capturado en la ejecución 21: 68.76 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
2025-11-07 09:51:56.840982: 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`.
2025-11-07 09:51:56.852181: 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 00:00:1762505516.865169 2355938 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505516.869169 2355938 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505516.878963 2355938 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505516.878982 2355938 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505516.878991 2355938 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505516.878993 2355938 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:51:56.882054: 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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | 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)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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)
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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)
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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)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.98 [%]
Global accuracy score (test) = 68.61 [%]
Global F1 score (train) = 74.75 [%]
Global F1 score (test) = 68.91 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.57      0.44      0.50       400
MODERATE-INTENSITY       0.56      0.78      0.65       400
         SEDENTARY       0.82      0.80      0.81       400
VIGOROUS-INTENSITY       0.88      0.73      0.80       345

          accuracy                           0.69      1545
         macro avg       0.71      0.69      0.69      1545
      weighted avg       0.70      0.69      0.69      1545


Accuracy capturado en la ejecución 22: 68.61 [%]
F1-score capturado en la ejecución 22: 68.91 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
2025-11-07 09:52:11.756720: 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`.
2025-11-07 09:52:11.767965: 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 00:00:1762505531.781196 2356289 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505531.785391 2356289 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505531.795377 2356289 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505531.795396 2356289 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505531.795399 2356289 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505531.795400 2356289 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:52:11.798592: 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 417 out of 417 | elapsed:    2.5s 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 417 out of 417 | 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)
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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)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 0. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.85 [%]
Global accuracy score (test) = 70.23 [%]
Global F1 score (train) = 74.55 [%]
Global F1 score (test) = 70.36 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.61      0.48      0.54       400
MODERATE-INTENSITY       0.58      0.78      0.67       400
         SEDENTARY       0.83      0.82      0.83       400
VIGOROUS-INTENSITY       0.85      0.73      0.78       345

          accuracy                           0.70      1545
         macro avg       0.72      0.70      0.70      1545
      weighted avg       0.71      0.70      0.70      1545


Accuracy capturado en la ejecución 23: 70.23 [%]
F1-score capturado en la ejecución 23: 70.36 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
2025-11-07 09:52:26.757505: 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`.
2025-11-07 09:52:26.768746: 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 00:00:1762505546.782116 2356641 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505546.786312 2356641 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505546.796434 2356641 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505546.796455 2356641 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505546.796458 2356641 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505546.796460 2356641 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:52:26.799593: 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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | 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)
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)
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)
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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Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.03 [%]
Global accuracy score (test) = 69.0 [%]
Global F1 score (train) = 73.62 [%]
Global F1 score (test) = 68.85 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.58      0.41      0.48       400
MODERATE-INTENSITY       0.57      0.81      0.67       400
         SEDENTARY       0.82      0.82      0.82       400
VIGOROUS-INTENSITY       0.86      0.72      0.79       345

          accuracy                           0.69      1545
         macro avg       0.71      0.69      0.69      1545
      weighted avg       0.70      0.69      0.69      1545


Accuracy capturado en la ejecución 24: 69.0 [%]
F1-score capturado en la ejecución 24: 68.85 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
2025-11-07 09:52:41.758914: 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`.
2025-11-07 09:52:41.770557: 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 00:00:1762505561.784075 2356992 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505561.788413 2356992 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505561.798776 2356992 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505561.798797 2356992 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505561.798800 2356992 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505561.798801 2356992 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:52:41.802178: 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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | 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)
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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)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[0. 0. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.62 [%]
Global accuracy score (test) = 69.19 [%]
Global F1 score (train) = 74.39 [%]
Global F1 score (test) = 69.24 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.59      0.46      0.52       400
MODERATE-INTENSITY       0.59      0.81      0.68       400
         SEDENTARY       0.82      0.76      0.79       400
VIGOROUS-INTENSITY       0.83      0.74      0.78       345

          accuracy                           0.69      1545
         macro avg       0.71      0.69      0.69      1545
      weighted avg       0.70      0.69      0.69      1545


Accuracy capturado en la ejecución 25: 69.19 [%]
F1-score capturado en la ejecución 25: 69.24 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
2025-11-07 09:52:56.787023: 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`.
2025-11-07 09:52:56.798778: 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 00:00:1762505576.813019 2357344 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505576.817419 2357344 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505576.827957 2357344 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505576.827975 2357344 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505576.827978 2357344 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505576.827979 2357344 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:52:56.831230: 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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | 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)
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)
This activity can't be balanced (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)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 0. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.54 [%]
Global accuracy score (test) = 69.26 [%]
Global F1 score (train) = 74.43 [%]
Global F1 score (test) = 69.2 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.59      0.45      0.51       400
MODERATE-INTENSITY       0.59      0.80      0.68       400
         SEDENTARY       0.81      0.79      0.80       400
VIGOROUS-INTENSITY       0.83      0.73      0.78       345

          accuracy                           0.69      1545
         macro avg       0.70      0.69      0.69      1545
      weighted avg       0.70      0.69      0.69      1545


Accuracy capturado en la ejecución 26: 69.26 [%]
F1-score capturado en la ejecución 26: 69.2 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
2025-11-07 09:53:11.720421: 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`.
2025-11-07 09:53:11.731628: 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 00:00:1762505591.744762 2357687 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505591.748723 2357687 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505591.758501 2357687 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505591.758523 2357687 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505591.758525 2357687 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505591.758527 2357687 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:53:11.761534: 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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | 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)
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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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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)
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)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 75.12 [%]
Global accuracy score (test) = 68.67 [%]
Global F1 score (train) = 75.05 [%]
Global F1 score (test) = 68.88 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.55      0.43      0.49       400
MODERATE-INTENSITY       0.56      0.76      0.65       400
         SEDENTARY       0.83      0.83      0.83       400
VIGOROUS-INTENSITY       0.87      0.73      0.79       345

          accuracy                           0.69      1545
         macro avg       0.70      0.69      0.69      1545
      weighted avg       0.70      0.69      0.69      1545


Accuracy capturado en la ejecución 27: 68.67 [%]
F1-score capturado en la ejecución 27: 68.88 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
2025-11-07 09:53:26.719535: 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`.
2025-11-07 09:53:26.730947: 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 00:00:1762505606.744304 2358039 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505606.748494 2358039 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505606.758301 2358039 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505606.758326 2358039 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505606.758328 2358039 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505606.758330 2358039 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:53:26.761584: 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 417 out of 417 | elapsed:    2.5s 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 417 out of 417 | 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)
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)
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)
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)
Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.69 [%]
Global accuracy score (test) = 70.16 [%]
Global F1 score (train) = 74.49 [%]
Global F1 score (test) = 70.1 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.61      0.46      0.52       400
MODERATE-INTENSITY       0.59      0.82      0.69       400
         SEDENTARY       0.83      0.81      0.82       400
VIGOROUS-INTENSITY       0.84      0.73      0.78       345

          accuracy                           0.70      1545
         macro avg       0.72      0.70      0.70      1545
      weighted avg       0.71      0.70      0.70      1545


Accuracy capturado en la ejecución 28: 70.16 [%]
F1-score capturado en la ejecución 28: 70.1 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
2025-11-07 09:53:41.692423: 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`.
2025-11-07 09:53:41.703946: 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 00:00:1762505621.717663 2358390 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762505621.721895 2358390 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762505621.731718 2358390 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505621.731738 2358390 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505621.731740 2358390 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762505621.731741 2358390 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:53:41.734869: 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 417 out of 417 | elapsed:    2.4s 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 417 out of 417 | 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)
(13286, 42)
[[1.]
 [1.]
 [1.]
 ...
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(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 75.15 [%]
Global accuracy score (test) = 71.39 [%]
Global F1 score (train) = 74.81 [%]
Global F1 score (test) = 71.3 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.64      0.47      0.55       400
MODERATE-INTENSITY       0.60      0.83      0.70       400
         SEDENTARY       0.81      0.82      0.82       400
VIGOROUS-INTENSITY       0.86      0.73      0.79       345

          accuracy                           0.71      1545
         macro avg       0.73      0.71      0.71      1545
      weighted avg       0.73      0.71      0.71      1545


Accuracy capturado en la ejecución 29: 71.39 [%]
F1-score capturado en la ejecución 29: 71.3 [%]

=== 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}
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Training a non-convolutional model.
Loaded model from disk
(1545, 42)
(13286, 42)
[[1.]
 [1.]
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 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 0. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 74.85 [%]
Global accuracy score (test) = 70.23 [%]
Global F1 score (train) = 74.62 [%]
Global F1 score (test) = 70.47 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.62      0.49      0.55       400
MODERATE-INTENSITY       0.58      0.80      0.67       400
         SEDENTARY       0.83      0.79      0.81       400
VIGOROUS-INTENSITY       0.86      0.73      0.79       345

          accuracy                           0.70      1545
         macro avg       0.72      0.70      0.70      1545
      weighted avg       0.72      0.70      0.70      1545


Accuracy capturado en la ejecución 30: 70.23 [%]
F1-score capturado en la ejecución 30: 70.47 [%]

=== RESUMEN FINAL ===
Accuracies: [69.0, 70.81, 70.1, 67.96, 70.29, 69.58, 69.84, 70.61, 69.45, 69.51, 69.64, 68.87, 71.0, 69.06, 70.23, 68.48, 70.87, 69.06, 70.16, 69.84, 68.8, 68.61, 70.23, 69.0, 69.19, 69.26, 68.67, 70.16, 71.39, 70.23]
F1-scores: [69.24, 70.89, 70.25, 68.22, 70.21, 69.8, 69.99, 70.79, 69.38, 69.41, 69.78, 68.63, 70.54, 69.59, 70.35, 68.74, 70.9, 69.1, 70.15, 69.92, 68.76, 68.91, 70.36, 68.85, 69.24, 69.2, 68.88, 70.1, 71.3, 70.47]
Accuracy mean: 69.6633 | std: 0.8242
F1 mean: 69.7317 | std: 0.7788

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