2025-11-03 16:48:20.730620: 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`.
2025-11-03 16:48:20.742364: 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 00:00:1762184900.756299  295034 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762184900.760599  295034 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762184900.771006  295034 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762184900.771034  295034 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762184900.771037  295034 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762184900.771040  295034 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:48:20.774307: 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(
2025-11-03 16:48:28,980	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-03 16:48:29,709	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-03 16:48:29,773	INFO trial.py:182 -- Creating a new dirname dir_8b39c_3000 because trial dirname 'dir_8b39c' already exists.
2025-11-03 16:48:29,776	INFO trial.py:182 -- Creating a new dirname dir_8b39c_df8c because trial dirname 'dir_8b39c' already exists.
2025-11-03 16:48:29,778	INFO trial.py:182 -- Creating a new dirname dir_8b39c_3beb because trial dirname 'dir_8b39c' already exists.
2025-11-03 16:48:29,780	INFO trial.py:182 -- Creating a new dirname dir_8b39c_fcb6 because trial dirname 'dir_8b39c' already exists.
2025-11-03 16:48:29,782	INFO trial.py:182 -- Creating a new dirname dir_8b39c_ae19 because trial dirname 'dir_8b39c' already exists.
2025-11-03 16:48:29,785	INFO trial.py:182 -- Creating a new dirname dir_8b39c_1a31 because trial dirname 'dir_8b39c' already exists.
2025-11-03 16:48:29,787	INFO trial.py:182 -- Creating a new dirname dir_8b39c_bcd3 because trial dirname 'dir_8b39c' already exists.
2025-11-03 16:48:29,790	INFO trial.py:182 -- Creating a new dirname dir_8b39c_5836 because trial dirname 'dir_8b39c' already exists.
2025-11-03 16:48:29,793	INFO trial.py:182 -- Creating a new dirname dir_8b39c_05b0 because trial dirname 'dir_8b39c' already exists.
2025-11-03 16:48:29,795	INFO trial.py:182 -- Creating a new dirname dir_8b39c_b4f2 because trial dirname 'dir_8b39c' already exists.
2025-11-03 16:48:29,798	INFO trial.py:182 -- Creating a new dirname dir_8b39c_d0af because trial dirname 'dir_8b39c' already exists.
2025-11-03 16:48:29,801	INFO trial.py:182 -- Creating a new dirname dir_8b39c_51cb because trial dirname 'dir_8b39c' already exists.
2025-11-03 16:48:29,804	INFO trial.py:182 -- Creating a new dirname dir_8b39c_38e1 because trial dirname 'dir_8b39c' already exists.
2025-11-03 16:48:29,809	INFO trial.py:182 -- Creating a new dirname dir_8b39c_65b0 because trial dirname 'dir_8b39c' already exists.
2025-11-03 16:48:29,816	INFO trial.py:182 -- Creating a new dirname dir_8b39c_ce82 because trial dirname 'dir_8b39c' already exists.
2025-11-03 16:48:29,820	INFO trial.py:182 -- Creating a new dirname dir_8b39c_a01e because trial dirname 'dir_8b39c' already exists.
2025-11-03 16:48:29,824	INFO trial.py:182 -- Creating a new dirname dir_8b39c_1ccb because trial dirname 'dir_8b39c' already exists.
2025-11-03 16:48:29,828	INFO trial.py:182 -- Creating a new dirname dir_8b39c_9d95 because trial dirname 'dir_8b39c' already exists.
2025-11-03 16:48:29,833	INFO trial.py:182 -- Creating a new dirname dir_8b39c_e99f because trial dirname 'dir_8b39c' already exists.
1 GPU(s) detected and VRAM set to crossover mode..
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Training a non-convolutional model.
Se lanza la búsqueda de hiperparámetros óptimos del modelo
╭──────────────────────────────────────────────────────────────────────╮
│ Configuration for experiment     BalancedRF_hyperparameters_tuning   │
├──────────────────────────────────────────────────────────────────────┤
│ Search algorithm                 BasicVariantGenerator               │
│ Scheduler                        AsyncHyperBandScheduler             │
│ Number of trials                 20                                  │
╰──────────────────────────────────────────────────────────────────────╯

View detailed results here: /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_BRF_acc_gyr_superclasses_CPA_METs/BalancedRF_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-03_16-48-28_262059_295034/artifacts/2025-11-03_16-48-29/BalancedRF_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-03 16:48:29. 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_8b39c    PENDING               465             7                    34                   17   sqrt                       9183 │
│ trial_8b39c    PENDING               452             6                    25                   20   0.3                        2120 │
│ trial_8b39c    PENDING               401             5                    26                   18   sqrt                       5661 │
│ trial_8b39c    PENDING               477             5                    31                   29   0.3                        1707 │
│ trial_8b39c    PENDING               218             5                    30                   17   0.3                        7005 │
│ trial_8b39c    PENDING               303             7                    38                   17   0.3                        4653 │
│ trial_8b39c    PENDING               335             5                    43                   13   0.3                         747 │
│ trial_8b39c    PENDING               281             5                    52                   27   sqrt                       3974 │
│ trial_8b39c    PENDING               327             7                    43                   13   0.3                        9902 │
│ trial_8b39c    PENDING               253             6                    34                   16   sqrt                        715 │
│ trial_8b39c    PENDING               348             5                    54                   10   sqrt                        126 │
│ trial_8b39c    PENDING               372             5                    38                   26   sqrt                       5985 │
│ trial_8b39c    PENDING               448             5                    39                   28   sqrt                       5271 │
│ trial_8b39c    PENDING               449             6                    56                   15   sqrt                       7552 │
│ trial_8b39c    PENDING               251             6                    30                   25   0.3                        7123 │
│ trial_8b39c    PENDING               354             5                    35                   27   0.3                        7977 │
│ trial_8b39c    PENDING               373             5                    50                   23   0.3                        1471 │
│ trial_8b39c    PENDING               295             7                    55                   26   0.3                        3173 │
│ trial_8b39c    PENDING               212             5                    20                   27   0.3                        4777 │
│ trial_8b39c    PENDING               333             7                    59                   10   0.3                        9352 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_8b39c started with configuration:
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               17 │
│ min_samples_split              38 │
│ n_estimators                  303 │
│ random_state                 4653 │
╰───────────────────────────────────╯
Trial trial_8b39c started with configuration:
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               16 │
│ min_samples_split              34 │
│ n_estimators                  253 │
│ random_state                  715 │
╰───────────────────────────────────╯
Trial trial_8b39c started with configuration:
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               15 │
│ min_samples_split              56 │
│ n_estimators                  449 │
│ random_state                 7552 │
╰───────────────────────────────────╯
Trial trial_8b39c started with configuration:
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               18 │
│ min_samples_split              26 │
│ n_estimators                  401 │
│ random_state                 5661 │
╰───────────────────────────────────╯
Trial trial_8b39c started with configuration:
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               29 │
│ min_samples_split              31 │
│ n_estimators                  477 │
│ random_state                 1707 │
╰───────────────────────────────────╯
Trial trial_8b39c started with configuration:
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               26 │
│ min_samples_split              38 │
│ n_estimators                  372 │
│ random_state                 5985 │
╰───────────────────────────────────╯
Trial trial_8b39c started with configuration:
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               25 │
│ min_samples_split              30 │
│ n_estimators                  251 │
│ random_state                 7123 │
╰───────────────────────────────────╯
Trial trial_8b39c started with configuration:
╭──────────────────────────────────╮
│ Trial trial_8b39c config         │
├──────────────────────────────────┤
│ max_depth                      5 │
│ max_features                 0.3 │
│ min_samples_leaf              13 │
│ min_samples_split             43 │
│ n_estimators                 335 │
│ random_state                 747 │
╰──────────────────────────────────╯
Trial trial_8b39c started with configuration:
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               10 │
│ min_samples_split              54 │
│ n_estimators                  348 │
│ random_state                  126 │
╰───────────────────────────────────╯
Trial trial_8b39c started with configuration:
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               27 │
│ min_samples_split              52 │
│ n_estimators                  281 │
│ random_state                 3974 │
╰───────────────────────────────────╯
Trial trial_8b39c started with configuration:
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               28 │
│ min_samples_split              39 │
│ n_estimators                  448 │
│ random_state                 5271 │
╰───────────────────────────────────╯
Trial trial_8b39c started with configuration:
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               17 │
│ min_samples_split              34 │
│ n_estimators                  465 │
│ random_state                 9183 │
╰───────────────────────────────────╯
Trial trial_8b39c started with configuration:
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               26 │
│ min_samples_split              55 │
│ n_estimators                  295 │
│ random_state                 3173 │
╰───────────────────────────────────╯
Trial trial_8b39c started with configuration:
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               20 │
│ min_samples_split              25 │
│ n_estimators                  452 │
│ random_state                 2120 │
╰───────────────────────────────────╯
Trial trial_8b39c started with configuration:
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               13 │
│ min_samples_split              43 │
│ n_estimators                  327 │
│ random_state                 9902 │
╰───────────────────────────────────╯
Trial trial_8b39c started with configuration:
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               23 │
│ min_samples_split              50 │
│ n_estimators                  373 │
│ random_state                 1471 │
╰───────────────────────────────────╯
Trial trial_8b39c started with configuration:
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               17 │
│ min_samples_split              30 │
│ n_estimators                  218 │
│ random_state                 7005 │
╰───────────────────────────────────╯
Trial trial_8b39c started with configuration:
[36m(train_brf_ray_tune pid=296719)[0m 2025-11-03 16:48:33.088252: 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=296719)[0m 2025-11-03 16:48:33.110371: 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=296719)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_brf_ray_tune pid=296719)[0m E0000 00:00:1762184913.139137  297832 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=296719)[0m E0000 00:00:1762184913.147375  297832 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=296719)[0m W0000 00:00:1762184913.167937  297832 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=296719)[0m W0000 00:00:1762184913.167994  297832 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=296719)[0m W0000 00:00:1762184913.167996  297832 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=296719)[0m W0000 00:00:1762184913.167998  297832 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=296719)[0m 2025-11-03 16:48:33.174197: 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=296719)[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=296719)[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=296719)[0m   return fit_method(estimator, *args, **kwargs)
[36m(train_brf_ray_tune pid=296719)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=296718)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.4s
[36m(train_brf_ray_tune pid=296709)[0m 2025-11-03 16:48:33.825427: 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=296710)[0m 2025-11-03 16:48:33.887039: 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=296710)[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=296710)[0m E0000 00:00:1762184913.916624  297990 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=296710)[0m E0000 00:00:1762184913.924975  297990 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=296709)[0m W0000 00:00:1762184913.913178  297981 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=296709)[0m 2025-11-03 16:48:33.919777: 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=296709)[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=296709)[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=296709)[0m   return fit_method(estimator, *args, **kwargs)[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=296724)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=296684)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    4.1s[32m [repeated 13x across cluster][0m
[36m(train_brf_ray_tune pid=296720)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   10.4s[32m [repeated 8x across cluster][0m
[36m(train_brf_ray_tune pid=296718)[0m [Parallel(n_jobs=-1)]: Done 253 out of 253 | elapsed:   14.8s finished
[36m(train_brf_ray_tune pid=296718)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=296718)[0m [Parallel(n_jobs=20)]: Done 253 out of 253 | elapsed:    0.1s finished
[36m(train_brf_ray_tune pid=296718)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=296718)[0m [Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.5s[32m [repeated 6x across cluster][0m
[36m(train_brf_ray_tune pid=296718)[0m [Parallel(n_jobs=20)]: Done 253 out of 253 | elapsed:    1.1s finished
[36m(train_brf_ray_tune pid=296718)[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=296718)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=296721)[0m [Parallel(n_jobs=-1)]: Done 281 out of 281 | elapsed:   21.5s finished
[36m(train_brf_ray_tune pid=296724)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   16.3s[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=296721)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=296721)[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=296721)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=296692)[0m [Parallel(n_jobs=20)]: Done 401 out of 401 | elapsed:    0.8s finished[32m [repeated 9x across cluster][0m
[36m(train_brf_ray_tune pid=296726)[0m [Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.2s[32m [repeated 15x across cluster][0m
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               10 │
│ min_samples_split              59 │
│ n_estimators                  333 │
│ random_state                 9352 │
╰───────────────────────────────────╯
Trial trial_8b39c started with configuration:
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               27 │
│ min_samples_split              35 │
│ n_estimators                  354 │
│ random_state                 7977 │
╰───────────────────────────────────╯
Trial trial_8b39c started with configuration:
╭───────────────────────────────────╮
│ Trial trial_8b39c config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               27 │
│ min_samples_split              20 │
│ n_estimators                  212 │
│ random_state                 4777 │
╰───────────────────────────────────╯

Trial trial_8b39c finished iteration 1 at 2025-11-03 16:48:53. Total running time: 23s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             20.8368 │
│ time_total_s                 20.8368 │
│ training_iteration                 1 │
│ test_accuracy                0.60841 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:48:53. Total running time: 23s

Trial status: 19 RUNNING | 1 TERMINATED
Current time: 2025-11-03 16:49:00. Total running time: 30s
Logical resource usage: 19.0/20 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_8b39c    RUNNING                 465             7                    34                   17   sqrt                       9183                                               │
│ trial_8b39c    RUNNING                 452             6                    25                   20   0.3                        2120                                               │
│ trial_8b39c    RUNNING                 401             5                    26                   18   sqrt                       5661                                               │
│ trial_8b39c    RUNNING                 477             5                    31                   29   0.3                        1707                                               │
│ trial_8b39c    RUNNING                 218             5                    30                   17   0.3                        7005                                               │
│ trial_8b39c    RUNNING                 303             7                    38                   17   0.3                        4653                                               │
│ trial_8b39c    RUNNING                 335             5                    43                   13   0.3                         747                                               │
│ trial_8b39c    RUNNING                 281             5                    52                   27   sqrt                       3974                                               │
│ trial_8b39c    RUNNING                 327             7                    43                   13   0.3                        9902                                               │
│ trial_8b39c    RUNNING                 348             5                    54                   10   sqrt                        126                                               │
│ trial_8b39c    RUNNING                 372             5                    38                   26   sqrt                       5985                                               │
│ trial_8b39c    RUNNING                 448             5                    39                   28   sqrt                       5271                                               │
│ trial_8b39c    RUNNING                 449             6                    56                   15   sqrt                       7552                                               │
│ trial_8b39c    RUNNING                 251             6                    30                   25   0.3                        7123                                               │
│ trial_8b39c    RUNNING                 354             5                    35                   27   0.3                        7977                                               │
│ trial_8b39c    RUNNING                 373             5                    50                   23   0.3                        1471                                               │
│ trial_8b39c    RUNNING                 295             7                    55                   26   0.3                        3173                                               │
│ trial_8b39c    RUNNING                 212             5                    20                   27   0.3                        4777                                               │
│ trial_8b39c    RUNNING                 333             7                    59                   10   0.3                        9352                                               │
│ trial_8b39c    TERMINATED              253             6                    34                   16   sqrt                        715        1            20.8368          0.608414 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:03. Total running time: 33s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             30.6205 │
│ time_total_s                 30.6205 │
│ training_iteration                 1 │
│ test_accuracy                0.58964 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:03. Total running time: 33s

Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:04. Total running time: 34s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             31.1189 │
│ time_total_s                 31.1189 │
│ training_iteration                 1 │
│ test_accuracy                0.59547 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:04. Total running time: 34s

Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:05. Total running time: 35s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             32.7903 │
│ time_total_s                 32.7903 │
│ training_iteration                 1 │
│ test_accuracy                0.59482 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:05. Total running time: 35s

Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:07. Total running time: 37s
[36m(train_brf_ray_tune pid=296726)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 7x across cluster][0m
[36m(train_brf_ray_tune pid=296726)[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=296726)[0m   _log_deprecation_warning([32m [repeated 3x across cluster][0m
[36m(train_brf_ray_tune pid=296711)[0m [Parallel(n_jobs=20)]: Done 448 out of 448 | elapsed:    0.3s finished[32m [repeated 5x across cluster][0m
[36m(train_brf_ray_tune pid=296684)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   33.7s[32m [repeated 14x across cluster][0m
[36m(train_brf_ray_tune pid=296711)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=296711)[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=296711)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=296722)[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=296722)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=296686)[0m [Parallel(n_jobs=-1)]: Done 465 out of 465 | elapsed:   38.4s finished[32m [repeated 5x across cluster][0m
[36m(train_brf_ray_tune pid=296724)[0m [Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.5s[32m [repeated 10x across cluster][0m
[36m(train_brf_ray_tune pid=296686)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 7x across cluster][0m
[36m(train_brf_ray_tune pid=296686)[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=296686)[0m   _log_deprecation_warning([32m [repeated 3x across cluster][0m
[36m(train_brf_ray_tune pid=296684)[0m [Parallel(n_jobs=-1)]: Done 251 out of 251 | elapsed:   44.7s finished[32m [repeated 8x across cluster][0m
[36m(train_brf_ray_tune pid=296686)[0m [Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.6s[32m [repeated 15x across cluster][0m
[36m(train_brf_ray_tune pid=296684)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 3x across cluster][0m
[36m(train_brf_ray_tune pid=296717)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=296717)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=296719)[0m [Parallel(n_jobs=-1)]: Done 303 out of 303 | elapsed:   51.4s finished[32m [repeated 7x across cluster][0m
[36m(train_brf_ray_tune pid=296708)[0m [Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:   50.7s[32m [repeated 9x across cluster][0m
[36m(train_brf_ray_tune pid=296727)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 8x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             34.6251 │
│ time_total_s                 34.6251 │
│ training_iteration                 1 │
│ test_accuracy                0.59094 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:07. Total running time: 37s

Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:10. Total running time: 40s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             37.3309 │
│ time_total_s                 37.3309 │
│ training_iteration                 1 │
│ test_accuracy                0.59353 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:10. Total running time: 40s

Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:14. Total running time: 45s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             41.7529 │
│ time_total_s                 41.7529 │
│ training_iteration                 1 │
│ test_accuracy                0.59547 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:14. Total running time: 45s

Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:18. Total running time: 48s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             45.3333 │
│ time_total_s                 45.3333 │
│ training_iteration                 1 │
│ test_accuracy                0.60906 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:18. Total running time: 48s

Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:18. Total running time: 48s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             45.0451 │
│ time_total_s                 45.0451 │
│ training_iteration                 1 │
│ test_accuracy                0.59159 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:18. Total running time: 48s

Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:19. Total running time: 49s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             46.6507 │
│ time_total_s                 46.6507 │
│ training_iteration                 1 │
│ test_accuracy                0.62006 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:19. Total running time: 49s

Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:24. Total running time: 54s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              51.461 │
│ time_total_s                  51.461 │
│ training_iteration                 1 │
│ test_accuracy                0.61748 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:24. Total running time: 54s

Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:27. Total running time: 57s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             54.8454 │
│ time_total_s                 54.8454 │
│ training_iteration                 1 │
│ test_accuracy                0.59741 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:27. Total running time: 58s

Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:28. Total running time: 59s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             55.8728 │
│ time_total_s                 55.8728 │
│ training_iteration                 1 │
│ test_accuracy                0.59935 │
╰──────────────────────────────────────╯
Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:28. Total running time: 59s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             55.9286 │
│ time_total_s                 55.9286 │
│ training_iteration                 1 │
│ test_accuracy                0.61553 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:28. Total running time: 59s
Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:28. Total running time: 59s

Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:29. Total running time: 59s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             56.3736 │
│ time_total_s                 56.3736 │
│ training_iteration                 1 │
│ test_accuracy                0.61359 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:29. Total running time: 59s

Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:29. Total running time: 59s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             56.3534 │
│ time_total_s                 56.3534 │
│ training_iteration                 1 │
│ test_accuracy                0.59676 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:29. Total running time: 59s

Trial status: 16 TERMINATED | 4 RUNNING
Current time: 2025-11-03 16:49:30. Total running time: 1min 0s
Logical resource usage: 4.0/20 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_8b39c    RUNNING                 452             6                    25                   20   0.3                        2120                                               │
│ trial_8b39c    RUNNING                 477             5                    31                   29   0.3                        1707                                               │
│ trial_8b39c    RUNNING                 327             7                    43                   13   0.3                        9902                                               │
│ trial_8b39c    RUNNING                 333             7                    59                   10   0.3                        9352                                               │
│ trial_8b39c    TERMINATED              465             7                    34                   17   sqrt                       9183        1            46.6507          0.620065 │
│ trial_8b39c    TERMINATED              401             5                    26                   18   sqrt                       5661        1            32.7903          0.594822 │
│ trial_8b39c    TERMINATED              218             5                    30                   17   0.3                        7005        1            41.7529          0.595469 │
│ trial_8b39c    TERMINATED              303             7                    38                   17   0.3                        4653        1            56.3736          0.613592 │
│ trial_8b39c    TERMINATED              335             5                    43                   13   0.3                         747        1            54.8454          0.597411 │
│ trial_8b39c    TERMINATED              281             5                    52                   27   sqrt                       3974        1            30.6205          0.589644 │
│ trial_8b39c    TERMINATED              253             6                    34                   16   sqrt                        715        1            20.8368          0.608414 │
│ trial_8b39c    TERMINATED              348             5                    54                   10   sqrt                        126        1            31.1189          0.595469 │
│ trial_8b39c    TERMINATED              372             5                    38                   26   sqrt                       5985        1            34.6251          0.590939 │
│ trial_8b39c    TERMINATED              448             5                    39                   28   sqrt                       5271        1            37.3309          0.593528 │
│ trial_8b39c    TERMINATED              449             6                    56                   15   sqrt                       7552        1            45.3333          0.609061 │
│ trial_8b39c    TERMINATED              251             6                    30                   25   0.3                        7123        1            51.461           0.617476 │
│ trial_8b39c    TERMINATED              354             5                    35                   27   0.3                        7977        1            56.3534          0.596764 │
│ trial_8b39c    TERMINATED              373             5                    50                   23   0.3                        1471        1            55.8728          0.599353 │
│ trial_8b39c    TERMINATED              295             7                    55                   26   0.3                        3173        1            55.9286          0.615534 │
│ trial_8b39c    TERMINATED              212             5                    20                   27   0.3                        4777        1            45.0451          0.591586 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:30. Total running time: 1min 0s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             57.0912 │
│ time_total_s                 57.0912 │
│ training_iteration                 1 │
│ test_accuracy                0.60971 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:30. Total running time: 1min 0s

2025-11-03 16:49:31,159	INFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_BRF_acc_gyr_superclasses_CPA_METs/BalancedRF_hyperparameters_tuning' in 0.0052s.
/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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.2s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | elapsed:    0.1s finished
Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:30. Total running time: 1min 0s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             57.2338 │
│ time_total_s                 57.2338 │
│ training_iteration                 1 │
│ test_accuracy                0.61165 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:30. Total running time: 1min 0s

Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:30. Total running time: 1min 0s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             57.8491 │
│ time_total_s                 57.8491 │
│ training_iteration                 1 │
│ test_accuracy                0.58835 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:30. Total running time: 1min 0s

Trial trial_8b39c finished iteration 1 at 2025-11-03 16:49:31. Total running time: 1min 1s
╭──────────────────────────────────────╮
│ Trial trial_8b39c result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             58.1251 │
│ time_total_s                 58.1251 │
│ training_iteration                 1 │
│ test_accuracy                0.61812 │
╰──────────────────────────────────────╯

Trial trial_8b39c completed after 1 iterations at 2025-11-03 16:49:31. Total running time: 1min 1s

Trial status: 20 TERMINATED
Current time: 2025-11-03 16:49:31. Total running time: 1min 1s
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_8b39c    TERMINATED              465             7                    34                   17   sqrt                       9183        1            46.6507          0.620065 │
│ trial_8b39c    TERMINATED              452             6                    25                   20   0.3                        2120        1            58.1251          0.618123 │
│ trial_8b39c    TERMINATED              401             5                    26                   18   sqrt                       5661        1            32.7903          0.594822 │
│ trial_8b39c    TERMINATED              477             5                    31                   29   0.3                        1707        1            57.8491          0.58835  │
│ trial_8b39c    TERMINATED              218             5                    30                   17   0.3                        7005        1            41.7529          0.595469 │
│ trial_8b39c    TERMINATED              303             7                    38                   17   0.3                        4653        1            56.3736          0.613592 │
│ trial_8b39c    TERMINATED              335             5                    43                   13   0.3                         747        1            54.8454          0.597411 │
│ trial_8b39c    TERMINATED              281             5                    52                   27   sqrt                       3974        1            30.6205          0.589644 │
│ trial_8b39c    TERMINATED              327             7                    43                   13   0.3                        9902        1            57.2338          0.61165  │
│ trial_8b39c    TERMINATED              253             6                    34                   16   sqrt                        715        1            20.8368          0.608414 │
│ trial_8b39c    TERMINATED              348             5                    54                   10   sqrt                        126        1            31.1189          0.595469 │
│ trial_8b39c    TERMINATED              372             5                    38                   26   sqrt                       5985        1            34.6251          0.590939 │
│ trial_8b39c    TERMINATED              448             5                    39                   28   sqrt                       5271        1            37.3309          0.593528 │
│ trial_8b39c    TERMINATED              449             6                    56                   15   sqrt                       7552        1            45.3333          0.609061 │
│ trial_8b39c    TERMINATED              251             6                    30                   25   0.3                        7123        1            51.461           0.617476 │
│ trial_8b39c    TERMINATED              354             5                    35                   27   0.3                        7977        1            56.3534          0.596764 │
│ trial_8b39c    TERMINATED              373             5                    50                   23   0.3                        1471        1            55.8728          0.599353 │
│ trial_8b39c    TERMINATED              295             7                    55                   26   0.3                        3173        1            55.9286          0.615534 │
│ trial_8b39c    TERMINATED              212             5                    20                   27   0.3                        4777        1            45.0451          0.591586 │
│ trial_8b39c    TERMINATED              333             7                    59                   10   0.3                        9352        1            57.0912          0.609709 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'n_estimators': 465, 'max_depth': 7, 'min_samples_split': 34, 'min_samples_leaf': 17, 'max_features': 'sqrt', 'random_state': 9183}
Saved model to disk
[36m(train_brf_ray_tune pid=296685)[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 8x across cluster][0m
[36m(train_brf_ray_tune pid=296685)[0m   _log_deprecation_warning([32m [repeated 8x across cluster][0m
[36m(train_brf_ray_tune pid=296685)[0m [Parallel(n_jobs=20)]: Done 452 out of 452 | elapsed:    0.1s finished[32m [repeated 22x across cluster][0m
[36m(train_brf_ray_tune pid=296685)[0m [Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s[32m [repeated 37x across cluster][0m
[36m(train_brf_ray_tune pid=296685)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 10x across cluster][0m
2025-11-03 16:49:47.497285: 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`.
2025-11-03 16:49:47.508542: 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 00:00:1762184987.521934  300364 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762184987.526006  300364 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762184987.536010  300364 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762184987.536035  300364 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762184987.536039  300364 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762184987.536042  300364 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:49:47.539118: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.2s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[1. 0. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.71 [%]
Global accuracy score (test) = 61.75 [%]
Global F1 score (train) = 68.79 [%]
Global F1 score (test) = 61.62 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.47      0.37      0.41       400
MODERATE-INTENSITY       0.55      0.74      0.63       400
         SEDENTARY       0.69      0.71      0.70       400
VIGOROUS-INTENSITY       0.80      0.65      0.72       345

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


Accuracy capturado en la ejecución 1: 61.75 [%]
F1-score capturado en la ejecución 1: 61.62 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
2025-11-03 16:50:09.361414: 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`.
2025-11-03 16:50:09.373426: 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 00:00:1762185009.387832  300766 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185009.392256  300766 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185009.402773  300766 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185009.402802  300766 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185009.402805  300766 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185009.402809  300766 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:50:09.406071: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.1s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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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, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.03 [%]
Global accuracy score (test) = 62.39 [%]
Global F1 score (train) = 68.0 [%]
Global F1 score (test) = 62.37 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.49      0.38      0.43       400
MODERATE-INTENSITY       0.55      0.73      0.63       400
         SEDENTARY       0.69      0.73      0.71       400
VIGOROUS-INTENSITY       0.82      0.66      0.73       345

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


Accuracy capturado en la ejecución 2: 62.39 [%]
F1-score capturado en la ejecución 2: 62.37 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
2025-11-03 16:50:31.174608: 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`.
2025-11-03 16:50:31.186258: 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 00:00:1762185031.199970  301191 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185031.204230  301191 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185031.214539  301191 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185031.214566  301191 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185031.214569  301191 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185031.214570  301191 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:50:31.217847: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.1s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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)
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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, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.16 [%]
Global accuracy score (test) = 61.75 [%]
Global F1 score (train) = 68.22 [%]
Global F1 score (test) = 61.85 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.48      0.40      0.44       400
MODERATE-INTENSITY       0.56      0.72      0.63       400
         SEDENTARY       0.68      0.70      0.69       400
VIGOROUS-INTENSITY       0.80      0.65      0.72       345

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


Accuracy capturado en la ejecución 3: 61.75 [%]
F1-score capturado en la ejecución 3: 61.85 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
2025-11-03 16:50:52.929069: 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`.
2025-11-03 16:50:52.940509: 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 00:00:1762185052.954087  301569 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185052.958245  301569 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185052.968471  301569 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185052.968501  301569 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185052.968504  301569 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185052.968506  301569 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:50:52.971819: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.1s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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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)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 0. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 67.92 [%]
Global accuracy score (test) = 61.49 [%]
Global F1 score (train) = 68.07 [%]
Global F1 score (test) = 61.66 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.46      0.39      0.42       400
MODERATE-INTENSITY       0.56      0.70      0.62       400
         SEDENTARY       0.67      0.72      0.70       400
VIGOROUS-INTENSITY       0.83      0.65      0.73       345

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


Accuracy capturado en la ejecución 4: 61.49 [%]
F1-score capturado en la ejecución 4: 61.66 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
2025-11-03 16:51:15.007309: 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`.
2025-11-03 16:51:15.019237: 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 00:00:1762185075.033050  301953 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185075.037234  301953 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185075.047666  301953 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185075.047693  301953 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185075.047703  301953 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185075.047705  301953 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:51:15.051033: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.1s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't 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)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.12 [%]
Global accuracy score (test) = 61.75 [%]
Global F1 score (train) = 68.09 [%]
Global F1 score (test) = 61.47 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.48      0.34      0.40       400
MODERATE-INTENSITY       0.54      0.73      0.62       400
         SEDENTARY       0.69      0.74      0.71       400
VIGOROUS-INTENSITY       0.81      0.66      0.72       345

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


Accuracy capturado en la ejecución 5: 61.75 [%]
F1-score capturado en la ejecución 5: 61.47 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
2025-11-03 16:51:37.035352: 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`.
2025-11-03 16:51:37.047161: 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 00:00:1762185097.060622  302365 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185097.064914  302365 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185097.074915  302365 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185097.074941  302365 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185097.074943  302365 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185097.074945  302365 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:51:37.078315: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.2s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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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)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[1. 0. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.24 [%]
Global accuracy score (test) = 62.01 [%]
Global F1 score (train) = 68.32 [%]
Global F1 score (test) = 62.21 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.46      0.39      0.42       400
MODERATE-INTENSITY       0.55      0.74      0.63       400
         SEDENTARY       0.70      0.71      0.70       400
VIGOROUS-INTENSITY       0.84      0.65      0.74       345

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


Accuracy capturado en la ejecución 6: 62.01 [%]
F1-score capturado en la ejecución 6: 62.21 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
2025-11-03 16:51:59.215975: 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`.
2025-11-03 16:51:59.227640: 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 00:00:1762185119.241329  302751 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185119.245653  302751 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185119.255846  302751 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185119.255870  302751 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185119.255873  302751 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185119.255874  302751 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:51:59.259247: 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 410 tasks      | elapsed:    2.2s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.14 [%]
Global accuracy score (test) = 60.91 [%]
Global F1 score (train) = 68.23 [%]
Global F1 score (test) = 60.81 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.47      0.35      0.40       400
MODERATE-INTENSITY       0.53      0.72      0.61       400
         SEDENTARY       0.69      0.72      0.70       400
VIGOROUS-INTENSITY       0.80      0.65      0.72       345

          accuracy                           0.61      1545
         macro avg       0.62      0.61      0.61      1545
      weighted avg       0.61      0.61      0.60      1545


Accuracy capturado en la ejecución 7: 60.91 [%]
F1-score capturado en la ejecución 7: 60.81 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
2025-11-03 16:52:20.984373: 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`.
2025-11-03 16:52:20.996129: 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 00:00:1762185141.010039  303140 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185141.014322  303140 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185141.024223  303140 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185141.024248  303140 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185141.024250  303140 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185141.024252  303140 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:52:21.027641: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.1s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 0. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 67.83 [%]
Global accuracy score (test) = 61.17 [%]
Global F1 score (train) = 67.74 [%]
Global F1 score (test) = 60.6 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.46      0.31      0.37       400
MODERATE-INTENSITY       0.53      0.77      0.63       400
         SEDENTARY       0.70      0.72      0.71       400
VIGOROUS-INTENSITY       0.80      0.66      0.72       345

          accuracy                           0.61      1545
         macro avg       0.62      0.61      0.61      1545
      weighted avg       0.61      0.61      0.60      1545


Accuracy capturado en la ejecución 8: 61.17 [%]
F1-score capturado en la ejecución 8: 60.6 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
2025-11-03 16:52:43.066407: 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`.
2025-11-03 16:52:43.078151: 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 00:00:1762185163.091969  303550 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185163.096379  303550 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185163.106621  303550 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185163.106643  303550 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185163.106646  303550 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185163.106647  303550 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:52:43.110054: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.1s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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)
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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, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.55 [%]
Global accuracy score (test) = 62.33 [%]
Global F1 score (train) = 68.59 [%]
Global F1 score (test) = 62.17 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.49      0.38      0.43       400
MODERATE-INTENSITY       0.55      0.72      0.62       400
         SEDENTARY       0.70      0.74      0.72       400
VIGOROUS-INTENSITY       0.80      0.66      0.72       345

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


Accuracy capturado en la ejecución 9: 62.33 [%]
F1-score capturado en la ejecución 9: 62.17 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
2025-11-03 16:53:04.955309: 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`.
2025-11-03 16:53:04.967063: 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 00:00:1762185184.980557  303931 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185184.984626  303931 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185184.994755  303931 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185184.994780  303931 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185184.994782  303931 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185184.994784  303931 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:53:04.998056: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.2s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.21 [%]
Global accuracy score (test) = 60.45 [%]
Global F1 score (train) = 68.31 [%]
Global F1 score (test) = 60.63 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.44      0.36      0.40       400
MODERATE-INTENSITY       0.53      0.70      0.61       400
         SEDENTARY       0.68      0.71      0.70       400
VIGOROUS-INTENSITY       0.82      0.66      0.73       345

          accuracy                           0.60      1545
         macro avg       0.62      0.61      0.61      1545
      weighted avg       0.61      0.60      0.60      1545


Accuracy capturado en la ejecución 10: 60.45 [%]
F1-score capturado en la ejecución 10: 60.63 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
2025-11-03 16:53:26.858089: 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`.
2025-11-03 16:53:26.869818: 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 00:00:1762185206.883504  304306 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185206.887749  304306 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185206.897883  304306 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185206.897907  304306 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185206.897912  304306 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185206.897913  304306 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:53:26.901200: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.1s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[0. 2. 1. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.06 [%]
Global accuracy score (test) = 63.5 [%]
Global F1 score (train) = 68.13 [%]
Global F1 score (test) = 63.75 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.49      0.43      0.46       400
MODERATE-INTENSITY       0.57      0.73      0.64       400
         SEDENTARY       0.71      0.73      0.72       400
VIGOROUS-INTENSITY       0.84      0.65      0.73       345

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


Accuracy capturado en la ejecución 11: 63.5 [%]
F1-score capturado en la ejecución 11: 63.75 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
2025-11-03 16:53:49.033542: 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`.
2025-11-03 16:53:49.045202: 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 00:00:1762185229.059158  304709 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185229.063632  304709 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185229.073694  304709 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185229.073716  304709 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185229.073719  304709 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185229.073721  304709 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:53:49.077199: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.1s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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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, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.38 [%]
Global accuracy score (test) = 62.2 [%]
Global F1 score (train) = 68.45 [%]
Global F1 score (test) = 62.36 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.47      0.40      0.43       400
MODERATE-INTENSITY       0.55      0.69      0.61       400
         SEDENTARY       0.69      0.76      0.72       400
VIGOROUS-INTENSITY       0.82      0.65      0.73       345

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


Accuracy capturado en la ejecución 12: 62.2 [%]
F1-score capturado en la ejecución 12: 62.36 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
2025-11-03 16:54:11.205169: 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`.
2025-11-03 16:54:11.217186: 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 00:00:1762185251.230995  305106 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185251.235081  305106 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185251.245322  305106 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185251.245345  305106 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185251.245348  305106 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185251.245350  305106 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:54:11.248467: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.1s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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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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, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 0. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 67.84 [%]
Global accuracy score (test) = 61.75 [%]
Global F1 score (train) = 67.9 [%]
Global F1 score (test) = 61.92 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.46      0.38      0.42       400
MODERATE-INTENSITY       0.55      0.74      0.63       400
         SEDENTARY       0.68      0.70      0.69       400
VIGOROUS-INTENSITY       0.85      0.65      0.74       345

          accuracy                           0.62      1545
         macro avg       0.64      0.62      0.62      1545
      weighted avg       0.63      0.62      0.61      1545


Accuracy capturado en la ejecución 13: 61.75 [%]
F1-score capturado en la ejecución 13: 61.92 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
2025-11-03 16:54:33.382788: 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`.
2025-11-03 16:54:33.394332: 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 00:00:1762185273.407815  305525 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185273.412250  305525 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185273.422264  305525 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185273.422287  305525 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185273.422290  305525 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185273.422292  305525 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:54:33.425640: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.1s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.09 [%]
Global accuracy score (test) = 60.97 [%]
Global F1 score (train) = 68.0 [%]
Global F1 score (test) = 60.83 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.46      0.35      0.40       400
MODERATE-INTENSITY       0.52      0.68      0.59       400
         SEDENTARY       0.69      0.76      0.72       400
VIGOROUS-INTENSITY       0.80      0.66      0.72       345

          accuracy                           0.61      1545
         macro avg       0.62      0.61      0.61      1545
      weighted avg       0.61      0.61      0.60      1545


Accuracy capturado en la ejecución 14: 60.97 [%]
F1-score capturado en la ejecución 14: 60.83 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
2025-11-03 16:54:54.978901: 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`.
2025-11-03 16:54:54.990202: 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 00:00:1762185295.003993  305912 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185295.008417  305912 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185295.018882  305912 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185295.018907  305912 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185295.018910  305912 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185295.018912  305912 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:54:55.022217: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.2s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't 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)
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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)
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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, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 67.85 [%]
Global accuracy score (test) = 61.04 [%]
Global F1 score (train) = 67.81 [%]
Global F1 score (test) = 60.7 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.45      0.31      0.37       400
MODERATE-INTENSITY       0.53      0.77      0.62       400
         SEDENTARY       0.69      0.72      0.70       400
VIGOROUS-INTENSITY       0.84      0.65      0.73       345

          accuracy                           0.61      1545
         macro avg       0.63      0.61      0.61      1545
      weighted avg       0.62      0.61      0.60      1545


Accuracy capturado en la ejecución 15: 61.04 [%]
F1-score capturado en la ejecución 15: 60.7 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
2025-11-03 16:55:16.662223: 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`.
2025-11-03 16:55:16.673962: 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 00:00:1762185316.687754  306296 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185316.692186  306296 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185316.702351  306296 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185316.702380  306296 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185316.702382  306296 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185316.702384  306296 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:55:16.705772: 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 410 tasks      | elapsed:    2.2s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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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Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 67.99 [%]
Global accuracy score (test) = 63.56 [%]
Global F1 score (train) = 67.98 [%]
Global F1 score (test) = 63.29 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.50      0.39      0.44       400
MODERATE-INTENSITY       0.58      0.77      0.66       400
         SEDENTARY       0.69      0.74      0.71       400
VIGOROUS-INTENSITY       0.81      0.65      0.72       345

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


Accuracy capturado en la ejecución 16: 63.56 [%]
F1-score capturado en la ejecución 16: 63.29 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
2025-11-03 16:55:38.750483: 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`.
2025-11-03 16:55:38.762127: 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 00:00:1762185338.775517  306670 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185338.779908  306670 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185338.789812  306670 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185338.789836  306670 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185338.789838  306670 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185338.789840  306670 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:55:38.793199: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.1s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.17 [%]
Global accuracy score (test) = 61.1 [%]
Global F1 score (train) = 68.31 [%]
Global F1 score (test) = 61.22 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.46      0.38      0.42       400
MODERATE-INTENSITY       0.55      0.72      0.62       400
         SEDENTARY       0.67      0.70      0.68       400
VIGOROUS-INTENSITY       0.82      0.66      0.73       345

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


Accuracy capturado en la ejecución 17: 61.1 [%]
F1-score capturado en la ejecución 17: 61.22 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
2025-11-03 16:56:00.644097: 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`.
2025-11-03 16:56:00.655651: 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 00:00:1762185360.669550  307073 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185360.673793  307073 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185360.684529  307073 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185360.684558  307073 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185360.684570  307073 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185360.684572  307073 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:56:00.687762: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.2s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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)
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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)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 1. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.3 [%]
Global accuracy score (test) = 61.94 [%]
Global F1 score (train) = 68.4 [%]
Global F1 score (test) = 61.86 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.47      0.38      0.42       400
MODERATE-INTENSITY       0.56      0.70      0.62       400
         SEDENTARY       0.69      0.74      0.71       400
VIGOROUS-INTENSITY       0.79      0.66      0.72       345

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


Accuracy capturado en la ejecución 18: 61.94 [%]
F1-score capturado en la ejecución 18: 61.86 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
2025-11-03 16:56:22.576845: 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`.
2025-11-03 16:56:22.588323: 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 00:00:1762185382.602063  307462 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185382.606342  307462 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185382.616937  307462 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185382.616968  307462 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185382.616970  307462 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185382.616972  307462 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:56:22.620260: 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 410 tasks      | elapsed:    2.2s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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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)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 1. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 67.97 [%]
Global accuracy score (test) = 61.36 [%]
Global F1 score (train) = 67.78 [%]
Global F1 score (test) = 60.85 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.46      0.32      0.38       400
MODERATE-INTENSITY       0.55      0.77      0.64       400
         SEDENTARY       0.67      0.72      0.69       400
VIGOROUS-INTENSITY       0.80      0.66      0.72       345

          accuracy                           0.61      1545
         macro avg       0.62      0.62      0.61      1545
      weighted avg       0.61      0.61      0.60      1545


Accuracy capturado en la ejecución 19: 61.36 [%]
F1-score capturado en la ejecución 19: 60.85 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
2025-11-03 16:56:44.777842: 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`.
2025-11-03 16:56:44.789296: 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 00:00:1762185404.802982  307855 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185404.807267  307855 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185404.817492  307855 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185404.817516  307855 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185404.817519  307855 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185404.817520  307855 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:56:44.820848: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.1s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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)
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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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Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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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, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 1. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 67.74 [%]
Global accuracy score (test) = 63.43 [%]
Global F1 score (train) = 67.72 [%]
Global F1 score (test) = 63.35 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.51      0.40      0.45       400
MODERATE-INTENSITY       0.55      0.74      0.63       400
         SEDENTARY       0.71      0.75      0.73       400
VIGOROUS-INTENSITY       0.82      0.65      0.72       345

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


Accuracy capturado en la ejecución 20: 63.43 [%]
F1-score capturado en la ejecución 20: 63.35 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
2025-11-03 16:57:06.700377: 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`.
2025-11-03 16:57:06.712205: 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 00:00:1762185426.726051  308258 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185426.730438  308258 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185426.740591  308258 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185426.740614  308258 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185426.740617  308258 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185426.740618  308258 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:57:06.743955: 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 410 tasks      | elapsed:    2.2s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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)
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)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 1. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.3 [%]
Global accuracy score (test) = 62.2 [%]
Global F1 score (train) = 68.24 [%]
Global F1 score (test) = 61.79 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.48      0.35      0.41       400
MODERATE-INTENSITY       0.56      0.76      0.65       400
         SEDENTARY       0.69      0.72      0.70       400
VIGOROUS-INTENSITY       0.78      0.66      0.71       345

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


Accuracy capturado en la ejecución 21: 62.2 [%]
F1-score capturado en la ejecución 21: 61.79 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
2025-11-03 16:57:29.044848: 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`.
2025-11-03 16:57:29.056154: 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 00:00:1762185449.069833  308652 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185449.074088  308652 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185449.084209  308652 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185449.084234  308652 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185449.084237  308652 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185449.084239  308652 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:57:29.087544: 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 410 tasks      | elapsed:    2.2s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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)
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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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 1. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 67.73 [%]
Global accuracy score (test) = 62.72 [%]
Global F1 score (train) = 67.7 [%]
Global F1 score (test) = 62.42 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.48      0.36      0.41       400
MODERATE-INTENSITY       0.55      0.76      0.64       400
         SEDENTARY       0.71      0.74      0.72       400
VIGOROUS-INTENSITY       0.80      0.65      0.72       345

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


Accuracy capturado en la ejecución 22: 62.72 [%]
F1-score capturado en la ejecución 22: 62.42 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
2025-11-03 16:57:51.178292: 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`.
2025-11-03 16:57:51.189664: 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 00:00:1762185471.203327  309055 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185471.207655  309055 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185471.217792  309055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185471.217818  309055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185471.217820  309055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185471.217822  309055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:57:51.221066: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.2s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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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)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.36 [%]
Global accuracy score (test) = 62.78 [%]
Global F1 score (train) = 68.45 [%]
Global F1 score (test) = 62.85 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.49      0.40      0.44       400
MODERATE-INTENSITY       0.55      0.75      0.63       400
         SEDENTARY       0.71      0.72      0.71       400
VIGOROUS-INTENSITY       0.82      0.65      0.73       345

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


Accuracy capturado en la ejecución 23: 62.78 [%]
F1-score capturado en la ejecución 23: 62.85 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
2025-11-03 16:58:13.109643: 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`.
2025-11-03 16:58:13.121007: 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 00:00:1762185493.134576  309441 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185493.138792  309441 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185493.149211  309441 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185493.149238  309441 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185493.149241  309441 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185493.149243  309441 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:58:13.152518: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.2s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
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Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 0. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.18 [%]
Global accuracy score (test) = 61.62 [%]
Global F1 score (train) = 68.41 [%]
Global F1 score (test) = 61.72 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.48      0.40      0.44       400
MODERATE-INTENSITY       0.55      0.71      0.62       400
         SEDENTARY       0.69      0.70      0.70       400
VIGOROUS-INTENSITY       0.78      0.66      0.71       345

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


Accuracy capturado en la ejecución 24: 61.62 [%]
F1-score capturado en la ejecución 24: 61.72 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
2025-11-03 16:58:35.047325: 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`.
2025-11-03 16:58:35.058811: 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 00:00:1762185515.072098  309815 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185515.076570  309815 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185515.086621  309815 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185515.086645  309815 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185515.086647  309815 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185515.086649  309815 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:58:35.089902: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.2s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.25 [%]
Global accuracy score (test) = 62.52 [%]
Global F1 score (train) = 68.28 [%]
Global F1 score (test) = 62.26 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.48      0.35      0.41       400
MODERATE-INTENSITY       0.56      0.75      0.64       400
         SEDENTARY       0.68      0.74      0.71       400
VIGOROUS-INTENSITY       0.83      0.66      0.73       345

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


Accuracy capturado en la ejecución 25: 62.52 [%]
F1-score capturado en la ejecución 25: 62.26 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
2025-11-03 16:58:56.989992: 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`.
2025-11-03 16:58:57.001725: 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 00:00:1762185537.015680  310222 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185537.019970  310222 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185537.030470  310222 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185537.030503  310222 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185537.030506  310222 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185537.030508  310222 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:58:57.033787: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.2s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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)
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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)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[1. 2. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 67.88 [%]
Global accuracy score (test) = 62.46 [%]
Global F1 score (train) = 67.73 [%]
Global F1 score (test) = 62.1 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.52      0.36      0.43       400
MODERATE-INTENSITY       0.54      0.75      0.63       400
         SEDENTARY       0.71      0.73      0.72       400
VIGOROUS-INTENSITY       0.77      0.66      0.71       345

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


Accuracy capturado en la ejecución 26: 62.46 [%]
F1-score capturado en la ejecución 26: 62.1 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
2025-11-03 16:59:19.045916: 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`.
2025-11-03 16:59:19.057274: 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 00:00:1762185559.070999  310624 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185559.075380  310624 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185559.085337  310624 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185559.085361  310624 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185559.085364  310624 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185559.085366  310624 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:59:19.088644: 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 410 tasks      | elapsed:    2.2s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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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, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 67.8 [%]
Global accuracy score (test) = 61.23 [%]
Global F1 score (train) = 67.87 [%]
Global F1 score (test) = 61.47 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.45      0.38      0.41       400
MODERATE-INTENSITY       0.55      0.71      0.62       400
         SEDENTARY       0.68      0.70      0.69       400
VIGOROUS-INTENSITY       0.83      0.66      0.73       345

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


Accuracy capturado en la ejecución 27: 61.23 [%]
F1-score capturado en la ejecución 27: 61.47 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
2025-11-03 16:59:41.364015: 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`.
2025-11-03 16:59:41.375683: 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 00:00:1762185581.389209  311007 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185581.393391  311007 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185581.403663  311007 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185581.403686  311007 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185581.403688  311007 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185581.403690  311007 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 16:59:41.407012: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.2s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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)
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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}
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Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[0. 0. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.31 [%]
Global accuracy score (test) = 62.78 [%]
Global F1 score (train) = 68.31 [%]
Global F1 score (test) = 62.58 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.51      0.38      0.43       400
MODERATE-INTENSITY       0.56      0.76      0.64       400
         SEDENTARY       0.68      0.72      0.70       400
VIGOROUS-INTENSITY       0.81      0.66      0.72       345

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


Accuracy capturado en la ejecución 28: 62.78 [%]
F1-score capturado en la ejecución 28: 62.58 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
2025-11-03 17:00:03.167060: 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`.
2025-11-03 17:00:03.178752: 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 00:00:1762185603.192792  311409 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762185603.197113  311409 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762185603.207860  311409 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185603.207890  311409 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185603.207892  311409 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762185603.207894  311409 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 17:00:03.211144: 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:    0.9s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.2s
[Parallel(n_jobs=-1)]: Done 465 out of 465 | 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 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 465 out of 465 | 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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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 1. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 67.68 [%]
Global accuracy score (test) = 62.59 [%]
Global F1 score (train) = 67.62 [%]
Global F1 score (test) = 62.09 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.49      0.33      0.40       400
MODERATE-INTENSITY       0.55      0.77      0.64       400
         SEDENTARY       0.68      0.75      0.71       400
VIGOROUS-INTENSITY       0.83      0.66      0.73       345

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


Accuracy capturado en la ejecución 29: 62.59 [%]
F1-score capturado en la ejecución 29: 62.09 [%]

=== 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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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13317, 91)
[[2.]
 [2.]
 [2.]
 ...
 [3.]
 [3.]
 [3.]]
(1545, 1)
[2. 2. 2. ... 3. 3. 3.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 68.41 [%]
Global accuracy score (test) = 62.59 [%]
Global F1 score (train) = 68.42 [%]
Global F1 score (test) = 62.52 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.48      0.38      0.43       400
MODERATE-INTENSITY       0.55      0.74      0.63       400
         SEDENTARY       0.71      0.73      0.72       400
VIGOROUS-INTENSITY       0.80      0.66      0.72       345

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


Accuracy capturado en la ejecución 30: 62.59 [%]
F1-score capturado en la ejecución 30: 62.52 [%]

=== RESUMEN FINAL ===
Accuracies: [61.75, 62.39, 61.75, 61.49, 61.75, 62.01, 60.91, 61.17, 62.33, 60.45, 63.5, 62.2, 61.75, 60.97, 61.04, 63.56, 61.1, 61.94, 61.36, 63.43, 62.2, 62.72, 62.78, 61.62, 62.52, 62.46, 61.23, 62.78, 62.59, 62.59]
F1-scores: [61.62, 62.37, 61.85, 61.66, 61.47, 62.21, 60.81, 60.6, 62.17, 60.63, 63.75, 62.36, 61.92, 60.83, 60.7, 63.29, 61.22, 61.86, 60.85, 63.35, 61.79, 62.42, 62.85, 61.72, 62.26, 62.1, 61.47, 62.58, 62.09, 62.52]
Accuracy mean: 62.0113 | std: 0.7898
F1 mean: 61.9107 | std: 0.8057

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