2025-11-03 14:06:15.629723: 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 14:06:15.641517: 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 00:00:1762175175.655438  175324 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175175.659785  175324 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175175.670261  175324 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175175.670286  175324 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175175.670289  175324 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175175.670292  175324 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:06:15.673557: 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 14:06:23,196	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-03 14:06:23,867	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-03 14:06:23,935	INFO trial.py:182 -- Creating a new dirname dir_e62c1_bd3c because trial dirname 'dir_e62c1' already exists.
2025-11-03 14:06:23,939	INFO trial.py:182 -- Creating a new dirname dir_e62c1_e342 because trial dirname 'dir_e62c1' already exists.
2025-11-03 14:06:23,941	INFO trial.py:182 -- Creating a new dirname dir_e62c1_df6c because trial dirname 'dir_e62c1' already exists.
2025-11-03 14:06:23,943	INFO trial.py:182 -- Creating a new dirname dir_e62c1_2368 because trial dirname 'dir_e62c1' already exists.
2025-11-03 14:06:23,945	INFO trial.py:182 -- Creating a new dirname dir_e62c1_d206 because trial dirname 'dir_e62c1' already exists.
2025-11-03 14:06:23,948	INFO trial.py:182 -- Creating a new dirname dir_e62c1_1c48 because trial dirname 'dir_e62c1' already exists.
2025-11-03 14:06:23,950	INFO trial.py:182 -- Creating a new dirname dir_e62c1_7ffb because trial dirname 'dir_e62c1' already exists.
2025-11-03 14:06:23,953	INFO trial.py:182 -- Creating a new dirname dir_e62c1_cb4c because trial dirname 'dir_e62c1' already exists.
2025-11-03 14:06:23,955	INFO trial.py:182 -- Creating a new dirname dir_e62c1_3093 because trial dirname 'dir_e62c1' already exists.
2025-11-03 14:06:23,958	INFO trial.py:182 -- Creating a new dirname dir_e62c1_1f05 because trial dirname 'dir_e62c1' already exists.
2025-11-03 14:06:23,961	INFO trial.py:182 -- Creating a new dirname dir_e62c1_cdad because trial dirname 'dir_e62c1' already exists.
2025-11-03 14:06:23,963	INFO trial.py:182 -- Creating a new dirname dir_e62c1_6393 because trial dirname 'dir_e62c1' already exists.
2025-11-03 14:06:23,967	INFO trial.py:182 -- Creating a new dirname dir_e62c1_7175 because trial dirname 'dir_e62c1' already exists.
2025-11-03 14:06:23,970	INFO trial.py:182 -- Creating a new dirname dir_e62c1_9e40 because trial dirname 'dir_e62c1' already exists.
2025-11-03 14:06:23,973	INFO trial.py:182 -- Creating a new dirname dir_e62c1_d0e3 because trial dirname 'dir_e62c1' already exists.
2025-11-03 14:06:23,977	INFO trial.py:182 -- Creating a new dirname dir_e62c1_611a because trial dirname 'dir_e62c1' already exists.
2025-11-03 14:06:23,980	INFO trial.py:182 -- Creating a new dirname dir_e62c1_8129 because trial dirname 'dir_e62c1' already exists.
2025-11-03 14:06:23,989	INFO trial.py:182 -- Creating a new dirname dir_e62c1_7adc because trial dirname 'dir_e62c1' already exists.
2025-11-03 14:06:23,998	INFO trial.py:182 -- Creating a new dirname dir_e62c1_b8a3 because trial dirname 'dir_e62c1' 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_PI/case_PI_BRF_acc_gyr_superclasses_CPA_METs/BalancedRF_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-03_14-06-22_484530_175324/artifacts/2025-11-03_14-06-23/BalancedRF_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-03 14:06:24. 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_e62c1    PENDING               301             6                    37                   12   sqrt                       6734 │
│ trial_e62c1    PENDING               201             7                    32                   21   0.3                        7163 │
│ trial_e62c1    PENDING               443             7                    22                   24   0.3                        9045 │
│ trial_e62c1    PENDING               469             6                    22                   23   sqrt                        609 │
│ trial_e62c1    PENDING               431             7                    38                   12   0.3                        9089 │
│ trial_e62c1    PENDING               309             6                    44                   13   sqrt                       1289 │
│ trial_e62c1    PENDING               318             6                    28                   17   sqrt                        204 │
│ trial_e62c1    PENDING               434             5                    55                   20   0.3                        6690 │
│ trial_e62c1    PENDING               350             5                    57                   12   0.3                        5758 │
│ trial_e62c1    PENDING               450             7                    44                   19   0.3                        9189 │
│ trial_e62c1    PENDING               260             6                    42                   18   0.3                        1015 │
│ trial_e62c1    PENDING               278             6                    26                   16   sqrt                       5018 │
│ trial_e62c1    PENDING               464             6                    49                   13   0.3                        1653 │
│ trial_e62c1    PENDING               317             6                    26                   10   sqrt                       7592 │
│ trial_e62c1    PENDING               241             6                    22                   20   0.3                        3855 │
│ trial_e62c1    PENDING               443             5                    28                   27   0.3                        3240 │
│ trial_e62c1    PENDING               282             7                    35                   19   0.3                        7102 │
│ trial_e62c1    PENDING               269             5                    41                   23   sqrt                       3682 │
│ trial_e62c1    PENDING               241             6                    45                   12   0.3                        7505 │
│ trial_e62c1    PENDING               311             5                    40                   10   sqrt                       3284 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               12 │
│ min_samples_split              45 │
│ n_estimators                  241 │
│ random_state                 7505 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               21 │
│ min_samples_split              32 │
│ n_estimators                  201 │
│ random_state                 7163 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               10 │
│ min_samples_split              40 │
│ n_estimators                  311 │
│ random_state                 3284 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               13 │
│ min_samples_split              44 │
│ n_estimators                  309 │
│ random_state                 1289 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               10 │
│ min_samples_split              26 │
│ n_estimators                  317 │
│ random_state                 7592 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               27 │
│ min_samples_split              28 │
│ n_estimators                  443 │
│ random_state                 3240 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               12 │
│ min_samples_split              38 │
│ n_estimators                  431 │
│ random_state                 9089 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               23 │
│ min_samples_split              22 │
│ n_estimators                  469 │
│ random_state                  609 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               12 │
│ min_samples_split              57 │
│ n_estimators                  350 │
│ random_state                 5758 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               13 │
│ min_samples_split              49 │
│ n_estimators                  464 │
│ random_state                 1653 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               20 │
│ min_samples_split              55 │
│ n_estimators                  434 │
│ random_state                 6690 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               23 │
│ min_samples_split              41 │
│ n_estimators                  269 │
│ random_state                 3682 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               17 │
│ min_samples_split              28 │
│ n_estimators                  318 │
│ random_state                  204 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               20 │
│ min_samples_split              22 │
│ n_estimators                  241 │
│ random_state                 3855 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               24 │
│ min_samples_split              22 │
│ n_estimators                  443 │
│ random_state                 9045 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               19 │
│ min_samples_split              44 │
│ n_estimators                  450 │
│ random_state                 9189 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               19 │
│ min_samples_split              35 │
│ n_estimators                  282 │
│ random_state                 7102 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
[36m(train_brf_ray_tune pid=176975)[0m 2025-11-03 14:06:27.225400: 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=177000)[0m 2025-11-03 14:06:27.181603: 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=177000)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_brf_ray_tune pid=177000)[0m E0000 00:00:1762175187.210391  178115 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=177000)[0m E0000 00:00:1762175187.218808  178115 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=176975)[0m W0000 00:00:1762175187.308696  178122 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=176975)[0m W0000 00:00:1762175187.308749  178122 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=176975)[0m W0000 00:00:1762175187.308753  178122 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=176975)[0m W0000 00:00:1762175187.308756  178122 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=176975)[0m 2025-11-03 14:06:27.315387: 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=176975)[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=177000)[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=177000)[0m   return fit_method(estimator, *args, **kwargs)
[36m(train_brf_ray_tune pid=177000)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=177000)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.6s
[36m(train_brf_ray_tune pid=176993)[0m 2025-11-03 14:06:27.599467: 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=176993)[0m 2025-11-03 14:06:27.620260: 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=176993)[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=176993)[0m E0000 00:00:1762175187.647802  178227 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=176993)[0m E0000 00:00:1762175187.656212  178227 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=176999)[0m W0000 00:00:1762175187.698209  178236 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=176999)[0m 2025-11-03 14:06:27.705417: 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=176999)[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=176983)[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=176983)[0m   return fit_method(estimator, *args, **kwargs)[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=176992)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 18x across cluster][0m
[36m(train_brf_ray_tune pid=176969)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    2.4s[32m [repeated 12x across cluster][0m
[36m(train_brf_ray_tune pid=176983)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=176990)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   11.5s[32m [repeated 10x across cluster][0m
[36m(train_brf_ray_tune pid=176992)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   15.3s[32m [repeated 5x across cluster][0m
[36m(train_brf_ray_tune pid=176998)[0m [Parallel(n_jobs=-1)]: Done 309 out of 309 | elapsed:   19.5s finished
[36m(train_brf_ray_tune pid=176998)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=176998)[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=176998)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=177000)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   24.4s[32m [repeated 9x across cluster][0m
[36m(train_brf_ray_tune pid=176990)[0m [Parallel(n_jobs=-1)]: Done 318 out of 318 | elapsed:   23.5s finished[32m [repeated 8x across cluster][0m
[36m(train_brf_ray_tune pid=177001)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 7x across cluster][0m
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               18 │
│ min_samples_split              42 │
│ n_estimators                  260 │
│ random_state                 1015 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               12 │
│ min_samples_split              37 │
│ n_estimators                  301 │
│ random_state                 6734 │
╰───────────────────────────────────╯
Trial trial_e62c1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_e62c1 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               16 │
│ min_samples_split              26 │
│ n_estimators                  278 │
│ random_state                 5018 │
╰───────────────────────────────────╯

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:06:53. Total running time: 29s
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             26.3694 │
│ time_total_s                 26.3694 │
│ training_iteration                 1 │
│ test_accuracy                0.81097 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:06:53. Total running time: 29s

Trial status: 19 RUNNING | 1 TERMINATED
Current time: 2025-11-03 14:06:54. 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_e62c1    RUNNING                 301             6                    37                   12   sqrt                       6734                                               │
│ trial_e62c1    RUNNING                 201             7                    32                   21   0.3                        7163                                               │
│ trial_e62c1    RUNNING                 443             7                    22                   24   0.3                        9045                                               │
│ trial_e62c1    RUNNING                 469             6                    22                   23   sqrt                        609                                               │
│ trial_e62c1    RUNNING                 431             7                    38                   12   0.3                        9089                                               │
│ trial_e62c1    RUNNING                 318             6                    28                   17   sqrt                        204                                               │
│ trial_e62c1    RUNNING                 434             5                    55                   20   0.3                        6690                                               │
│ trial_e62c1    RUNNING                 350             5                    57                   12   0.3                        5758                                               │
│ trial_e62c1    RUNNING                 450             7                    44                   19   0.3                        9189                                               │
│ trial_e62c1    RUNNING                 260             6                    42                   18   0.3                        1015                                               │
│ trial_e62c1    RUNNING                 278             6                    26                   16   sqrt                       5018                                               │
│ trial_e62c1    RUNNING                 464             6                    49                   13   0.3                        1653                                               │
│ trial_e62c1    RUNNING                 317             6                    26                   10   sqrt                       7592                                               │
│ trial_e62c1    RUNNING                 241             6                    22                   20   0.3                        3855                                               │
│ trial_e62c1    RUNNING                 443             5                    28                   27   0.3                        3240                                               │
│ trial_e62c1    RUNNING                 282             7                    35                   19   0.3                        7102                                               │
│ trial_e62c1    RUNNING                 269             5                    41                   23   sqrt                       3682                                               │
│ trial_e62c1    RUNNING                 241             6                    45                   12   0.3                        7505                                               │
│ trial_e62c1    RUNNING                 311             5                    40                   10   sqrt                       3284                                               │
│ trial_e62c1    TERMINATED              309             6                    44                   13   sqrt                       1289        1            26.3694          0.810971 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:06:54. Total running time: 30s
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             27.9357 │
│ time_total_s                 27.9357 │
│ training_iteration                 1 │
│ test_accuracy                0.78651 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:06:54. Total running time: 30s

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:06:56. Total running time: 32s
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             29.8026 │
│ time_total_s                 29.8026 │
│ training_iteration                 1 │
│ test_accuracy                0.78354 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:06:56. Total running time: 32s

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:06:57. Total running time: 33s
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             30.1529 │
│ time_total_s                 30.1529 │
│ training_iteration                 1 │
│ test_accuracy                0.81097 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:06:57. Total running time: 33s

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:06:57. Total running time: 33s
[36m(train_brf_ray_tune pid=176990)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=176990)[0m   _log_deprecation_warning([32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=176997)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   28.4s[32m [repeated 23x across cluster][0m
[36m(train_brf_ray_tune pid=176992)[0m [Parallel(n_jobs=20)]: Done 278 out of 278 | elapsed:    0.5s finished[32m [repeated 12x across cluster][0m
[36m(train_brf_ray_tune pid=176992)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 6x across cluster][0m
[36m(train_brf_ray_tune pid=176992)[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=176992)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=176984)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   34.2s[32m [repeated 5x across cluster][0m
[36m(train_brf_ray_tune pid=176969)[0m [Parallel(n_jobs=-1)]: Done 469 out of 469 | elapsed:   32.6s finished
[36m(train_brf_ray_tune pid=176969)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=176969)[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=176969)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=176975)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.2s[32m [repeated 18x across cluster][0m
[36m(train_brf_ray_tune pid=176996)[0m [Parallel(n_jobs=-1)]: Done 350 out of 350 | elapsed:   43.5s finished[32m [repeated 9x across cluster][0m
[36m(train_brf_ray_tune pid=176975)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=176975)[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=176975)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=176983)[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=176983)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=176993)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.2s[32m [repeated 14x across cluster][0m
[36m(train_brf_ray_tune pid=176993)[0m [Parallel(n_jobs=20)]: Done 282 out of 282 | elapsed:    0.2s finished[32m [repeated 10x across cluster][0m
[36m(train_brf_ray_tune pid=176993)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 8x across cluster][0m
[36m(train_brf_ray_tune pid=176993)[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=176993)[0m   _log_deprecation_warning([32m [repeated 3x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             30.7931 │
│ time_total_s                 30.7931 │
│ training_iteration                 1 │
│ test_accuracy                0.81394 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:06:57. Total running time: 34s

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:06:58. Total running time: 34s
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             31.6828 │
│ time_total_s                 31.6828 │
│ training_iteration                 1 │
│ test_accuracy                0.81468 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:06:58. Total running time: 34s

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:06:59. Total running time: 35s
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             32.2784 │
│ time_total_s                 32.2784 │
│ training_iteration                 1 │
│ test_accuracy                0.81394 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:06:59. Total running time: 35s

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:07:08. Total running time: 44s
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             42.0186 │
│ time_total_s                 42.0186 │
│ training_iteration                 1 │
│ test_accuracy                0.80578 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:07:08. Total running time: 44s

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:07:08. Total running time: 44s
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             41.8729 │
│ time_total_s                 41.8729 │
│ training_iteration                 1 │
│ test_accuracy                 0.8169 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:07:08. Total running time: 44s

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:07:10. Total running time: 46s
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             43.4447 │
│ time_total_s                 43.4447 │
│ training_iteration                 1 │
│ test_accuracy                0.82728 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:07:10. Total running time: 46s

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:07:16. Total running time: 52s
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             49.2812 │
│ time_total_s                 49.2812 │
│ training_iteration                 1 │
│ test_accuracy                0.81023 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:07:16. Total running time: 52s

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:07:16. Total running time: 52s
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             49.4618 │
│ time_total_s                 49.4618 │
│ training_iteration                 1 │
│ test_accuracy                0.78132 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:07:16. Total running time: 52s

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:07:16. Total running time: 52s
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             49.5962 │
│ time_total_s                 49.5962 │
│ training_iteration                 1 │
│ test_accuracy                0.80504 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:07:16. Total running time: 52s

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:07:20. Total running time: 56s
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             53.1798 │
│ time_total_s                 53.1798 │
│ training_iteration                 1 │
│ test_accuracy                0.83543 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:07:20. Total running time: 56s

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:07:21. Total running time: 58s
[36m(train_brf_ray_tune pid=176991)[0m [Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:   51.4s[32m [repeated 11x across cluster][0m
[36m(train_brf_ray_tune pid=176991)[0m [Parallel(n_jobs=-1)]: Done 464 out of 464 | elapsed:   53.5s finished[32m [repeated 8x across cluster][0m
[36m(train_brf_ray_tune pid=176997)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=176997)[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=176997)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             55.0421 │
│ time_total_s                 55.0421 │
│ training_iteration                 1 │
│ test_accuracy                0.78206 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:07:21. Total running time: 58s

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:07:22. Total running time: 58s
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             55.4475 │
│ time_total_s                 55.4475 │
│ training_iteration                 1 │
│ test_accuracy                0.77761 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:07:22. Total running time: 58s

Trial status: 16 TERMINATED | 4 RUNNING
Current time: 2025-11-03 14:07:24. 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_e62c1    RUNNING                 443             7                    22                   24   0.3                        9045                                               │
│ trial_e62c1    RUNNING                 431             7                    38                   12   0.3                        9089                                               │
│ trial_e62c1    RUNNING                 450             7                    44                   19   0.3                        9189                                               │
│ trial_e62c1    RUNNING                 464             6                    49                   13   0.3                        1653                                               │
│ trial_e62c1    TERMINATED              301             6                    37                   12   sqrt                       6734        1            30.1529          0.810971 │
│ trial_e62c1    TERMINATED              201             7                    32                   21   0.3                        7163        1            43.4447          0.827279 │
│ trial_e62c1    TERMINATED              469             6                    22                   23   sqrt                        609        1            41.8729          0.816901 │
│ trial_e62c1    TERMINATED              309             6                    44                   13   sqrt                       1289        1            26.3694          0.810971 │
│ trial_e62c1    TERMINATED              318             6                    28                   17   sqrt                        204        1            30.7931          0.813936 │
│ trial_e62c1    TERMINATED              434             5                    55                   20   0.3                        6690        1            55.4475          0.777613 │
│ trial_e62c1    TERMINATED              350             5                    57                   12   0.3                        5758        1            49.4618          0.781319 │
│ trial_e62c1    TERMINATED              260             6                    42                   18   0.3                        1015        1            49.5962          0.805041 │
│ trial_e62c1    TERMINATED              278             6                    26                   16   sqrt                       5018        1            32.2785          0.813936 │
│ trial_e62c1    TERMINATED              317             6                    26                   10   sqrt                       7592        1            31.6828          0.814678 │
│ trial_e62c1    TERMINATED              241             6                    22                   20   0.3                        3855        1            49.2812          0.81023  │
│ trial_e62c1    TERMINATED              443             5                    28                   27   0.3                        3240        1            55.0421          0.782061 │
│ trial_e62c1    TERMINATED              282             7                    35                   19   0.3                        7102        1            53.1798          0.835434 │
│ trial_e62c1    TERMINATED              269             5                    41                   23   sqrt                       3682        1            29.8025          0.783543 │
│ trial_e62c1    TERMINATED              241             6                    45                   12   0.3                        7505        1            42.0186          0.805782 │
│ trial_e62c1    TERMINATED              311             5                    40                   10   sqrt                       3284        1            27.9357          0.786509 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:07:26. Total running time: 1min 2s
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             59.7653 │
│ time_total_s                 59.7653 │
│ training_iteration                 1 │
│ test_accuracy                0.80875 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:07:26. Total running time: 1min 2s

2025-11-03 14:07:27,482	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_PI/case_PI_BRF_acc_gyr_superclasses_CPA_METs/BalancedRF_hyperparameters_tuning' in 0.0065s.
/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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | elapsed:    0.1s finished
Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:07:27. Total running time: 1min 3s
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             60.2575 │
│ time_total_s                 60.2575 │
│ training_iteration                 1 │
│ test_accuracy                0.82876 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:07:27. Total running time: 1min 3s

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:07:27. Total running time: 1min 3s
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             60.2449 │
│ time_total_s                 60.2449 │
│ training_iteration                 1 │
│ test_accuracy                0.83321 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:07:27. Total running time: 1min 3s

Trial trial_e62c1 finished iteration 1 at 2025-11-03 14:07:27. Total running time: 1min 3s
╭──────────────────────────────────────╮
│ Trial trial_e62c1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             60.3489 │
│ time_total_s                 60.3489 │
│ training_iteration                 1 │
│ test_accuracy                0.82802 │
╰──────────────────────────────────────╯

Trial trial_e62c1 completed after 1 iterations at 2025-11-03 14:07:27. Total running time: 1min 3s

Trial status: 20 TERMINATED
Current time: 2025-11-03 14:07:27. Total running time: 1min 3s
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_e62c1    TERMINATED              301             6                    37                   12   sqrt                       6734        1            30.1529          0.810971 │
│ trial_e62c1    TERMINATED              201             7                    32                   21   0.3                        7163        1            43.4447          0.827279 │
│ trial_e62c1    TERMINATED              443             7                    22                   24   0.3                        9045        1            60.2449          0.83321  │
│ trial_e62c1    TERMINATED              469             6                    22                   23   sqrt                        609        1            41.8729          0.816901 │
│ trial_e62c1    TERMINATED              431             7                    38                   12   0.3                        9089        1            60.2575          0.828762 │
│ trial_e62c1    TERMINATED              309             6                    44                   13   sqrt                       1289        1            26.3694          0.810971 │
│ trial_e62c1    TERMINATED              318             6                    28                   17   sqrt                        204        1            30.7931          0.813936 │
│ trial_e62c1    TERMINATED              434             5                    55                   20   0.3                        6690        1            55.4475          0.777613 │
│ trial_e62c1    TERMINATED              350             5                    57                   12   0.3                        5758        1            49.4618          0.781319 │
│ trial_e62c1    TERMINATED              450             7                    44                   19   0.3                        9189        1            60.3489          0.828021 │
│ trial_e62c1    TERMINATED              260             6                    42                   18   0.3                        1015        1            49.5962          0.805041 │
│ trial_e62c1    TERMINATED              278             6                    26                   16   sqrt                       5018        1            32.2785          0.813936 │
│ trial_e62c1    TERMINATED              464             6                    49                   13   0.3                        1653        1            59.7653          0.808747 │
│ trial_e62c1    TERMINATED              317             6                    26                   10   sqrt                       7592        1            31.6828          0.814678 │
│ trial_e62c1    TERMINATED              241             6                    22                   20   0.3                        3855        1            49.2812          0.81023  │
│ trial_e62c1    TERMINATED              443             5                    28                   27   0.3                        3240        1            55.0421          0.782061 │
│ trial_e62c1    TERMINATED              282             7                    35                   19   0.3                        7102        1            53.1798          0.835434 │
│ trial_e62c1    TERMINATED              269             5                    41                   23   sqrt                       3682        1            29.8025          0.783543 │
│ trial_e62c1    TERMINATED              241             6                    45                   12   0.3                        7505        1            42.0186          0.805782 │
│ trial_e62c1    TERMINATED              311             5                    40                   10   sqrt                       3284        1            27.9357          0.786509 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'n_estimators': 282, 'max_depth': 7, 'min_samples_split': 35, 'min_samples_leaf': 19, 'max_features': 0.3, 'random_state': 7102}
Saved model to disk
[36m(train_brf_ray_tune pid=176994)[0m [Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s[32m [repeated 21x across cluster][0m
[36m(train_brf_ray_tune pid=176994)[0m [Parallel(n_jobs=20)]: Done 450 out of 450 | elapsed:    0.1s finished[32m [repeated 11x across cluster][0m
[36m(train_brf_ray_tune pid=176994)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 8x across cluster][0m
[36m(train_brf_ray_tune pid=176994)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=176994)[0m   _log_deprecation_warning([32m [repeated 4x across cluster][0m
2025-11-03 14:07:43.179331: 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 14:07:43.190699: 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 00:00:1762175263.204186  180646 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175263.208475  180646 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175263.218765  180646 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175263.218791  180646 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175263.218793  180646 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175263.218795  180646 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:07:43.221843: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.1s 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 282 out of 282 | elapsed:    0.0s 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
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 1. 1. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.57 [%]
Global accuracy score (test) = 80.58 [%]
Global F1 score (train) = 82.89 [%]
Global F1 score (test) = 81.13 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.68      0.55      0.61       350
MODERATE-INTENSITY       0.64      0.82      0.72       350
         SEDENTARY       0.98      0.95      0.97       350
VIGOROUS-INTENSITY       0.99      0.92      0.95       299

          accuracy                           0.81      1349
         macro avg       0.82      0.81      0.81      1349
      weighted avg       0.82      0.81      0.81      1349


Accuracy capturado en la ejecución 1: 80.58 [%]
F1-score capturado en la ejecución 1: 81.13 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
2025-11-03 14:08:03.278880: 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 14:08:03.290070: 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 00:00:1762175283.303122  181018 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175283.307232  181018 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175283.317124  181018 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175283.317145  181018 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175283.317147  181018 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175283.317149  181018 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:08:03.320180: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | 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)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[1. 0. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.4 [%]
Global accuracy score (test) = 81.69 [%]
Global F1 score (train) = 82.72 [%]
Global F1 score (test) = 82.23 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.72      0.58      0.64       350
MODERATE-INTENSITY       0.65      0.83      0.73       350
         SEDENTARY       0.98      0.95      0.96       350
VIGOROUS-INTENSITY       0.99      0.92      0.95       299

          accuracy                           0.82      1349
         macro avg       0.83      0.82      0.82      1349
      weighted avg       0.83      0.82      0.82      1349


Accuracy capturado en la ejecución 2: 81.69 [%]
F1-score capturado en la ejecución 2: 82.23 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
2025-11-03 14:08:23.497301: 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 14:08:23.515377: 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 00:00:1762175303.528620  181394 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175303.532773  181394 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175303.542735  181394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175303.542755  181394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175303.542757  181394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175303.542759  181394 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:08:23.545883: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | elapsed:    0.0s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 1. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.36 [%]
Global accuracy score (test) = 81.39 [%]
Global F1 score (train) = 82.7 [%]
Global F1 score (test) = 81.82 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.72      0.55      0.62       350
MODERATE-INTENSITY       0.64      0.85      0.73       350
         SEDENTARY       0.98      0.95      0.97       350
VIGOROUS-INTENSITY       0.99      0.92      0.95       299

          accuracy                           0.81      1349
         macro avg       0.83      0.82      0.82      1349
      weighted avg       0.83      0.81      0.81      1349


Accuracy capturado en la ejecución 3: 81.39 [%]
F1-score capturado en la ejecución 3: 81.82 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
2025-11-03 14:08:43.729915: 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 14:08:43.741287: 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 00:00:1762175323.754607  181764 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175323.758802  181764 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175323.768574  181764 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175323.768593  181764 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175323.768595  181764 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175323.768597  181764 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:08:43.771814: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 1. 1. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.58 [%]
Global accuracy score (test) = 83.25 [%]
Global F1 score (train) = 82.9 [%]
Global F1 score (test) = 83.75 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.74      0.62      0.67       350
MODERATE-INTENSITY       0.68      0.85      0.76       350
         SEDENTARY       0.98      0.95      0.96       350
VIGOROUS-INTENSITY       0.99      0.92      0.95       299

          accuracy                           0.83      1349
         macro avg       0.85      0.84      0.84      1349
      weighted avg       0.84      0.83      0.83      1349


Accuracy capturado en la ejecución 4: 83.25 [%]
F1-score capturado en la ejecución 4: 83.75 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
2025-11-03 14:09:03.864066: 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 14:09:03.875548: 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 00:00:1762175343.888970  182137 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175343.893280  182137 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175343.903251  182137 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175343.903272  182137 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175343.903275  182137 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175343.903276  182137 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:09:03.906585: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | elapsed:    0.0s 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)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.07 [%]
Global accuracy score (test) = 82.06 [%]
Global F1 score (train) = 82.39 [%]
Global F1 score (test) = 82.39 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.73      0.56      0.63       350
MODERATE-INTENSITY       0.66      0.86      0.74       350
         SEDENTARY       0.98      0.96      0.97       350
VIGOROUS-INTENSITY       0.98      0.92      0.95       299

          accuracy                           0.82      1349
         macro avg       0.84      0.82      0.82      1349
      weighted avg       0.83      0.82      0.82      1349


Accuracy capturado en la ejecución 5: 82.06 [%]
F1-score capturado en la ejecución 5: 82.39 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
2025-11-03 14:09:24.068941: 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 14:09:24.080431: 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 00:00:1762175364.093823  182522 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175364.098168  182522 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175364.108233  182522 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175364.108253  182522 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175364.108256  182522 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175364.108257  182522 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:09:24.111552: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.54 [%]
Global accuracy score (test) = 81.69 [%]
Global F1 score (train) = 82.88 [%]
Global F1 score (test) = 82.14 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.72      0.57      0.64       350
MODERATE-INTENSITY       0.64      0.81      0.71       350
         SEDENTARY       0.98      0.99      0.98       350
VIGOROUS-INTENSITY       0.99      0.92      0.95       299

          accuracy                           0.82      1349
         macro avg       0.83      0.82      0.82      1349
      weighted avg       0.83      0.82      0.82      1349


Accuracy capturado en la ejecución 6: 81.69 [%]
F1-score capturado en la ejecución 6: 82.14 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
2025-11-03 14:09:44.253617: 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 14:09:44.264674: 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 00:00:1762175384.277982  182906 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175384.281992  182906 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175384.291924  182906 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175384.291943  182906 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175384.291945  182906 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175384.291946  182906 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:09:44.295019: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.3s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 282 out of 282 | elapsed:    0.0s finished
1 GPU(s) detected and VRAM set to crossover mode..
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Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 1. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 81.65 [%]
Global accuracy score (test) = 80.43 [%]
Global F1 score (train) = 81.91 [%]
Global F1 score (test) = 81.05 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.69      0.56      0.62       350
MODERATE-INTENSITY       0.63      0.81      0.71       350
         SEDENTARY       0.98      0.95      0.96       350
VIGOROUS-INTENSITY       0.99      0.92      0.95       299

          accuracy                           0.80      1349
         macro avg       0.82      0.81      0.81      1349
      weighted avg       0.82      0.80      0.81      1349


Accuracy capturado en la ejecución 7: 80.43 [%]
F1-score capturado en la ejecución 7: 81.05 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
2025-11-03 14:10:04.424469: 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 14:10:04.435888: 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 00:00:1762175404.449290  183290 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175404.453566  183290 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175404.463361  183290 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175404.463381  183290 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175404.463383  183290 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175404.463385  183290 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:10:04.466651: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.1s 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 282 out of 282 | elapsed:    0.0s 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
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.38 [%]
Global accuracy score (test) = 80.5 [%]
Global F1 score (train) = 82.66 [%]
Global F1 score (test) = 80.82 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.72      0.52      0.60       350
MODERATE-INTENSITY       0.62      0.84      0.72       350
         SEDENTARY       0.98      0.96      0.97       350
VIGOROUS-INTENSITY       0.98      0.92      0.95       299

          accuracy                           0.81      1349
         macro avg       0.82      0.81      0.81      1349
      weighted avg       0.82      0.81      0.80      1349


Accuracy capturado en la ejecución 8: 80.5 [%]
F1-score capturado en la ejecución 8: 80.82 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
2025-11-03 14:10:24.809003: 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 14:10:24.820517: 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 00:00:1762175424.833801  183672 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175424.837846  183672 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175424.847927  183672 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175424.847949  183672 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175424.847951  183672 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175424.847953  183672 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:10:24.851209: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | elapsed:    0.0s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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)
Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 1. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.43 [%]
Global accuracy score (test) = 81.32 [%]
Global F1 score (train) = 82.74 [%]
Global F1 score (test) = 81.78 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.70      0.56      0.62       350
MODERATE-INTENSITY       0.65      0.84      0.73       350
         SEDENTARY       0.98      0.95      0.97       350
VIGOROUS-INTENSITY       0.99      0.92      0.95       299

          accuracy                           0.81      1349
         macro avg       0.83      0.82      0.82      1349
      weighted avg       0.82      0.81      0.81      1349


Accuracy capturado en la ejecución 9: 81.32 [%]
F1-score capturado en la ejecución 9: 81.78 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
2025-11-03 14:10:44.867815: 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 14:10:44.879116: 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 00:00:1762175444.892361  184046 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175444.896637  184046 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175444.906501  184046 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175444.906521  184046 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175444.906523  184046 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175444.906525  184046 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:10:44.909763: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | 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)
Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.44 [%]
Global accuracy score (test) = 82.21 [%]
Global F1 score (train) = 82.75 [%]
Global F1 score (test) = 82.52 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.74      0.56      0.64       350
MODERATE-INTENSITY       0.66      0.85      0.74       350
         SEDENTARY       0.98      0.97      0.97       350
VIGOROUS-INTENSITY       0.98      0.92      0.95       299

          accuracy                           0.82      1349
         macro avg       0.84      0.83      0.83      1349
      weighted avg       0.83      0.82      0.82      1349


Accuracy capturado en la ejecución 10: 82.21 [%]
F1-score capturado en la ejecución 10: 82.52 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
2025-11-03 14:11:05.091338: 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 14:11:05.102626: 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 00:00:1762175465.115781  184421 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175465.120019  184421 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175465.129821  184421 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175465.129843  184421 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175465.129845  184421 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175465.129846  184421 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:11:05.133055: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | elapsed:    0.0s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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)
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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Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 1. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.32 [%]
Global accuracy score (test) = 82.65 [%]
Global F1 score (train) = 82.68 [%]
Global F1 score (test) = 83.21 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.73      0.61      0.66       350
MODERATE-INTENSITY       0.66      0.82      0.73       350
         SEDENTARY       0.98      0.97      0.97       350
VIGOROUS-INTENSITY       1.00      0.92      0.96       299

          accuracy                           0.83      1349
         macro avg       0.84      0.83      0.83      1349
      weighted avg       0.84      0.83      0.83      1349


Accuracy capturado en la ejecución 11: 82.65 [%]
F1-score capturado en la ejecución 11: 83.21 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
2025-11-03 14:11:25.275617: 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 14:11:25.287035: 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 00:00:1762175485.300386  184797 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175485.304712  184797 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175485.314705  184797 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175485.314729  184797 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175485.314731  184797 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175485.314733  184797 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:11:25.318042: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.1s 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 282 out of 282 | 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)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 81.78 [%]
Global accuracy score (test) = 80.36 [%]
Global F1 score (train) = 82.08 [%]
Global F1 score (test) = 80.81 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.70      0.53      0.60       350
MODERATE-INTENSITY       0.63      0.83      0.71       350
         SEDENTARY       0.98      0.95      0.97       350
VIGOROUS-INTENSITY       0.99      0.92      0.95       299

          accuracy                           0.80      1349
         macro avg       0.82      0.81      0.81      1349
      weighted avg       0.82      0.80      0.80      1349


Accuracy capturado en la ejecución 12: 80.36 [%]
F1-score capturado en la ejecución 12: 80.81 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
2025-11-03 14:11:45.372427: 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 14:11:45.383529: 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 00:00:1762175505.396400  185171 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175505.400487  185171 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175505.410471  185171 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175505.410496  185171 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175505.410499  185171 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175505.410501  185171 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:11:45.413738: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | elapsed:    0.0s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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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)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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)
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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)
Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 81.94 [%]
Global accuracy score (test) = 80.65 [%]
Global F1 score (train) = 82.24 [%]
Global F1 score (test) = 81.17 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.70      0.56      0.62       350
MODERATE-INTENSITY       0.63      0.81      0.71       350
         SEDENTARY       0.98      0.96      0.97       350
VIGOROUS-INTENSITY       0.98      0.92      0.95       299

          accuracy                           0.81      1349
         macro avg       0.82      0.81      0.81      1349
      weighted avg       0.82      0.81      0.81      1349


Accuracy capturado en la ejecución 13: 80.65 [%]
F1-score capturado en la ejecución 13: 81.17 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
2025-11-03 14:12:05.559225: 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 14:12:05.570732: 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 00:00:1762175525.584014  185548 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175525.588163  185548 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175525.598110  185548 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175525.598132  185548 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175525.598134  185548 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175525.598136  185548 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:12:05.601378: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | elapsed:    0.0s finished
1 GPU(s) detected and VRAM set to crossover mode..
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Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[1. 0. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 81.88 [%]
Global accuracy score (test) = 79.24 [%]
Global F1 score (train) = 82.19 [%]
Global F1 score (test) = 79.7 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.67      0.50      0.57       350
MODERATE-INTENSITY       0.61      0.82      0.70       350
         SEDENTARY       0.98      0.95      0.97       350
VIGOROUS-INTENSITY       0.99      0.92      0.95       299

          accuracy                           0.79      1349
         macro avg       0.81      0.80      0.80      1349
      weighted avg       0.81      0.79      0.79      1349


Accuracy capturado en la ejecución 14: 79.24 [%]
F1-score capturado en la ejecución 14: 79.7 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
2025-11-03 14:12:25.981527: 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 14:12:25.992814: 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 00:00:1762175546.006186  185922 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175546.010589  185922 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175546.020903  185922 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175546.020924  185922 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175546.020926  185922 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175546.020928  185922 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:12:26.024150: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | elapsed:    0.0s 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
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 1. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.52 [%]
Global accuracy score (test) = 82.28 [%]
Global F1 score (train) = 82.85 [%]
Global F1 score (test) = 82.88 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.68      0.65      0.67       350
MODERATE-INTENSITY       0.69      0.77      0.73       350
         SEDENTARY       0.98      0.97      0.97       350
VIGOROUS-INTENSITY       0.98      0.92      0.95       299

          accuracy                           0.82      1349
         macro avg       0.83      0.83      0.83      1349
      weighted avg       0.83      0.82      0.82      1349


Accuracy capturado en la ejecución 15: 82.28 [%]
F1-score capturado en la ejecución 15: 82.88 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
2025-11-03 14:12:46.059133: 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 14:12:46.070512: 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 00:00:1762175566.083624  186301 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175566.087817  186301 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175566.097574  186301 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175566.097594  186301 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175566.097597  186301 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175566.097598  186301 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:12:46.100830: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | 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
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[1. 1. 1. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.19 [%]
Global accuracy score (test) = 82.13 [%]
Global F1 score (train) = 82.51 [%]
Global F1 score (test) = 82.64 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.72      0.59      0.64       350
MODERATE-INTENSITY       0.66      0.83      0.73       350
         SEDENTARY       0.98      0.97      0.97       350
VIGOROUS-INTENSITY       0.99      0.92      0.95       299

          accuracy                           0.82      1349
         macro avg       0.84      0.82      0.83      1349
      weighted avg       0.83      0.82      0.82      1349


Accuracy capturado en la ejecución 16: 82.13 [%]
F1-score capturado en la ejecución 16: 82.64 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
2025-11-03 14:13:06.152988: 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 14:13:06.164292: 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 00:00:1762175586.177555  186677 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175586.181764  186677 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175586.191735  186677 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175586.191755  186677 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175586.191758  186677 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175586.191760  186677 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:13:06.194996: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.3s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 282 out of 282 | elapsed:    0.0s 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
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.31 [%]
Global accuracy score (test) = 80.58 [%]
Global F1 score (train) = 82.64 [%]
Global F1 score (test) = 81.24 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.66      0.61      0.63       350
MODERATE-INTENSITY       0.65      0.74      0.69       350
         SEDENTARY       0.98      0.97      0.97       350
VIGOROUS-INTENSITY       0.98      0.92      0.95       299

          accuracy                           0.81      1349
         macro avg       0.82      0.81      0.81      1349
      weighted avg       0.81      0.81      0.81      1349


Accuracy capturado en la ejecución 17: 80.58 [%]
F1-score capturado en la ejecución 17: 81.24 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
2025-11-03 14:13:26.472402: 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 14:13:26.483462: 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 00:00:1762175606.496411  187050 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175606.500461  187050 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175606.510150  187050 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175606.510172  187050 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175606.510174  187050 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175606.510176  187050 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:13:26.513182: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | elapsed:    0.0s 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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Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 81.98 [%]
Global accuracy score (test) = 81.76 [%]
Global F1 score (train) = 82.26 [%]
Global F1 score (test) = 82.13 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.74      0.55      0.63       350
MODERATE-INTENSITY       0.64      0.86      0.74       350
         SEDENTARY       0.98      0.95      0.97       350
VIGOROUS-INTENSITY       0.98      0.92      0.95       299

          accuracy                           0.82      1349
         macro avg       0.84      0.82      0.82      1349
      weighted avg       0.83      0.82      0.82      1349


Accuracy capturado en la ejecución 18: 81.76 [%]
F1-score capturado en la ejecución 18: 82.13 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
2025-11-03 14:13:46.502324: 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 14:13:46.513668: 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 00:00:1762175626.526902  187427 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175626.531207  187427 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175626.541337  187427 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175626.541360  187427 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175626.541362  187427 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175626.541364  187427 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:13:46.544542: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.3s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 282 out of 282 | 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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Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[1. 1. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.49 [%]
Global accuracy score (test) = 80.5 [%]
Global F1 score (train) = 82.87 [%]
Global F1 score (test) = 80.97 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.70      0.53      0.61       350
MODERATE-INTENSITY       0.62      0.81      0.71       350
         SEDENTARY       0.98      0.97      0.97       350
VIGOROUS-INTENSITY       0.99      0.92      0.95       299

          accuracy                           0.81      1349
         macro avg       0.82      0.81      0.81      1349
      weighted avg       0.82      0.81      0.80      1349


Accuracy capturado en la ejecución 19: 80.5 [%]
F1-score capturado en la ejecución 19: 80.97 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
2025-11-03 14:14:06.752253: 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 14:14:06.763529: 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 00:00:1762175646.776773  187800 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175646.780973  187800 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175646.790809  187800 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175646.790829  187800 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175646.790831  187800 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175646.790833  187800 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:14:06.794048: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | elapsed:    0.0s 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)
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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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Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 81.87 [%]
Global accuracy score (test) = 80.5 [%]
Global F1 score (train) = 82.13 [%]
Global F1 score (test) = 81.04 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.68      0.55      0.61       350
MODERATE-INTENSITY       0.64      0.81      0.71       350
         SEDENTARY       0.98      0.96      0.97       350
VIGOROUS-INTENSITY       0.99      0.92      0.95       299

          accuracy                           0.81      1349
         macro avg       0.82      0.81      0.81      1349
      weighted avg       0.81      0.81      0.81      1349


Accuracy capturado en la ejecución 20: 80.5 [%]
F1-score capturado en la ejecución 20: 81.04 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
2025-11-03 14:14:26.700312: 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 14:14:26.711891: 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 00:00:1762175666.725213  188198 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175666.729502  188198 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175666.739523  188198 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175666.739546  188198 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175666.739548  188198 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175666.739550  188198 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:14:26.742828: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.3s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 282 out of 282 | elapsed:    0.0s 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)
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
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.39 [%]
Global accuracy score (test) = 80.28 [%]
Global F1 score (train) = 82.73 [%]
Global F1 score (test) = 80.76 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.69      0.53      0.60       350
MODERATE-INTENSITY       0.62      0.82      0.71       350
         SEDENTARY       0.98      0.95      0.97       350
VIGOROUS-INTENSITY       0.99      0.92      0.95       299

          accuracy                           0.80      1349
         macro avg       0.82      0.81      0.81      1349
      weighted avg       0.81      0.80      0.80      1349


Accuracy capturado en la ejecución 21: 80.28 [%]
F1-score capturado en la ejecución 21: 80.76 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
2025-11-03 14:14:46.821970: 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 14:14:46.833370: 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 00:00:1762175686.846732  188574 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175686.850847  188574 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175686.860903  188574 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175686.860922  188574 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175686.860925  188574 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175686.860927  188574 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:14:46.864222: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.3s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 282 out of 282 | elapsed:    0.0s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.75 [%]
Global accuracy score (test) = 82.43 [%]
Global F1 score (train) = 83.1 [%]
Global F1 score (test) = 82.8 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.75      0.56      0.64       350
MODERATE-INTENSITY       0.65      0.87      0.74       350
         SEDENTARY       0.98      0.97      0.97       350
VIGOROUS-INTENSITY       0.99      0.92      0.95       299

          accuracy                           0.82      1349
         macro avg       0.84      0.83      0.83      1349
      weighted avg       0.84      0.82      0.82      1349


Accuracy capturado en la ejecución 22: 82.43 [%]
F1-score capturado en la ejecución 22: 82.8 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
2025-11-03 14:15:06.825609: 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 14:15:06.836773: 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 00:00:1762175706.849908  188951 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175706.854051  188951 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175706.864027  188951 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175706.864050  188951 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175706.864052  188951 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175706.864054  188951 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:15:06.867226: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
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Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.07 [%]
Global accuracy score (test) = 81.76 [%]
Global F1 score (train) = 82.31 [%]
Global F1 score (test) = 82.12 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.73      0.54      0.62       350
MODERATE-INTENSITY       0.64      0.86      0.73       350
         SEDENTARY       0.98      0.97      0.97       350
VIGOROUS-INTENSITY       1.00      0.92      0.96       299

          accuracy                           0.82      1349
         macro avg       0.84      0.82      0.82      1349
      weighted avg       0.83      0.82      0.82      1349


Accuracy capturado en la ejecución 23: 81.76 [%]
F1-score capturado en la ejecución 23: 82.12 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
2025-11-03 14:15:27.126315: 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 14:15:27.137560: 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 00:00:1762175727.150949  189327 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175727.155168  189327 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175727.165008  189327 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175727.165029  189327 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175727.165031  189327 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175727.165032  189327 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:15:27.168279: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
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Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 81.87 [%]
Global accuracy score (test) = 81.84 [%]
Global F1 score (train) = 82.18 [%]
Global F1 score (test) = 82.19 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.74      0.55      0.63       350
MODERATE-INTENSITY       0.65      0.85      0.73       350
         SEDENTARY       0.98      0.96      0.97       350
VIGOROUS-INTENSITY       0.98      0.92      0.95       299

          accuracy                           0.82      1349
         macro avg       0.84      0.82      0.82      1349
      weighted avg       0.83      0.82      0.82      1349


Accuracy capturado en la ejecución 24: 81.84 [%]
F1-score capturado en la ejecución 24: 82.19 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
2025-11-03 14:15:47.282158: 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 14:15:47.293447: 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 00:00:1762175747.306546  189697 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175747.310761  189697 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175747.320582  189697 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175747.320602  189697 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175747.320604  189697 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175747.320606  189697 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:15:47.323831: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | elapsed:    0.0s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 1. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.21 [%]
Global accuracy score (test) = 80.28 [%]
Global F1 score (train) = 82.52 [%]
Global F1 score (test) = 80.75 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.69      0.53      0.60       350
MODERATE-INTENSITY       0.62      0.81      0.70       350
         SEDENTARY       0.98      0.97      0.97       350
VIGOROUS-INTENSITY       0.99      0.92      0.95       299

          accuracy                           0.80      1349
         macro avg       0.82      0.81      0.81      1349
      weighted avg       0.81      0.80      0.80      1349


Accuracy capturado en la ejecución 25: 80.28 [%]
F1-score capturado en la ejecución 25: 80.75 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
2025-11-03 14:16:07.360836: 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 14:16:07.372080: 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 00:00:1762175767.385139  190068 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175767.389355  190068 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175767.399417  190068 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175767.399444  190068 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175767.399446  190068 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175767.399448  190068 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:16:07.402690: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | elapsed:    0.0s 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)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.75 [%]
Global accuracy score (test) = 81.62 [%]
Global F1 score (train) = 83.08 [%]
Global F1 score (test) = 82.01 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.73      0.55      0.63       350
MODERATE-INTENSITY       0.64      0.86      0.73       350
         SEDENTARY       0.98      0.96      0.97       350
VIGOROUS-INTENSITY       0.99      0.92      0.95       299

          accuracy                           0.82      1349
         macro avg       0.84      0.82      0.82      1349
      weighted avg       0.83      0.82      0.82      1349


Accuracy capturado en la ejecución 26: 81.62 [%]
F1-score capturado en la ejecución 26: 82.01 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
2025-11-03 14:16:27.465405: 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 14:16:27.476703: 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 00:00:1762175787.489889  190443 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175787.494093  190443 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175787.503964  190443 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175787.503991  190443 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175787.503994  190443 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175787.503996  190443 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:16:27.507223: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | elapsed:    0.0s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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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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Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 1. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.36 [%]
Global accuracy score (test) = 80.65 [%]
Global F1 score (train) = 82.7 [%]
Global F1 score (test) = 81.37 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.65      0.63      0.64       350
MODERATE-INTENSITY       0.67      0.75      0.70       350
         SEDENTARY       0.98      0.95      0.97       350
VIGOROUS-INTENSITY       0.98      0.92      0.95       299

          accuracy                           0.81      1349
         macro avg       0.82      0.81      0.81      1349
      weighted avg       0.81      0.81      0.81      1349


Accuracy capturado en la ejecución 27: 80.65 [%]
F1-score capturado en la ejecución 27: 81.37 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
2025-11-03 14:16:47.557348: 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 14:16:47.568676: 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 00:00:1762175807.581831  190818 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175807.585849  190818 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175807.595768  190818 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175807.595796  190818 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175807.595798  190818 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175807.595800  190818 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:16:47.598899: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | 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)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 1. 1. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.3 [%]
Global accuracy score (test) = 80.13 [%]
Global F1 score (train) = 82.52 [%]
Global F1 score (test) = 80.56 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.69      0.53      0.60       350
MODERATE-INTENSITY       0.62      0.82      0.71       350
         SEDENTARY       0.98      0.96      0.97       350
VIGOROUS-INTENSITY       0.98      0.92      0.95       299

          accuracy                           0.80      1349
         macro avg       0.82      0.81      0.81      1349
      weighted avg       0.81      0.80      0.80      1349


Accuracy capturado en la ejecución 28: 80.13 [%]
F1-score capturado en la ejecución 28: 80.56 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
2025-11-03 14:17:07.590749: 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 14:17:07.602221: 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 00:00:1762175827.616189  191201 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762175827.620250  191201 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762175827.630309  191201 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175827.630332  191201 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175827.630343  191201 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762175827.630345  191201 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 14:17:07.633623: 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:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 282 out of 282 | elapsed:    3.2s 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 282 out of 282 | elapsed:    0.0s finished
1 GPU(s) detected and VRAM set to crossover mode..
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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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)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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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
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 82.28 [%]
Global accuracy score (test) = 81.54 [%]
Global F1 score (train) = 82.62 [%]
Global F1 score (test) = 82.07 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.72      0.58      0.64       350
MODERATE-INTENSITY       0.64      0.81      0.72       350
         SEDENTARY       0.98      0.97      0.97       350
VIGOROUS-INTENSITY       0.99      0.92      0.95       299

          accuracy                           0.82      1349
         macro avg       0.83      0.82      0.82      1349
      weighted avg       0.83      0.82      0.82      1349


Accuracy capturado en la ejecución 29: 81.54 [%]
F1-score capturado en la ejecución 29: 82.07 [%]

=== EJECUCIÓN 30 ===

--- TRAIN (ejecución 30) ---

--- TEST (ejecución 30) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Training a non-convolutional model.
Loaded model from disk
(1349, 91)
(12556, 91)
[[0.]
 [0.]
 [0.]
 ...
 [3.]
 [3.]
 [3.]]
(1349, 1)
[0. 0. 0. ... 3. 3. 2.]
(1349,)
-------------------------------------------------

Global accuracy score (train) = 81.98 [%]
Global accuracy score (test) = 80.58 [%]
Global F1 score (train) = 82.34 [%]
Global F1 score (test) = 81.13 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.69      0.56      0.62       350
MODERATE-INTENSITY       0.63      0.80      0.71       350
         SEDENTARY       0.98      0.96      0.97       350
VIGOROUS-INTENSITY       0.99      0.92      0.95       299

          accuracy                           0.81      1349
         macro avg       0.82      0.81      0.81      1349
      weighted avg       0.82      0.81      0.81      1349


Accuracy capturado en la ejecución 30: 80.58 [%]
F1-score capturado en la ejecución 30: 81.13 [%]

=== RESUMEN FINAL ===
Accuracies: [80.58, 81.69, 81.39, 83.25, 82.06, 81.69, 80.43, 80.5, 81.32, 82.21, 82.65, 80.36, 80.65, 79.24, 82.28, 82.13, 80.58, 81.76, 80.5, 80.5, 80.28, 82.43, 81.76, 81.84, 80.28, 81.62, 80.65, 80.13, 81.54, 80.58]
F1-scores: [81.13, 82.23, 81.82, 83.75, 82.39, 82.14, 81.05, 80.82, 81.78, 82.52, 83.21, 80.81, 81.17, 79.7, 82.88, 82.64, 81.24, 82.13, 80.97, 81.04, 80.76, 82.8, 82.12, 82.19, 80.75, 82.01, 81.37, 80.56, 82.07, 81.13]
Accuracy mean: 81.2293 | std: 0.9064
F1 mean: 81.7060 | std: 0.8896

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