2025-11-03 15:47:19.976662: 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 15:47:19.988587: 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 00:00:1762181240.002729  246730 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181240.006970  246730 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181240.017660  246730 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181240.017689  246730 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181240.017692  246730 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181240.017695  246730 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:47:20.020884: 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 15:47:28,358	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-03 15:47:29,034	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-03 15:47:29,107	INFO trial.py:182 -- Creating a new dirname dir_054b1_0d0f because trial dirname 'dir_054b1' already exists.
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2025-11-03 15:47:29,128	INFO trial.py:182 -- Creating a new dirname dir_054b1_a82d because trial dirname 'dir_054b1' already exists.
2025-11-03 15:47:29,131	INFO trial.py:182 -- Creating a new dirname dir_054b1_a4db because trial dirname 'dir_054b1' already exists.
2025-11-03 15:47:29,134	INFO trial.py:182 -- Creating a new dirname dir_054b1_da78 because trial dirname 'dir_054b1' already exists.
2025-11-03 15:47:29,137	INFO trial.py:182 -- Creating a new dirname dir_054b1_a8da because trial dirname 'dir_054b1' already exists.
2025-11-03 15:47:29,142	INFO trial.py:182 -- Creating a new dirname dir_054b1_75ac because trial dirname 'dir_054b1' already exists.
2025-11-03 15:47:29,146	INFO trial.py:182 -- Creating a new dirname dir_054b1_0b42 because trial dirname 'dir_054b1' already exists.
2025-11-03 15:47:29,151	INFO trial.py:182 -- Creating a new dirname dir_054b1_16a6 because trial dirname 'dir_054b1' already exists.
2025-11-03 15:47:29,154	INFO trial.py:182 -- Creating a new dirname dir_054b1_2f4e because trial dirname 'dir_054b1' already exists.
2025-11-03 15:47:29,161	INFO trial.py:182 -- Creating a new dirname dir_054b1_01d5 because trial dirname 'dir_054b1' already exists.
2025-11-03 15:47:29,169	INFO trial.py:182 -- Creating a new dirname dir_054b1_45c9 because trial dirname 'dir_054b1' already exists.
1 GPU(s) detected and VRAM set to crossover mode..
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Training a non-convolutional model.
Se lanza la búsqueda de hiperparámetros óptimos del modelo
╭──────────────────────────────────────────────────────────────────────╮
│ Configuration for experiment     BalancedRF_hyperparameters_tuning   │
├──────────────────────────────────────────────────────────────────────┤
│ Search algorithm                 BasicVariantGenerator               │
│ Scheduler                        AsyncHyperBandScheduler             │
│ Number of trials                 20                                  │
╰──────────────────────────────────────────────────────────────────────╯

View detailed results here: /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_C/case_C_BRF_acc_gyr_superclasses_CPA_METs/BalancedRF_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-03_15-47-27_651174_246730/artifacts/2025-11-03_15-47-29/BalancedRF_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-03 15:47:29. Total running time: 0s
Logical resource usage: 0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       n_estimators     max_depth     min_samples_split     min_samples_leaf   max_features       random_state │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_054b1    PENDING               482             6                    38                   17   sqrt                       9479 │
│ trial_054b1    PENDING               204             6                    33                   13   0.3                        8896 │
│ trial_054b1    PENDING               283             7                    21                   16   sqrt                       5669 │
│ trial_054b1    PENDING               400             5                    20                   17   sqrt                       6115 │
│ trial_054b1    PENDING               451             5                    38                   29   sqrt                       3121 │
│ trial_054b1    PENDING               468             5                    34                   25   sqrt                       5110 │
│ trial_054b1    PENDING               226             7                    51                   19   sqrt                       5553 │
│ trial_054b1    PENDING               434             6                    58                   24   sqrt                       6517 │
│ trial_054b1    PENDING               265             7                    51                   25   0.3                        4596 │
│ trial_054b1    PENDING               361             5                    33                   16   0.3                        2221 │
│ trial_054b1    PENDING               332             6                    32                   11   0.3                        6357 │
│ trial_054b1    PENDING               487             7                    45                   27   sqrt                        423 │
│ trial_054b1    PENDING               257             5                    42                   28   sqrt                       6732 │
│ trial_054b1    PENDING               255             5                    25                   17   0.3                        9631 │
│ trial_054b1    PENDING               271             6                    37                   17   0.3                        5920 │
│ trial_054b1    PENDING               454             6                    32                   16   sqrt                       4502 │
│ trial_054b1    PENDING               271             7                    32                   25   0.3                        9758 │
│ trial_054b1    PENDING               494             6                    23                   12   0.3                        4815 │
│ trial_054b1    PENDING               439             6                    50                   26   sqrt                       2496 │
│ trial_054b1    PENDING               339             5                    57                   16   sqrt                       6332 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_054b1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               25 │
│ min_samples_split              51 │
│ n_estimators                  265 │
│ random_state                 4596 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               17 │
│ min_samples_split              25 │
│ n_estimators                  255 │
│ random_state                 9631 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               17 │
│ min_samples_split              38 │
│ n_estimators                  482 │
│ random_state                 9479 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               16 │
│ min_samples_split              33 │
│ n_estimators                  361 │
│ random_state                 2221 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               24 │
│ min_samples_split              58 │
│ n_estimators                  434 │
│ random_state                 6517 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
[36m(train_brf_ray_tune pid=248402)[0m 2025-11-03 15:47:32.478201: 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=248402)[0m 2025-11-03 15:47:32.498630: 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=248412)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_brf_ray_tune pid=248412)[0m E0000 00:00:1762181252.501064  249565 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=248419)[0m E0000 00:00:1762181252.471596  249558 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=248419)[0m W0000 00:00:1762181252.491723  249558 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=248419)[0m W0000 00:00:1762181252.491779  249558 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=248419)[0m W0000 00:00:1762181252.491782  249558 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=248419)[0m W0000 00:00:1762181252.491784  249558 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=248419)[0m 2025-11-03 15:47:32.497825: 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=248419)[0m To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               13 │
│ min_samples_split              33 │
│ n_estimators                  204 │
│ random_state                 8896 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               17 │
│ min_samples_split              20 │
│ n_estimators                  400 │
│ random_state                 6115 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               26 │
│ min_samples_split              50 │
│ n_estimators                  439 │
│ random_state                 2496 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               11 │
│ min_samples_split              32 │
│ n_estimators                  332 │
│ random_state                 6357 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               25 │
│ min_samples_split              32 │
│ n_estimators                  271 │
│ random_state                 9758 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               16 │
│ min_samples_split              21 │
│ n_estimators                  283 │
│ random_state                 5669 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               19 │
│ min_samples_split              51 │
│ n_estimators                  226 │
│ random_state                 5553 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               25 │
│ min_samples_split              34 │
│ n_estimators                  468 │
│ random_state                 5110 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               12 │
│ min_samples_split              23 │
│ n_estimators                  494 │
│ random_state                 4815 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               17 │
│ min_samples_split              37 │
│ n_estimators                  271 │
│ random_state                 5920 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               29 │
│ min_samples_split              38 │
│ n_estimators                  451 │
│ random_state                 3121 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               27 │
│ min_samples_split              45 │
│ n_estimators                  487 │
│ random_state                  423 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
[36m(train_brf_ray_tune pid=248419)[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=248419)[0m   return fit_method(estimator, *args, **kwargs)
[36m(train_brf_ray_tune pid=248419)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=248419)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.8s
[36m(train_brf_ray_tune pid=248413)[0m 2025-11-03 15:47:33.190286: 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=248413)[0m 2025-11-03 15:47:33.212374: 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=248413)[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=248413)[0m E0000 00:00:1762181253.240800  249698 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=248413)[0m E0000 00:00:1762181253.248946  249698 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=248413)[0m W0000 00:00:1762181253.270906  249698 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=248413)[0m 2025-11-03 15:47:33.277153: 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=248413)[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=248413)[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=248413)[0m   return fit_method(estimator, *args, **kwargs)[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=248408)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 17x across cluster][0m
[36m(train_brf_ray_tune pid=248417)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    4.7s[32m [repeated 15x across cluster][0m
[36m(train_brf_ray_tune pid=248409)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=248389)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   11.4s[32m [repeated 6x across cluster][0m
[36m(train_brf_ray_tune pid=248413)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   12.4s[32m [repeated 9x across cluster][0m
[36m(train_brf_ray_tune pid=248418)[0m [Parallel(n_jobs=-1)]: Done 226 out of 226 | elapsed:   20.7s finished
[36m(train_brf_ray_tune pid=248418)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=248418)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=248418)[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=248418)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=248396)[0m [Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.4s[32m [repeated 7x across cluster][0m
[36m(train_brf_ray_tune pid=248396)[0m [Parallel(n_jobs=20)]: Done 283 out of 283 | elapsed:    0.6s finished[32m [repeated 6x across cluster][0m
[36m(train_brf_ray_tune pid=248408)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 4x across cluster][0m
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               16 │
│ min_samples_split              32 │
│ n_estimators                  454 │
│ random_state                 4502 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               28 │
│ min_samples_split              42 │
│ n_estimators                  257 │
│ random_state                 6732 │
╰───────────────────────────────────╯
Trial trial_054b1 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_054b1 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               16 │
│ min_samples_split              57 │
│ n_estimators                  339 │
│ random_state                 6332 │
╰───────────────────────────────────╯

Trial status: 20 RUNNING
Current time: 2025-11-03 15:47:59. Total running time: 30s
Logical resource usage: 20.0/20 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_054b1    RUNNING               482             6                    38                   17   sqrt                       9479 │
│ trial_054b1    RUNNING               204             6                    33                   13   0.3                        8896 │
│ trial_054b1    RUNNING               283             7                    21                   16   sqrt                       5669 │
│ trial_054b1    RUNNING               400             5                    20                   17   sqrt                       6115 │
│ trial_054b1    RUNNING               451             5                    38                   29   sqrt                       3121 │
│ trial_054b1    RUNNING               468             5                    34                   25   sqrt                       5110 │
│ trial_054b1    RUNNING               226             7                    51                   19   sqrt                       5553 │
│ trial_054b1    RUNNING               434             6                    58                   24   sqrt                       6517 │
│ trial_054b1    RUNNING               265             7                    51                   25   0.3                        4596 │
│ trial_054b1    RUNNING               361             5                    33                   16   0.3                        2221 │
│ trial_054b1    RUNNING               332             6                    32                   11   0.3                        6357 │
│ trial_054b1    RUNNING               487             7                    45                   27   sqrt                        423 │
│ trial_054b1    RUNNING               257             5                    42                   28   sqrt                       6732 │
│ trial_054b1    RUNNING               255             5                    25                   17   0.3                        9631 │
│ trial_054b1    RUNNING               271             6                    37                   17   0.3                        5920 │
│ trial_054b1    RUNNING               454             6                    32                   16   sqrt                       4502 │
│ trial_054b1    RUNNING               271             7                    32                   25   0.3                        9758 │
│ trial_054b1    RUNNING               494             6                    23                   12   0.3                        4815 │
│ trial_054b1    RUNNING               439             6                    50                   26   sqrt                       2496 │
│ trial_054b1    RUNNING               339             5                    57                   16   sqrt                       6332 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:47:59. Total running time: 30s
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             27.1999 │
│ time_total_s                 27.1999 │
│ training_iteration                 1 │
│ test_accuracy                0.70939 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:47:59. Total running time: 30s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:01. Total running time: 32s
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             29.7788 │
│ time_total_s                 29.7788 │
│ training_iteration                 1 │
│ test_accuracy                0.71262 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:02. Total running time: 33s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:03. Total running time: 34s
[36m(train_brf_ray_tune pid=248408)[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=248408)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=248410)[0m [Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:   27.7s[32m [repeated 13x across cluster][0m
[36m(train_brf_ray_tune pid=248413)[0m [Parallel(n_jobs=20)]: Done 339 out of 339 | elapsed:    0.7s finished[32m [repeated 7x across cluster][0m
[36m(train_brf_ray_tune pid=248413)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=248413)[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=248413)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=248402)[0m [Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.2s[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=248412)[0m [Parallel(n_jobs=-1)]: Done 255 out of 255 | elapsed:   36.0s finished[32m [repeated 19x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              31.106 │
│ time_total_s                  31.106 │
│ training_iteration                 1 │
│ test_accuracy                0.69968 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:03. Total running time: 34s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:06. Total running time: 37s
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             34.4707 │
│ time_total_s                 34.4707 │
│ training_iteration                 1 │
│ test_accuracy                0.69644 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:06. Total running time: 37s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:08. Total running time: 39s
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             35.4945 │
│ time_total_s                 35.4945 │
│ training_iteration                 1 │
│ test_accuracy                0.68932 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:08. Total running time: 39s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:10. Total running time: 41s
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             38.1076 │
│ time_total_s                 38.1076 │
│ training_iteration                 1 │
│ test_accuracy                0.69903 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:10. Total running time: 41s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:11. Total running time: 42s
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             39.6733 │
│ time_total_s                 39.6733 │
│ training_iteration                 1 │
│ test_accuracy                0.70744 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:11. Total running time: 42s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:11. Total running time: 42s
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             39.8411 │
│ time_total_s                 39.8411 │
│ training_iteration                 1 │
│ test_accuracy                 0.7068 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:11. Total running time: 42s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:12. Total running time: 42s
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             39.9845 │
│ time_total_s                 39.9845 │
│ training_iteration                 1 │
│ test_accuracy                0.70227 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:12. Total running time: 42s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:12. Total running time: 42s
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             39.4979 │
│ time_total_s                 39.4979 │
│ training_iteration                 1 │
│ test_accuracy                 0.7055 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:12. Total running time: 43s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:13. Total running time: 44s
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             41.0098 │
│ time_total_s                 41.0098 │
│ training_iteration                 1 │
│ test_accuracy                0.69644 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:13. Total running time: 44s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:13. Total running time: 44s
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             41.5343 │
│ time_total_s                 41.5343 │
│ training_iteration                 1 │
│ test_accuracy                0.70227 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:13. Total running time: 44s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:13. Total running time: 44s
[36m(train_brf_ray_tune pid=248445)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 16x across cluster][0m
[36m(train_brf_ray_tune pid=248445)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 8x across cluster][0m
[36m(train_brf_ray_tune pid=248445)[0m   _log_deprecation_warning([32m [repeated 8x across cluster][0m
[36m(train_brf_ray_tune pid=248445)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.4s[32m [repeated 35x across cluster][0m
[36m(train_brf_ray_tune pid=248409)[0m [Parallel(n_jobs=20)]: Done 487 out of 487 | elapsed:    0.4s finished[32m [repeated 9x across cluster][0m
2025-11-03 15:48:21,315	INFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_C/case_C_BRF_acc_gyr_superclasses_CPA_METs/BalancedRF_hyperparameters_tuning' in 0.0064s.
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             41.6886 │
│ time_total_s                 41.6886 │
│ training_iteration                 1 │
│ test_accuracy                0.70227 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:13. Total running time: 44s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:17. Total running time: 48s
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             45.3427 │
│ time_total_s                 45.3427 │
│ training_iteration                 1 │
│ test_accuracy                0.70809 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:17. Total running time: 48s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:18. Total running time: 49s
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             46.3967 │
│ time_total_s                 46.3967 │
│ training_iteration                 1 │
│ test_accuracy                0.71262 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:18. Total running time: 49s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:18. Total running time: 49s
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             46.5958 │
│ time_total_s                 46.5958 │
│ training_iteration                 1 │
│ test_accuracy                0.70874 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:18. Total running time: 49s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:19. Total running time: 50s
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             46.9948 │
│ time_total_s                 46.9948 │
│ training_iteration                 1 │
│ test_accuracy                0.70421 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:19. Total running time: 50s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:19. Total running time: 50s
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             47.5775 │
│ time_total_s                 47.5775 │
│ training_iteration                 1 │
│ test_accuracy                0.71456 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:19. Total running time: 50s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:20. Total running time: 50s
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             47.8437 │
│ time_total_s                 47.8437 │
│ training_iteration                 1 │
│ test_accuracy                0.70939 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:20. Total running time: 50s

Trial trial_054b1 finished iteration 1 at 2025-11-03 15:48:21. Total running time: 52s
╭──────────────────────────────────────╮
│ Trial trial_054b1 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             49.0351 │
│ time_total_s                 49.0351 │
│ training_iteration                 1 │
│ test_accuracy                0.70939 │
╰──────────────────────────────────────╯

Trial trial_054b1 completed after 1 iterations at 2025-11-03 15:48:21. Total running time: 52s

Trial status: 20 TERMINATED
Current time: 2025-11-03 15:48:21. Total running time: 52s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 271 out of 271 | 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 271 out of 271 | elapsed:    0.0s finished
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         n_estimators     max_depth     min_samples_split     min_samples_leaf   max_features       random_state     iter     total time (s)     test_accuracy │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_054b1    TERMINATED              482             6                    38                   17   sqrt                       9479        1            39.9845          0.702265 │
│ trial_054b1    TERMINATED              204             6                    33                   13   0.3                        8896        1            39.6733          0.707443 │
│ trial_054b1    TERMINATED              283             7                    21                   16   sqrt                       5669        1            29.7788          0.712621 │
│ trial_054b1    TERMINATED              400             5                    20                   17   sqrt                       6115        1            34.4707          0.69644  │
│ trial_054b1    TERMINATED              451             5                    38                   29   sqrt                       3121        1            38.1076          0.699029 │
│ trial_054b1    TERMINATED              468             5                    34                   25   sqrt                       5110        1            41.0098          0.69644  │
│ trial_054b1    TERMINATED              226             7                    51                   19   sqrt                       5553        1            27.1999          0.709385 │
│ trial_054b1    TERMINATED              434             6                    58                   24   sqrt                       6517        1            39.8411          0.706796 │
│ trial_054b1    TERMINATED              265             7                    51                   25   0.3                        4596        1            46.3967          0.712621 │
│ trial_054b1    TERMINATED              361             5                    33                   16   0.3                        2221        1            46.9948          0.704207 │
│ trial_054b1    TERMINATED              332             6                    32                   11   0.3                        6357        1            47.8437          0.709385 │
│ trial_054b1    TERMINATED              487             7                    45                   27   sqrt                        423        1            45.3427          0.708091 │
│ trial_054b1    TERMINATED              257             5                    42                   28   sqrt                       6732        1            31.106           0.699676 │
│ trial_054b1    TERMINATED              255             5                    25                   17   0.3                        9631        1            41.5343          0.702265 │
│ trial_054b1    TERMINATED              271             6                    37                   17   0.3                        5920        1            46.5958          0.708738 │
│ trial_054b1    TERMINATED              454             6                    32                   16   sqrt                       4502        1            39.4979          0.705502 │
│ trial_054b1    TERMINATED              271             7                    32                   25   0.3                        9758        1            47.5775          0.714563 │
│ trial_054b1    TERMINATED              494             6                    23                   12   0.3                        4815        1            49.0351          0.709385 │
│ trial_054b1    TERMINATED              439             6                    50                   26   sqrt                       2496        1            41.6886          0.702265 │
│ trial_054b1    TERMINATED              339             5                    57                   16   sqrt                       6332        1            35.4944          0.68932  │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'n_estimators': 271, 'max_depth': 7, 'min_samples_split': 32, 'min_samples_leaf': 25, 'max_features': 0.3, 'random_state': 9758}
Saved model to disk
[36m(train_brf_ray_tune pid=248414)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 14x across cluster][0m
[36m(train_brf_ray_tune pid=248414)[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 7x across cluster][0m
[36m(train_brf_ray_tune pid=248414)[0m   _log_deprecation_warning([32m [repeated 7x across cluster][0m
[36m(train_brf_ray_tune pid=248414)[0m [Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s[32m [repeated 33x across cluster][0m
[36m(train_brf_ray_tune pid=248414)[0m [Parallel(n_jobs=20)]: Done 494 out of 494 | elapsed:    0.1s finished[32m [repeated 18x across cluster][0m
2025-11-03 15:48:37.885450: 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 15:48:37.897048: 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 00:00:1762181317.910531  251964 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181317.914937  251964 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181317.924839  251964 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181317.924859  251964 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181317.924872  251964 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181317.924874  251964 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:48:37.928211: 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 271 out of 271 | 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 271 out of 271 | 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
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.55 [%]
Global accuracy score (test) = 71.65 [%]
Global F1 score (train) = 76.19 [%]
Global F1 score (test) = 70.61 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.73      0.34      0.47       400
MODERATE-INTENSITY       0.56      0.93      0.70       400
         SEDENTARY       0.84      0.84      0.84       400
VIGOROUS-INTENSITY       0.89      0.76      0.82       345

          accuracy                           0.72      1545
         macro avg       0.76      0.72      0.71      1545
      weighted avg       0.75      0.72      0.70      1545


Accuracy capturado en la ejecución 1: 71.65 [%]
F1-score capturado en la ejecución 1: 70.61 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
2025-11-03 15:48:59.568946: 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 15:48:59.580373: 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 00:00:1762181339.593473  252346 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181339.597828  252346 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181339.607981  252346 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181339.608005  252346 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181339.608008  252346 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181339.608011  252346 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:48:59.611189: 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 271 out of 271 | 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 271 out of 271 | 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
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 75.58 [%]
Global accuracy score (test) = 70.94 [%]
Global F1 score (train) = 75.11 [%]
Global F1 score (test) = 69.95 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.67      0.34      0.45       400
MODERATE-INTENSITY       0.56      0.92      0.70       400
         SEDENTARY       0.83      0.84      0.84       400
VIGOROUS-INTENSITY       0.89      0.76      0.82       345

          accuracy                           0.71      1545
         macro avg       0.74      0.71      0.70      1545
      weighted avg       0.73      0.71      0.70      1545


Accuracy capturado en la ejecución 2: 70.94 [%]
F1-score capturado en la ejecución 2: 69.95 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
2025-11-03 15:49:21.351187: 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 15:49:21.362593: 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 00:00:1762181361.376040  252745 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181361.380280  252745 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181361.390750  252745 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181361.390776  252745 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181361.390778  252745 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181361.390780  252745 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:49:21.394085: 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 271 out of 271 | 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 271 out of 271 | 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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Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.89 [%]
Global accuracy score (test) = 71.84 [%]
Global F1 score (train) = 76.55 [%]
Global F1 score (test) = 70.94 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.70      0.35      0.47       400
MODERATE-INTENSITY       0.57      0.92      0.70       400
         SEDENTARY       0.83      0.85      0.84       400
VIGOROUS-INTENSITY       0.91      0.76      0.83       345

          accuracy                           0.72      1545
         macro avg       0.75      0.72      0.71      1545
      weighted avg       0.75      0.72      0.71      1545


Accuracy capturado en la ejecución 3: 71.84 [%]
F1-score capturado en la ejecución 3: 70.94 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
2025-11-03 15:49:43.130089: 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 15:49:43.141469: 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 00:00:1762181383.154979  253143 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181383.159027  253143 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181383.169272  253143 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181383.169294  253143 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181383.169297  253143 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181383.169299  253143 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:49:43.172553: 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 271 out of 271 | 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 271 out of 271 | 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}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 0. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 75.91 [%]
Global accuracy score (test) = 71.46 [%]
Global F1 score (train) = 75.41 [%]
Global F1 score (test) = 70.53 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.67      0.34      0.45       400
MODERATE-INTENSITY       0.57      0.94      0.71       400
         SEDENTARY       0.84      0.82      0.83       400
VIGOROUS-INTENSITY       0.91      0.76      0.83       345

          accuracy                           0.71      1545
         macro avg       0.75      0.72      0.71      1545
      weighted avg       0.74      0.71      0.70      1545


Accuracy capturado en la ejecución 4: 71.46 [%]
F1-score capturado en la ejecución 4: 70.53 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
2025-11-03 15:50:05.128569: 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 15:50:05.139947: 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 00:00:1762181405.153468  253529 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181405.157727  253529 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181405.168052  253529 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181405.168077  253529 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181405.168080  253529 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181405.168081  253529 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:50:05.171354: 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 271 out of 271 | 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 271 out of 271 | 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}
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.13 [%]
Global accuracy score (test) = 71.84 [%]
Global F1 score (train) = 75.82 [%]
Global F1 score (test) = 71.1 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.69      0.37      0.48       400
MODERATE-INTENSITY       0.57      0.92      0.71       400
         SEDENTARY       0.83      0.83      0.83       400
VIGOROUS-INTENSITY       0.90      0.76      0.83       345

          accuracy                           0.72      1545
         macro avg       0.75      0.72      0.71      1545
      weighted avg       0.74      0.72      0.71      1545


Accuracy capturado en la ejecución 5: 71.84 [%]
F1-score capturado en la ejecución 5: 71.1 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
2025-11-03 15:50:27.428588: 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 15:50:27.440144: 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 00:00:1762181427.453787  253917 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181427.457863  253917 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181427.468441  253917 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181427.468463  253917 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181427.468466  253917 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181427.468468  253917 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:50:27.471551: 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 271 out of 271 | 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 271 out of 271 | 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
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 0. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.61 [%]
Global accuracy score (test) = 70.87 [%]
Global F1 score (train) = 76.21 [%]
Global F1 score (test) = 69.75 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.67      0.32      0.44       400
MODERATE-INTENSITY       0.56      0.93      0.70       400
         SEDENTARY       0.83      0.83      0.83       400
VIGOROUS-INTENSITY       0.90      0.77      0.83       345

          accuracy                           0.71      1545
         macro avg       0.74      0.71      0.70      1545
      weighted avg       0.73      0.71      0.69      1545


Accuracy capturado en la ejecución 6: 70.87 [%]
F1-score capturado en la ejecución 6: 69.75 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
2025-11-03 15:50:49.240507: 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 15:50:49.252371: 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 00:00:1762181449.266218  254320 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181449.270466  254320 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181449.280601  254320 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181449.280625  254320 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181449.280628  254320 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181449.280630  254320 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:50:49.283869: 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 271 out of 271 | 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 271 out of 271 | 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)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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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
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.52 [%]
Global accuracy score (test) = 70.36 [%]
Global F1 score (train) = 76.16 [%]
Global F1 score (test) = 69.58 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.63      0.33      0.44       400
MODERATE-INTENSITY       0.56      0.91      0.69       400
         SEDENTARY       0.82      0.82      0.82       400
VIGOROUS-INTENSITY       0.92      0.76      0.83       345

          accuracy                           0.70      1545
         macro avg       0.73      0.71      0.70      1545
      weighted avg       0.73      0.70      0.69      1545


Accuracy capturado en la ejecución 7: 70.36 [%]
F1-score capturado en la ejecución 7: 69.58 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
2025-11-03 15:51:11.394223: 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 15:51:11.405916: 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 00:00:1762181471.419839  254704 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181471.424332  254704 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181471.435078  254704 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181471.435103  254704 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181471.435105  254704 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181471.435107  254704 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:51:11.438462: 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 271 out of 271 | 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 271 out of 271 | 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)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 1. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.3 [%]
Global accuracy score (test) = 70.87 [%]
Global F1 score (train) = 75.94 [%]
Global F1 score (test) = 70.07 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.63      0.35      0.45       400
MODERATE-INTENSITY       0.57      0.90      0.70       400
         SEDENTARY       0.82      0.83      0.83       400
VIGOROUS-INTENSITY       0.90      0.76      0.82       345

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


Accuracy capturado en la ejecución 8: 70.87 [%]
F1-score capturado en la ejecución 8: 70.07 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
2025-11-03 15:51:33.215938: 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 15:51:33.227249: 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 00:00:1762181493.240617  255089 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181493.244904  255089 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181493.254946  255089 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181493.254966  255089 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181493.254969  255089 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181493.254970  255089 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:51:33.258112: 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 271 out of 271 | 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 271 out of 271 | 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)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 75.97 [%]
Global accuracy score (test) = 71.78 [%]
Global F1 score (train) = 75.57 [%]
Global F1 score (test) = 70.93 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.66      0.35      0.45       400
MODERATE-INTENSITY       0.57      0.92      0.70       400
         SEDENTARY       0.84      0.85      0.84       400
VIGOROUS-INTENSITY       0.93      0.76      0.83       345

          accuracy                           0.72      1545
         macro avg       0.75      0.72      0.71      1545
      weighted avg       0.74      0.72      0.70      1545


Accuracy capturado en la ejecución 9: 71.78 [%]
F1-score capturado en la ejecución 9: 70.93 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
2025-11-03 15:51:55.185544: 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 15:51:55.197312: 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 00:00:1762181515.210921  255486 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181515.215216  255486 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181515.225511  255486 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181515.225532  255486 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181515.225534  255486 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181515.225536  255486 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:51:55.228933: 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 271 out of 271 | 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 271 out of 271 | 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)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.46 [%]
Global accuracy score (test) = 71.65 [%]
Global F1 score (train) = 76.07 [%]
Global F1 score (test) = 70.76 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.68      0.35      0.47       400
MODERATE-INTENSITY       0.57      0.92      0.70       400
         SEDENTARY       0.83      0.84      0.84       400
VIGOROUS-INTENSITY       0.89      0.76      0.82       345

          accuracy                           0.72      1545
         macro avg       0.75      0.72      0.71      1545
      weighted avg       0.74      0.72      0.70      1545


Accuracy capturado en la ejecución 10: 71.65 [%]
F1-score capturado en la ejecución 10: 70.76 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
2025-11-03 15:52:17.510812: 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 15:52:17.522303: 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 00:00:1762181537.536041  255876 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181537.540272  255876 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181537.550621  255876 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181537.550644  255876 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181537.550647  255876 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181537.550648  255876 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:52:17.553972: 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 271 out of 271 | 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 271 out of 271 | 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)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.95 [%]
Global accuracy score (test) = 70.03 [%]
Global F1 score (train) = 76.66 [%]
Global F1 score (test) = 69.05 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.66      0.33      0.44       400
MODERATE-INTENSITY       0.56      0.92      0.69       400
         SEDENTARY       0.83      0.81      0.82       400
VIGOROUS-INTENSITY       0.87      0.76      0.81       345

          accuracy                           0.70      1545
         macro avg       0.73      0.70      0.69      1545
      weighted avg       0.72      0.70      0.69      1545


Accuracy capturado en la ejecución 11: 70.03 [%]
F1-score capturado en la ejecución 11: 69.05 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
2025-11-03 15:52:39.385046: 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 15:52:39.396340: 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 00:00:1762181559.409375  256292 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181559.413709  256292 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181559.423783  256292 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181559.423805  256292 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181559.423807  256292 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181559.423818  256292 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:52:39.426898: 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 271 out of 271 | 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 271 out of 271 | 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)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[0. 0. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.52 [%]
Global accuracy score (test) = 70.49 [%]
Global F1 score (train) = 76.16 [%]
Global F1 score (test) = 69.66 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.64      0.34      0.45       400
MODERATE-INTENSITY       0.56      0.89      0.69       400
         SEDENTARY       0.84      0.84      0.84       400
VIGOROUS-INTENSITY       0.88      0.76      0.81       345

          accuracy                           0.70      1545
         macro avg       0.73      0.71      0.70      1545
      weighted avg       0.72      0.70      0.69      1545


Accuracy capturado en la ejecución 12: 70.49 [%]
F1-score capturado en la ejecución 12: 69.66 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
2025-11-03 15:53:01.511160: 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 15:53:01.522759: 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 00:00:1762181581.537097  256675 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181581.541521  256675 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181581.552077  256675 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181581.552104  256675 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181581.552106  256675 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181581.552108  256675 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:53:01.555435: 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 271 out of 271 | 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 271 out of 271 | 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)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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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)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[0. 1. 1. ... 0. 0. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.68 [%]
Global accuracy score (test) = 71.07 [%]
Global F1 score (train) = 76.4 [%]
Global F1 score (test) = 70.18 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.65      0.34      0.45       400
MODERATE-INTENSITY       0.56      0.91      0.69       400
         SEDENTARY       0.85      0.84      0.85       400
VIGOROUS-INTENSITY       0.89      0.76      0.82       345

          accuracy                           0.71      1545
         macro avg       0.74      0.71      0.70      1545
      weighted avg       0.73      0.71      0.70      1545


Accuracy capturado en la ejecución 13: 71.07 [%]
F1-score capturado en la ejecución 13: 70.18 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
2025-11-03 15:53:23.783399: 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 15:53:23.795063: 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 00:00:1762181603.808408  257063 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181603.812931  257063 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181603.823193  257063 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181603.823216  257063 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181603.823218  257063 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181603.823220  257063 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:53:23.826549: 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 271 out of 271 | 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 271 out of 271 | 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)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 75.86 [%]
Global accuracy score (test) = 71.2 [%]
Global F1 score (train) = 75.29 [%]
Global F1 score (test) = 69.95 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.70      0.32      0.44       400
MODERATE-INTENSITY       0.56      0.94      0.70       400
         SEDENTARY       0.84      0.84      0.84       400
VIGOROUS-INTENSITY       0.89      0.76      0.82       345

          accuracy                           0.71      1545
         macro avg       0.75      0.71      0.70      1545
      weighted avg       0.74      0.71      0.70      1545


Accuracy capturado en la ejecución 14: 71.2 [%]
F1-score capturado en la ejecución 14: 69.95 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
2025-11-03 15:53:46.061325: 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 15:53:46.073239: 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 00:00:1762181626.087629  257472 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181626.091938  257472 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181626.102764  257472 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181626.102790  257472 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181626.102792  257472 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181626.102794  257472 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:53:46.106023: 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 271 out of 271 | 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 271 out of 271 | 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)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.82 [%]
Global accuracy score (test) = 71.39 [%]
Global F1 score (train) = 76.49 [%]
Global F1 score (test) = 70.7 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.64      0.36      0.46       400
MODERATE-INTENSITY       0.57      0.92      0.70       400
         SEDENTARY       0.83      0.83      0.83       400
VIGOROUS-INTENSITY       0.92      0.76      0.83       345

          accuracy                           0.71      1545
         macro avg       0.74      0.72      0.71      1545
      weighted avg       0.74      0.71      0.70      1545


Accuracy capturado en la ejecución 15: 71.39 [%]
F1-score capturado en la ejecución 15: 70.7 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
2025-11-03 15:54:08.294155: 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 15:54:08.305585: 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 00:00:1762181648.319326  257841 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181648.323370  257841 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181648.333918  257841 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181648.333944  257841 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181648.333946  257841 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181648.333948  257841 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:54:08.337027: 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 271 out of 271 | 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 271 out of 271 | 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
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 0. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.75 [%]
Global accuracy score (test) = 70.55 [%]
Global F1 score (train) = 76.48 [%]
Global F1 score (test) = 69.69 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.64      0.34      0.45       400
MODERATE-INTENSITY       0.57      0.91      0.70       400
         SEDENTARY       0.82      0.82      0.82       400
VIGOROUS-INTENSITY       0.90      0.76      0.82       345

          accuracy                           0.71      1545
         macro avg       0.73      0.71      0.70      1545
      weighted avg       0.73      0.71      0.69      1545


Accuracy capturado en la ejecución 16: 70.55 [%]
F1-score capturado en la ejecución 16: 69.69 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
2025-11-03 15:54:30.321858: 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 15:54:30.333555: 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 00:00:1762181670.347132  258233 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181670.351260  258233 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181670.361569  258233 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181670.361593  258233 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181670.361596  258233 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181670.361598  258233 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:54:30.364699: 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 271 out of 271 | 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 271 out of 271 | 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)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 0. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.34 [%]
Global accuracy score (test) = 70.68 [%]
Global F1 score (train) = 76.03 [%]
Global F1 score (test) = 70.03 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.65      0.37      0.47       400
MODERATE-INTENSITY       0.57      0.89      0.69       400
         SEDENTARY       0.83      0.82      0.82       400
VIGOROUS-INTENSITY       0.88      0.76      0.81       345

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


Accuracy capturado en la ejecución 17: 70.68 [%]
F1-score capturado en la ejecución 17: 70.03 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
2025-11-03 15:54:52.649695: 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 15:54:52.661227: 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 00:00:1762181692.674831  258640 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181692.679234  258640 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181692.689197  258640 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181692.689229  258640 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181692.689232  258640 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181692.689233  258640 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:54:52.692624: 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 271 out of 271 | 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 271 out of 271 | 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)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.49 [%]
Global accuracy score (test) = 69.97 [%]
Global F1 score (train) = 76.22 [%]
Global F1 score (test) = 69.12 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.63      0.33      0.43       400
MODERATE-INTENSITY       0.55      0.90      0.68       400
         SEDENTARY       0.83      0.82      0.83       400
VIGOROUS-INTENSITY       0.89      0.76      0.82       345

          accuracy                           0.70      1545
         macro avg       0.73      0.70      0.69      1545
      weighted avg       0.72      0.70      0.69      1545


Accuracy capturado en la ejecución 18: 69.97 [%]
F1-score capturado en la ejecución 18: 69.12 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
2025-11-03 15:55:14.820411: 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 15:55:14.832138: 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 00:00:1762181714.845611  259028 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181714.849874  259028 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181714.860016  259028 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181714.860039  259028 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181714.860042  259028 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181714.860044  259028 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:55:14.863395: 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 271 out of 271 | 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 271 out of 271 | 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
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[0. 1. 0. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.38 [%]
Global accuracy score (test) = 69.13 [%]
Global F1 score (train) = 76.0 [%]
Global F1 score (test) = 67.97 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.62      0.30      0.40       400
MODERATE-INTENSITY       0.55      0.90      0.68       400
         SEDENTARY       0.82      0.82      0.82       400
VIGOROUS-INTENSITY       0.88      0.75      0.81       345

          accuracy                           0.69      1545
         macro avg       0.72      0.69      0.68      1545
      weighted avg       0.71      0.69      0.68      1545


Accuracy capturado en la ejecución 19: 69.13 [%]
F1-score capturado en la ejecución 19: 67.97 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
2025-11-03 15:55:37.164663: 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 15:55:37.176009: 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 00:00:1762181737.189585  259440 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181737.194003  259440 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181737.204108  259440 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181737.204135  259440 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181737.204137  259440 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181737.204139  259440 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:55:37.207468: 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 271 out of 271 | 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 271 out of 271 | 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)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.83 [%]
Global accuracy score (test) = 71.52 [%]
Global F1 score (train) = 76.62 [%]
Global F1 score (test) = 70.75 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.67      0.36      0.47       400
MODERATE-INTENSITY       0.57      0.93      0.71       400
         SEDENTARY       0.84      0.82      0.83       400
VIGOROUS-INTENSITY       0.91      0.76      0.83       345

          accuracy                           0.72      1545
         macro avg       0.75      0.72      0.71      1545
      weighted avg       0.74      0.72      0.70      1545


Accuracy capturado en la ejecución 20: 71.52 [%]
F1-score capturado en la ejecución 20: 70.75 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
2025-11-03 15:55:59.260529: 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 15:55:59.271990: 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 00:00:1762181759.285805  259828 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181759.289865  259828 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181759.300376  259828 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181759.300400  259828 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181759.300403  259828 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181759.300405  259828 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:55:59.303630: 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 271 out of 271 | 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 271 out of 271 | 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)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 0. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.23 [%]
Global accuracy score (test) = 70.94 [%]
Global F1 score (train) = 75.84 [%]
Global F1 score (test) = 69.91 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.69      0.33      0.45       400
MODERATE-INTENSITY       0.56      0.93      0.70       400
         SEDENTARY       0.83      0.83      0.83       400
VIGOROUS-INTENSITY       0.90      0.76      0.82       345

          accuracy                           0.71      1545
         macro avg       0.74      0.71      0.70      1545
      weighted avg       0.74      0.71      0.69      1545


Accuracy capturado en la ejecución 21: 70.94 [%]
F1-score capturado en la ejecución 21: 69.91 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
2025-11-03 15:56:21.507099: 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 15:56:21.519163: 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 00:00:1762181781.533278  260222 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181781.537618  260222 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181781.547993  260222 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181781.548018  260222 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181781.548021  260222 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181781.548022  260222 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:56:21.551316: 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 271 out of 271 | 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 271 out of 271 | 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)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 75.92 [%]
Global accuracy score (test) = 70.87 [%]
Global F1 score (train) = 75.38 [%]
Global F1 score (test) = 69.95 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.66      0.34      0.45       400
MODERATE-INTENSITY       0.56      0.90      0.69       400
         SEDENTARY       0.83      0.84      0.84       400
VIGOROUS-INTENSITY       0.88      0.77      0.82       345

          accuracy                           0.71      1545
         macro avg       0.74      0.71      0.70      1545
      weighted avg       0.73      0.71      0.70      1545


Accuracy capturado en la ejecución 22: 70.87 [%]
F1-score capturado en la ejecución 22: 69.95 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
2025-11-03 15:56:43.767207: 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 15:56:43.778874: 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 00:00:1762181803.792537  260640 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181803.796871  260640 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181803.806961  260640 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181803.806984  260640 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181803.806987  260640 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181803.806990  260640 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:56:43.810460: 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 271 out of 271 | 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 271 out of 271 | 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)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 0. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.1 [%]
Global accuracy score (test) = 70.49 [%]
Global F1 score (train) = 75.58 [%]
Global F1 score (test) = 69.21 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.65      0.30      0.41       400
MODERATE-INTENSITY       0.56      0.93      0.70       400
         SEDENTARY       0.83      0.84      0.84       400
VIGOROUS-INTENSITY       0.91      0.76      0.83       345

          accuracy                           0.70      1545
         macro avg       0.74      0.71      0.69      1545
      weighted avg       0.73      0.70      0.69      1545


Accuracy capturado en la ejecución 23: 70.49 [%]
F1-score capturado en la ejecución 23: 69.21 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
2025-11-03 15:57:06.164291: 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 15:57:06.175859: 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 00:00:1762181826.189387  261026 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181826.193711  261026 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181826.203912  261026 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181826.203939  261026 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181826.203942  261026 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181826.203944  261026 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:57:06.207061: 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 271 out of 271 | 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 271 out of 271 | 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)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 0. 0. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 77.26 [%]
Global accuracy score (test) = 71.72 [%]
Global F1 score (train) = 77.15 [%]
Global F1 score (test) = 71.38 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.65      0.40      0.49       400
MODERATE-INTENSITY       0.57      0.88      0.69       400
         SEDENTARY       0.85      0.84      0.84       400
VIGOROUS-INTENSITY       0.91      0.76      0.83       345

          accuracy                           0.72      1545
         macro avg       0.74      0.72      0.71      1545
      weighted avg       0.74      0.72      0.71      1545


Accuracy capturado en la ejecución 24: 71.72 [%]
F1-score capturado en la ejecución 24: 71.38 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
2025-11-03 15:57:28.469301: 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 15:57:28.481305: 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 00:00:1762181848.495294  261438 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181848.499733  261438 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181848.510156  261438 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181848.510183  261438 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181848.510185  261438 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181848.510187  261438 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:57:28.513551: 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 271 out of 271 | 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 271 out of 271 | 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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Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[0. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.9 [%]
Global accuracy score (test) = 72.49 [%]
Global F1 score (train) = 76.68 [%]
Global F1 score (test) = 71.69 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.71      0.38      0.49       400
MODERATE-INTENSITY       0.58      0.92      0.71       400
         SEDENTARY       0.84      0.84      0.84       400
VIGOROUS-INTENSITY       0.89      0.77      0.82       345

          accuracy                           0.72      1545
         macro avg       0.75      0.73      0.72      1545
      weighted avg       0.75      0.72      0.71      1545


Accuracy capturado en la ejecución 25: 72.49 [%]
F1-score capturado en la ejecución 25: 71.69 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
2025-11-03 15:57:50.805448: 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 15:57:50.817452: 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 00:00:1762181870.831541  261818 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181870.835982  261818 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181870.846473  261818 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181870.846563  261818 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181870.846567  261818 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181870.846568  261818 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:57:50.849778: 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 271 out of 271 | 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 271 out of 271 | 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)
Training a non-convolutional model.
Saved model to disk
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 77.02 [%]
Global accuracy score (test) = 71.52 [%]
Global F1 score (train) = 76.8 [%]
Global F1 score (test) = 70.82 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.67      0.37      0.48       400
MODERATE-INTENSITY       0.57      0.90      0.70       400
         SEDENTARY       0.84      0.84      0.84       400
VIGOROUS-INTENSITY       0.89      0.76      0.82       345

          accuracy                           0.72      1545
         macro avg       0.74      0.72      0.71      1545
      weighted avg       0.74      0.72      0.70      1545


Accuracy capturado en la ejecución 26: 71.52 [%]
F1-score capturado en la ejecución 26: 70.82 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
2025-11-03 15:58:12.907649: 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 15:58:12.919404: 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 00:00:1762181892.933076  262199 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181892.937309  262199 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181892.947584  262199 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181892.947608  262199 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181892.947610  262199 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181892.947612  262199 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:58:12.950912: 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 271 out of 271 | 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 271 out of 271 | 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)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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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)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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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.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.67 [%]
Global accuracy score (test) = 70.55 [%]
Global F1 score (train) = 76.43 [%]
Global F1 score (test) = 69.46 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.65      0.31      0.42       400
MODERATE-INTENSITY       0.55      0.92      0.69       400
         SEDENTARY       0.84      0.84      0.84       400
VIGOROUS-INTENSITY       0.90      0.76      0.82       345

          accuracy                           0.71      1545
         macro avg       0.74      0.71      0.69      1545
      weighted avg       0.73      0.71      0.69      1545


Accuracy capturado en la ejecución 27: 70.55 [%]
F1-score capturado en la ejecución 27: 69.46 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
2025-11-03 15:58:34.998400: 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 15:58:35.010202: 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 00:00:1762181915.023727  262593 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181915.028116  262593 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181915.038116  262593 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181915.038139  262593 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181915.038141  262593 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181915.038143  262593 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:58:35.041446: 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:    2.1s
[Parallel(n_jobs=-1)]: Done 271 out of 271 | 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 271 out of 271 | 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)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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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)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (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)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.32 [%]
Global accuracy score (test) = 71.0 [%]
Global F1 score (train) = 76.05 [%]
Global F1 score (test) = 70.12 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.68      0.35      0.46       400
MODERATE-INTENSITY       0.57      0.90      0.70       400
         SEDENTARY       0.83      0.84      0.83       400
VIGOROUS-INTENSITY       0.88      0.76      0.81       345

          accuracy                           0.71      1545
         macro avg       0.74      0.71      0.70      1545
      weighted avg       0.73      0.71      0.70      1545


Accuracy capturado en la ejecución 28: 71.0 [%]
F1-score capturado en la ejecución 28: 70.12 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
2025-11-03 15:58:57.020813: 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 15:58:57.032502: 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 00:00:1762181937.046347  262977 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762181937.050760  262977 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762181937.061073  262977 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181937.061100  262977 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181937.061113  262977 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762181937.061114  262977 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-03 15:58:57.064302: 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 271 out of 271 | 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 271 out of 271 | 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
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 0. 1. ... 2. 0. 0.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.89 [%]
Global accuracy score (test) = 70.94 [%]
Global F1 score (train) = 76.59 [%]
Global F1 score (test) = 70.13 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.68      0.35      0.46       400
MODERATE-INTENSITY       0.56      0.91      0.69       400
         SEDENTARY       0.83      0.83      0.83       400
VIGOROUS-INTENSITY       0.90      0.76      0.82       345

          accuracy                           0.71      1545
         macro avg       0.74      0.71      0.70      1545
      weighted avg       0.74      0.71      0.70      1545


Accuracy capturado en la ejecución 29: 70.94 [%]
F1-score capturado en la ejecución 29: 70.13 [%]

=== EJECUCIÓN 30 ===

--- TRAIN (ejecución 30) ---

--- TEST (ejecución 30) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Training a non-convolutional model.
Loaded model from disk
(1545, 91)
(13286, 91)
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[1. 1. 1. ... 2. 2. 2.]
(1545,)
-------------------------------------------------

Global accuracy score (train) = 76.34 [%]
Global accuracy score (test) = 70.61 [%]
Global F1 score (train) = 75.98 [%]
Global F1 score (test) = 69.5 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.67      0.33      0.44       400
MODERATE-INTENSITY       0.57      0.92      0.71       400
         SEDENTARY       0.81      0.82      0.82       400
VIGOROUS-INTENSITY       0.88      0.76      0.82       345

          accuracy                           0.71      1545
         macro avg       0.73      0.71      0.69      1545
      weighted avg       0.73      0.71      0.69      1545


Accuracy capturado en la ejecución 30: 70.61 [%]
F1-score capturado en la ejecución 30: 69.5 [%]

=== RESUMEN FINAL ===
Accuracies: [71.65, 70.94, 71.84, 71.46, 71.84, 70.87, 70.36, 70.87, 71.78, 71.65, 70.03, 70.49, 71.07, 71.2, 71.39, 70.55, 70.68, 69.97, 69.13, 71.52, 70.94, 70.87, 70.49, 71.72, 72.49, 71.52, 70.55, 71.0, 70.94, 70.61]
F1-scores: [70.61, 69.95, 70.94, 70.53, 71.1, 69.75, 69.58, 70.07, 70.93, 70.76, 69.05, 69.66, 70.18, 69.95, 70.7, 69.69, 70.03, 69.12, 67.97, 70.75, 69.91, 69.95, 69.21, 71.38, 71.69, 70.82, 69.46, 70.12, 70.13, 69.5]
Accuracy mean: 71.0140 | std: 0.6757
F1 mean: 70.1163 | std: 0.7696

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