2025-10-26 20:15:12.411550: 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`.
2025-10-26 20:15:12.424376: 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 00:00:1761506112.438753 3766520 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761506112.443099 3766520 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761506112.454812 3766520 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506112.454839 3766520 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506112.454842 3766520 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506112.454845 3766520 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:15:12.458186: 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(
2025-10-26 20:15:39,751	INFO worker.py:1927 -- Started a local Ray instance.
2025-10-26 20:15:40,438	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-10-26 20:15:40,499	INFO trial.py:182 -- Creating a new dirname dir_29368_a92e because trial dirname 'dir_29368' already exists.
2025-10-26 20:15:40,503	INFO trial.py:182 -- Creating a new dirname dir_29368_e2e4 because trial dirname 'dir_29368' already exists.
2025-10-26 20:15:40,505	INFO trial.py:182 -- Creating a new dirname dir_29368_084c because trial dirname 'dir_29368' already exists.
2025-10-26 20:15:40,507	INFO trial.py:182 -- Creating a new dirname dir_29368_3146 because trial dirname 'dir_29368' already exists.
2025-10-26 20:15:40,510	INFO trial.py:182 -- Creating a new dirname dir_29368_a675 because trial dirname 'dir_29368' already exists.
2025-10-26 20:15:40,512	INFO trial.py:182 -- Creating a new dirname dir_29368_abb4 because trial dirname 'dir_29368' already exists.
2025-10-26 20:15:40,514	INFO trial.py:182 -- Creating a new dirname dir_29368_3107 because trial dirname 'dir_29368' already exists.
2025-10-26 20:15:40,517	INFO trial.py:182 -- Creating a new dirname dir_29368_562d because trial dirname 'dir_29368' already exists.
2025-10-26 20:15:40,519	INFO trial.py:182 -- Creating a new dirname dir_29368_b4e2 because trial dirname 'dir_29368' already exists.
2025-10-26 20:15:40,522	INFO trial.py:182 -- Creating a new dirname dir_29368_d028 because trial dirname 'dir_29368' already exists.
2025-10-26 20:15:40,525	INFO trial.py:182 -- Creating a new dirname dir_29368_9428 because trial dirname 'dir_29368' already exists.
2025-10-26 20:15:40,527	INFO trial.py:182 -- Creating a new dirname dir_29368_c512 because trial dirname 'dir_29368' already exists.
2025-10-26 20:15:40,530	INFO trial.py:182 -- Creating a new dirname dir_29368_8d88 because trial dirname 'dir_29368' already exists.
2025-10-26 20:15:40,536	INFO trial.py:182 -- Creating a new dirname dir_29368_adbd because trial dirname 'dir_29368' already exists.
2025-10-26 20:15:40,539	INFO trial.py:182 -- Creating a new dirname dir_29368_189a because trial dirname 'dir_29368' already exists.
2025-10-26 20:15:40,543	INFO trial.py:182 -- Creating a new dirname dir_29368_8704 because trial dirname 'dir_29368' already exists.
2025-10-26 20:15:40,547	INFO trial.py:182 -- Creating a new dirname dir_29368_c9fe because trial dirname 'dir_29368' already exists.
2025-10-26 20:15:40,551	INFO trial.py:182 -- Creating a new dirname dir_29368_de4e because trial dirname 'dir_29368' already exists.
2025-10-26 20:15:40,560	INFO trial.py:182 -- Creating a new dirname dir_29368_9331 because trial dirname 'dir_29368' already exists.
1 GPU(s) detected and VRAM set to crossover mode..
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_17_classes/BalancedRF_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-10-26_20-15-39_012801_3766520/artifacts/2025-10-26_20-15-40/BalancedRF_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-10-26 20:15:40. 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_29368    PENDING               424             7                    27                   22   sqrt                       9030 │
│ trial_29368    PENDING               341             5                    45                   14   0.3                        7546 │
│ trial_29368    PENDING               362             6                    40                   13   sqrt                       9553 │
│ trial_29368    PENDING               291             5                    51                   14   0.3                         477 │
│ trial_29368    PENDING               497             7                    31                   22   sqrt                       9854 │
│ trial_29368    PENDING               382             5                    22                   22   sqrt                       2356 │
│ trial_29368    PENDING               396             5                    41                   20   0.3                        2993 │
│ trial_29368    PENDING               257             5                    52                   17   0.3                        7620 │
│ trial_29368    PENDING               375             6                    37                   29   sqrt                       2382 │
│ trial_29368    PENDING               485             7                    31                   28   sqrt                       9271 │
│ trial_29368    PENDING               266             7                    40                   12   0.3                        1336 │
│ trial_29368    PENDING               310             7                    48                   26   0.3                        3903 │
│ trial_29368    PENDING               383             5                    50                   12   0.3                        2472 │
│ trial_29368    PENDING               351             7                    40                   26   0.3                         133 │
│ trial_29368    PENDING               291             7                    37                   28   sqrt                       1369 │
│ trial_29368    PENDING               489             5                    24                   29   0.3                        3261 │
│ trial_29368    PENDING               337             5                    21                   29   0.3                        6167 │
│ trial_29368    PENDING               226             5                    26                   15   sqrt                       6081 │
│ trial_29368    PENDING               221             7                    51                   26   sqrt                       9314 │
│ trial_29368    PENDING               266             7                    59                   13   sqrt                       5692 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_29368 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29368 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               29 │
│ min_samples_split              37 │
│ n_estimators                  375 │
│ random_state                 2382 │
╰───────────────────────────────────╯
Trial trial_29368 started with configuration:
╭──────────────────────────────────╮
│ Trial trial_29368 config         │
├──────────────────────────────────┤
│ max_depth                      5 │
│ max_features                 0.3 │
│ min_samples_leaf              14 │
│ min_samples_split             51 │
│ n_estimators                 291 │
│ random_state                 477 │
╰──────────────────────────────────╯
Trial trial_29368 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29368 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               12 │
│ min_samples_split              40 │
│ n_estimators                  266 │
│ random_state                 1336 │
╰───────────────────────────────────╯
Trial trial_29368 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29368 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               26 │
│ min_samples_split              51 │
│ n_estimators                  221 │
│ random_state                 9314 │
╰───────────────────────────────────╯
Trial trial_29368 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29368 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               20 │
│ min_samples_split              41 │
│ n_estimators                  396 │
│ random_state                 2993 │
╰───────────────────────────────────╯
Trial trial_29368 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29368 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               14 │
│ min_samples_split              45 │
│ n_estimators                  341 │
│ random_state                 7546 │
╰───────────────────────────────────╯
Trial trial_29368 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29368 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               17 │
│ min_samples_split              52 │
│ n_estimators                  257 │
│ random_state                 7620 │
╰───────────────────────────────────╯
Trial trial_29368 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29368 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               26 │
│ min_samples_split              48 │
│ n_estimators                  310 │
│ random_state                 3903 │
╰───────────────────────────────────╯
Trial trial_29368 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29368 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               22 │
│ min_samples_split              27 │
│ n_estimators                  424 │
│ random_state                 9030 │
╰───────────────────────────────────╯
Trial trial_29368 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29368 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               28 │
│ min_samples_split              37 │
│ n_estimators                  291 │
│ random_state                 1369 │
╰───────────────────────────────────╯
Trial trial_29368 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29368 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               22 │
│ min_samples_split              31 │
│ n_estimators                  497 │
│ random_state                 9854 │
╰───────────────────────────────────╯
Trial trial_29368 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29368 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               29 │
│ min_samples_split              21 │
│ n_estimators                  337 │
│ random_state                 6167 │
╰───────────────────────────────────╯
Trial trial_29368 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29368 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               22 │
│ min_samples_split              22 │
│ n_estimators                  382 │
│ random_state                 2356 │
╰───────────────────────────────────╯
Trial trial_29368 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29368 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               12 │
│ min_samples_split              50 │
│ n_estimators                  383 │
│ random_state                 2472 │
╰───────────────────────────────────╯
Trial trial_29368 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29368 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               13 │
│ min_samples_split              40 │
│ n_estimators                  362 │
│ random_state                 9553 │
╰───────────────────────────────────╯
Trial trial_29368 started with configuration:
[36m(train_brf_ray_tune pid=3768605)[0m 2025-10-26 20:15:43.698193: 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=3768605)[0m 2025-10-26 20:15:43.719850: 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=3768605)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_brf_ray_tune pid=3768605)[0m E0000 00:00:1761506143.748768 3769743 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=3768605)[0m E0000 00:00:1761506143.757350 3769743 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=3768605)[0m W0000 00:00:1761506143.778499 3769743 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=3768605)[0m W0000 00:00:1761506143.778527 3769743 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=3768605)[0m W0000 00:00:1761506143.778530 3769743 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=3768605)[0m W0000 00:00:1761506143.778533 3769743 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=3768605)[0m 2025-10-26 20:15:43.784596: 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=3768605)[0m To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
[36m(train_brf_ray_tune pid=3768605)[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=3768605)[0m   return fit_method(estimator, *args, **kwargs)
[36m(train_brf_ray_tune pid=3768607)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=3768615)[0m 2025-10-26 20:15:44.240146: 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=3768615)[0m 2025-10-26 20:15:44.262564: 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=3768615)[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=3768615)[0m E0000 00:00:1761506144.291065 3769876 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=3768615)[0m E0000 00:00:1761506144.299384 3769876 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=3768615)[0m W0000 00:00:1761506144.320551 3769876 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=3768615)[0m 2025-10-26 20:15:44.327097: 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=3768615)[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=3768611)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    2.3s
[36m(train_brf_ray_tune pid=3768595)[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=3768595)[0m   return fit_method(estimator, *args, **kwargs)[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=3768571)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=3768605)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    8.0s[32m [repeated 7x across cluster][0m
[36m(train_brf_ray_tune pid=3768614)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:   12.7s[32m [repeated 11x across cluster][0m
[36m(train_brf_ray_tune pid=3768611)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   30.2s[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=3768584)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   36.7s[32m [repeated 3x across cluster][0m
[36m(train_brf_ray_tune pid=3768615)[0m [Parallel(n_jobs=-1)]: Done 226 out of 226 | elapsed:   40.7s finished
[36m(train_brf_ray_tune pid=3768595)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   38.6s[32m [repeated 6x across cluster][0m
[36m(train_brf_ray_tune pid=3768615)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=3768615)[0m [Parallel(n_jobs=20)]: Done 226 out of 226 | elapsed:    0.9s finished
[36m(train_brf_ray_tune pid=3768615)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
╭──────────────────────────────────╮
│ Trial trial_29368 config         │
├──────────────────────────────────┤
│ max_depth                      7 │
│ max_features                 0.3 │
│ min_samples_leaf              26 │
│ min_samples_split             40 │
│ n_estimators                 351 │
│ random_state                 133 │
╰──────────────────────────────────╯
Trial trial_29368 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29368 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               13 │
│ min_samples_split              59 │
│ n_estimators                  266 │
│ random_state                 5692 │
╰───────────────────────────────────╯
Trial trial_29368 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29368 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               28 │
│ min_samples_split              31 │
│ n_estimators                  485 │
│ random_state                 9271 │
╰───────────────────────────────────╯
Trial trial_29368 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29368 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               29 │
│ min_samples_split              24 │
│ n_estimators                  489 │
│ random_state                 3261 │
╰───────────────────────────────────╯
Trial trial_29368 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_29368 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               15 │
│ min_samples_split              26 │
│ n_estimators                  226 │
│ random_state                 6081 │
╰───────────────────────────────────╯

Trial status: 20 RUNNING
Current time: 2025-10-26 20:16:10. 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_29368    RUNNING               424             7                    27                   22   sqrt                       9030 │
│ trial_29368    RUNNING               341             5                    45                   14   0.3                        7546 │
│ trial_29368    RUNNING               362             6                    40                   13   sqrt                       9553 │
│ trial_29368    RUNNING               291             5                    51                   14   0.3                         477 │
│ trial_29368    RUNNING               497             7                    31                   22   sqrt                       9854 │
│ trial_29368    RUNNING               382             5                    22                   22   sqrt                       2356 │
│ trial_29368    RUNNING               396             5                    41                   20   0.3                        2993 │
│ trial_29368    RUNNING               257             5                    52                   17   0.3                        7620 │
│ trial_29368    RUNNING               375             6                    37                   29   sqrt                       2382 │
│ trial_29368    RUNNING               485             7                    31                   28   sqrt                       9271 │
│ trial_29368    RUNNING               266             7                    40                   12   0.3                        1336 │
│ trial_29368    RUNNING               310             7                    48                   26   0.3                        3903 │
│ trial_29368    RUNNING               383             5                    50                   12   0.3                        2472 │
│ trial_29368    RUNNING               351             7                    40                   26   0.3                         133 │
│ trial_29368    RUNNING               291             7                    37                   28   sqrt                       1369 │
│ trial_29368    RUNNING               489             5                    24                   29   0.3                        3261 │
│ trial_29368    RUNNING               337             5                    21                   29   0.3                        6167 │
│ trial_29368    RUNNING               226             5                    26                   15   sqrt                       6081 │
│ trial_29368    RUNNING               221             7                    51                   26   sqrt                       9314 │
│ trial_29368    RUNNING               266             7                    59                   13   sqrt                       5692 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_29368 finished iteration 1 at 2025-10-26 20:16:35. Total running time: 55s
[36m(train_brf_ray_tune pid=3768615)[0m [Parallel(n_jobs=20)]: Done 226 out of 226 | elapsed:    0.9s finished
[36m(train_brf_ray_tune pid=3768615)[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=3768615)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=3768597)[0m [Parallel(n_jobs=-1)]: Done 221 out of 221 | elapsed:   51.9s finished
[36m(train_brf_ray_tune pid=3768615)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.8s[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=3768597)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=3768597)[0m [Parallel(n_jobs=20)]: Done 221 out of 221 | elapsed:    0.7s finished
[36m(train_brf_ray_tune pid=3768597)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=3768597)[0m [Parallel(n_jobs=20)]: Done 221 out of 221 | elapsed:    0.4s finished
[36m(train_brf_ray_tune pid=3768597)[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=3768597)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=3768597)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.3s[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=3768595)[0m [Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:  1.0min finished
[36m(train_brf_ray_tune pid=3768610)[0m [Parallel(n_jobs=-1)]: Done 291 out of 291 | elapsed:  1.1min finished
[36m(train_brf_ray_tune pid=3768595)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=3768595)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=3768595)[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=3768595)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=3768610)[0m [Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.3s[32m [repeated 7x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             51.8276 │
│ time_total_s                 51.8276 │
│ training_iteration                 1 │
│ test_accuracy                0.46511 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:16:35. Total running time: 55s

Trial status: 19 RUNNING | 1 TERMINATED
Current time: 2025-10-26 20:16:40. Total running time: 1min 0s
Logical resource usage: 19.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         n_estimators     max_depth     min_samples_split     min_samples_leaf   max_features       random_state     iter     total time (s)     test_accuracy │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_29368    RUNNING                 424             7                    27                   22   sqrt                       9030                                               │
│ trial_29368    RUNNING                 341             5                    45                   14   0.3                        7546                                               │
│ trial_29368    RUNNING                 362             6                    40                   13   sqrt                       9553                                               │
│ trial_29368    RUNNING                 291             5                    51                   14   0.3                         477                                               │
│ trial_29368    RUNNING                 497             7                    31                   22   sqrt                       9854                                               │
│ trial_29368    RUNNING                 382             5                    22                   22   sqrt                       2356                                               │
│ trial_29368    RUNNING                 396             5                    41                   20   0.3                        2993                                               │
│ trial_29368    RUNNING                 257             5                    52                   17   0.3                        7620                                               │
│ trial_29368    RUNNING                 375             6                    37                   29   sqrt                       2382                                               │
│ trial_29368    RUNNING                 485             7                    31                   28   sqrt                       9271                                               │
│ trial_29368    RUNNING                 266             7                    40                   12   0.3                        1336                                               │
│ trial_29368    RUNNING                 310             7                    48                   26   0.3                        3903                                               │
│ trial_29368    RUNNING                 383             5                    50                   12   0.3                        2472                                               │
│ trial_29368    RUNNING                 351             7                    40                   26   0.3                         133                                               │
│ trial_29368    RUNNING                 291             7                    37                   28   sqrt                       1369                                               │
│ trial_29368    RUNNING                 489             5                    24                   29   0.3                        3261                                               │
│ trial_29368    RUNNING                 337             5                    21                   29   0.3                        6167                                               │
│ trial_29368    RUNNING                 221             7                    51                   26   sqrt                       9314                                               │
│ trial_29368    RUNNING                 266             7                    59                   13   sqrt                       5692                                               │
│ trial_29368    TERMINATED              226             5                    26                   15   sqrt                       6081        1            51.8276          0.465108 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_29368 finished iteration 1 at 2025-10-26 20:16:42. Total running time: 1min 2s
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             59.2091 │
│ time_total_s                 59.2091 │
│ training_iteration                 1 │
│ test_accuracy                0.48666 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:16:42. Total running time: 1min 2s

Trial trial_29368 finished iteration 1 at 2025-10-26 20:16:55. Total running time: 1min 15s
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             72.2827 │
│ time_total_s                 72.2827 │
│ training_iteration                 1 │
│ test_accuracy                0.50055 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:16:56. Total running time: 1min 15s

[36m(train_brf_ray_tune pid=3768610)[0m [Parallel(n_jobs=20)]: Done 291 out of 291 | elapsed:    0.5s finished[32m [repeated 6x across cluster][0m
[36m(train_brf_ray_tune pid=3768596)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 6x across cluster][0m
[36m(train_brf_ray_tune pid=3768596)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 3x across cluster][0m
[36m(train_brf_ray_tune pid=3768596)[0m   _log_deprecation_warning([32m [repeated 3x across cluster][0m
[36m(train_brf_ray_tune pid=3768571)[0m [Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.5s[32m [repeated 14x across cluster][0m
[36m(train_brf_ray_tune pid=3768571)[0m [Parallel(n_jobs=20)]: Done 362 out of 362 | elapsed:    1.1s finished[32m [repeated 6x across cluster][0m
[36m(train_brf_ray_tune pid=3768571)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=3768571)[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=3768571)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=3768606)[0m [Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:  1.3min[32m [repeated 7x across cluster][0m
[36m(train_brf_ray_tune pid=3768584)[0m [Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:  1.4min finished[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=3768584)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=3768584)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.7s[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=3768584)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=3768584)[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=3768584)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=3768584)[0m [Parallel(n_jobs=20)]: Done 424 out of 424 | elapsed:    1.4s finished[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=3768613)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:  1.5min[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=3768578)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=3768578)[0m [Parallel(n_jobs=-1)]: Done 485 out of 485 | elapsed:  1.5min finished[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=3768606)[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=3768606)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=3768578)[0m [Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.8s[32m [repeated 12x across cluster][0m
[36m(train_brf_ray_tune pid=3768609)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 5x across cluster][0m
[36m(train_brf_ray_tune pid=3768609)[0m [Parallel(n_jobs=20)]: Done 257 out of 257 | elapsed:    0.7s finished[32m [repeated 6x across cluster][0m
Trial trial_29368 finished iteration 1 at 2025-10-26 20:16:56. Total running time: 1min 16s
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             73.3638 │
│ time_total_s                 73.3638 │
│ training_iteration                 1 │
│ test_accuracy                0.48995 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:16:57. Total running time: 1min 16s

Trial trial_29368 finished iteration 1 at 2025-10-26 20:16:59. Total running time: 1min 18s
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             75.7815 │
│ time_total_s                 75.7815 │
│ training_iteration                 1 │
│ test_accuracy                0.48776 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:16:59. Total running time: 1min 18s

Trial trial_29368 finished iteration 1 at 2025-10-26 20:17:00. Total running time: 1min 19s
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             76.5732 │
│ time_total_s                 76.5732 │
│ training_iteration                 1 │
│ test_accuracy                0.47315 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:17:00. Total running time: 1min 19s

Trial trial_29368 finished iteration 1 at 2025-10-26 20:17:03. Total running time: 1min 23s
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             80.1939 │
│ time_total_s                 80.1939 │
│ training_iteration                 1 │
│ test_accuracy                0.48886 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:17:03. Total running time: 1min 23s

Trial status: 13 RUNNING | 7 TERMINATED
Current time: 2025-10-26 20:17:10. Total running time: 1min 30s
Logical resource usage: 13.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         n_estimators     max_depth     min_samples_split     min_samples_leaf   max_features       random_state     iter     total time (s)     test_accuracy │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_29368    RUNNING                 424             7                    27                   22   sqrt                       9030                                               │
│ trial_29368    RUNNING                 341             5                    45                   14   0.3                        7546                                               │
│ trial_29368    RUNNING                 291             5                    51                   14   0.3                         477                                               │
│ trial_29368    RUNNING                 497             7                    31                   22   sqrt                       9854                                               │
│ trial_29368    RUNNING                 396             5                    41                   20   0.3                        2993                                               │
│ trial_29368    RUNNING                 257             5                    52                   17   0.3                        7620                                               │
│ trial_29368    RUNNING                 485             7                    31                   28   sqrt                       9271                                               │
│ trial_29368    RUNNING                 266             7                    40                   12   0.3                        1336                                               │
│ trial_29368    RUNNING                 310             7                    48                   26   0.3                        3903                                               │
│ trial_29368    RUNNING                 383             5                    50                   12   0.3                        2472                                               │
│ trial_29368    RUNNING                 351             7                    40                   26   0.3                         133                                               │
│ trial_29368    RUNNING                 489             5                    24                   29   0.3                        3261                                               │
│ trial_29368    RUNNING                 337             5                    21                   29   0.3                        6167                                               │
│ trial_29368    TERMINATED              362             6                    40                   13   sqrt                       9553        1            80.1939          0.488856 │
│ trial_29368    TERMINATED              382             5                    22                   22   sqrt                       2356        1            76.5732          0.473146 │
│ trial_29368    TERMINATED              375             6                    37                   29   sqrt                       2382        1            75.7815          0.48776  │
│ trial_29368    TERMINATED              291             7                    37                   28   sqrt                       1369        1            73.3639          0.489953 │
│ trial_29368    TERMINATED              226             5                    26                   15   sqrt                       6081        1            51.8276          0.465108 │
│ trial_29368    TERMINATED              221             7                    51                   26   sqrt                       9314        1            59.2091          0.486664 │
│ trial_29368    TERMINATED              266             7                    59                   13   sqrt                       5692        1            72.2827          0.500548 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_29368 finished iteration 1 at 2025-10-26 20:17:14. Total running time: 1min 33s
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             90.8003 │
│ time_total_s                 90.8003 │
│ training_iteration                 1 │
│ test_accuracy                0.48484 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:17:14. Total running time: 1min 33s

Trial trial_29368 finished iteration 1 at 2025-10-26 20:17:22. Total running time: 1min 41s
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             98.6901 │
│ time_total_s                 98.6901 │
│ training_iteration                 1 │
│ test_accuracy                0.49251 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:17:22. Total running time: 1min 41s

Trial trial_29368 finished iteration 1 at 2025-10-26 20:17:22. Total running time: 1min 42s
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             98.9597 │
│ time_total_s                 98.9597 │
│ training_iteration                 1 │
│ test_accuracy                0.48776 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:17:22. Total running time: 1min 42s

Trial trial_29368 finished iteration 1 at 2025-10-26 20:17:25. Total running time: 1min 45s
[36m(train_brf_ray_tune pid=3768609)[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=3768609)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=3768609)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.5s[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=3768605)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=3768605)[0m [Parallel(n_jobs=20)]: Done 291 out of 291 | elapsed:    0.5s finished[32m [repeated 3x across cluster][0m
[36m(train_brf_ray_tune pid=3768605)[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=3768605)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=3768605)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.3s[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=3768569)[0m [Parallel(n_jobs=-1)]: Done 341 out of 341 | elapsed:  1.8min finished[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=3768569)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=3768604)[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=3768604)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             102.531 │
│ time_total_s                 102.531 │
│ training_iteration                 1 │
│ test_accuracy                0.45342 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:17:25. Total running time: 1min 45s

Trial trial_29368 finished iteration 1 at 2025-10-26 20:17:31. Total running time: 1min 50s
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             107.817 │
│ time_total_s                 107.817 │
│ training_iteration                 1 │
│ test_accuracy                 0.4483 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:17:31. Total running time: 1min 50s

Trial trial_29368 finished iteration 1 at 2025-10-26 20:17:38. Total running time: 1min 57s
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             114.731 │
│ time_total_s                 114.731 │
│ training_iteration                 1 │
│ test_accuracy                0.44136 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:17:38. Total running time: 1min 57s

Trial trial_29368 finished iteration 1 at 2025-10-26 20:17:38. Total running time: 1min 57s
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             114.982 │
│ time_total_s                 114.982 │
│ training_iteration                 1 │
│ test_accuracy                0.45049 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:17:38. Total running time: 1min 57s

Trial trial_29368 finished iteration 1 at 2025-10-26 20:17:39. Total running time: 1min 58s
╭─────────────────────────────────────╮
│ Trial trial_29368 result            │
├─────────────────────────────────────┤
│ checkpoint_dir_name                 │
│ time_this_iter_s             115.93 │
│ time_total_s                 115.93 │
│ training_iteration                1 │
│ test_accuracy                0.4947 │
╰─────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:17:39. Total running time: 1min 58s

Trial status: 15 TERMINATED | 5 RUNNING
Current time: 2025-10-26 20:17:40. Total running time: 2min 0s
Logical resource usage: 5.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
[36m(train_brf_ray_tune pid=3768607)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.2s[32m [repeated 12x across cluster][0m
[36m(train_brf_ray_tune pid=3768612)[0m [Parallel(n_jobs=-1)]: Done 383 out of 383 | elapsed:  1.9min finished[32m [repeated 9x across cluster][0m
[36m(train_brf_ray_tune pid=3768607)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 5x across cluster][0m
[36m(train_brf_ray_tune pid=3768607)[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=3768607)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         n_estimators     max_depth     min_samples_split     min_samples_leaf   max_features       random_state     iter     total time (s)     test_accuracy │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_29368    RUNNING                 396             5                    41                   20   0.3                        2993                                               │
│ trial_29368    RUNNING                 310             7                    48                   26   0.3                        3903                                               │
│ trial_29368    RUNNING                 383             5                    50                   12   0.3                        2472                                               │
│ trial_29368    RUNNING                 351             7                    40                   26   0.3                         133                                               │
│ trial_29368    RUNNING                 489             5                    24                   29   0.3                        3261                                               │
│ trial_29368    TERMINATED              424             7                    27                   22   sqrt                       9030        1            90.8003          0.484837 │
│ trial_29368    TERMINATED              341             5                    45                   14   0.3                        7546        1           114.982           0.450493 │
│ trial_29368    TERMINATED              362             6                    40                   13   sqrt                       9553        1            80.1939          0.488856 │
│ trial_29368    TERMINATED              291             5                    51                   14   0.3                         477        1           107.817           0.448301 │
│ trial_29368    TERMINATED              497             7                    31                   22   sqrt                       9854        1            98.6901          0.49251  │
│ trial_29368    TERMINATED              382             5                    22                   22   sqrt                       2356        1            76.5732          0.473146 │
│ trial_29368    TERMINATED              257             5                    52                   17   0.3                        7620        1           102.531           0.453416 │
│ trial_29368    TERMINATED              375             6                    37                   29   sqrt                       2382        1            75.7815          0.48776  │
│ trial_29368    TERMINATED              485             7                    31                   28   sqrt                       9271        1            98.9597          0.48776  │
│ trial_29368    TERMINATED              266             7                    40                   12   0.3                        1336        1           115.93            0.494702 │
│ trial_29368    TERMINATED              291             7                    37                   28   sqrt                       1369        1            73.3639          0.489953 │
│ trial_29368    TERMINATED              337             5                    21                   29   0.3                        6167        1           114.731           0.441359 │
│ trial_29368    TERMINATED              226             5                    26                   15   sqrt                       6081        1            51.8276          0.465108 │
│ trial_29368    TERMINATED              221             7                    51                   26   sqrt                       9314        1            59.2091          0.486664 │
│ trial_29368    TERMINATED              266             7                    59                   13   sqrt                       5692        1            72.2827          0.500548 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_29368 finished iteration 1 at 2025-10-26 20:17:43. Total running time: 2min 3s
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s               120.2 │
│ time_total_s                   120.2 │
│ training_iteration                 1 │
│ test_accuracy                0.45195 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:17:43. Total running time: 2min 3s

Trial trial_29368 finished iteration 1 at 2025-10-26 20:17:43. Total running time: 2min 3s
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             120.113 │
│ time_total_s                 120.113 │
│ training_iteration                 1 │
│ test_accuracy                 0.4589 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:17:43. Total running time: 2min 3s

Trial trial_29368 finished iteration 1 at 2025-10-26 20:17:45. Total running time: 2min 4s
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             121.656 │
│ time_total_s                 121.656 │
│ training_iteration                 1 │
│ test_accuracy                0.48557 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:17:45. Total running time: 2min 4s

Trial trial_29368 finished iteration 1 at 2025-10-26 20:17:45. Total running time: 2min 4s
2025-10-26 20:17:45,859	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_17_classes/BalancedRF_hyperparameters_tuning' in 0.0091s.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.8s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             121.877 │
│ time_total_s                 121.877 │
│ training_iteration                 1 │
│ test_accuracy                0.49178 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:17:45. Total running time: 2min 5s

Trial trial_29368 finished iteration 1 at 2025-10-26 20:17:45. Total running time: 2min 5s
╭──────────────────────────────────────╮
│ Trial trial_29368 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             122.209 │
│ time_total_s                 122.209 │
│ training_iteration                 1 │
│ test_accuracy                0.44099 │
╰──────────────────────────────────────╯

Trial trial_29368 completed after 1 iterations at 2025-10-26 20:17:45. Total running time: 2min 5s

Trial status: 20 TERMINATED
Current time: 2025-10-26 20:17:45. Total running time: 2min 5s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         n_estimators     max_depth     min_samples_split     min_samples_leaf   max_features       random_state     iter     total time (s)     test_accuracy │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_29368    TERMINATED              424             7                    27                   22   sqrt                       9030        1            90.8003          0.484837 │
│ trial_29368    TERMINATED              341             5                    45                   14   0.3                        7546        1           114.982           0.450493 │
│ trial_29368    TERMINATED              362             6                    40                   13   sqrt                       9553        1            80.1939          0.488856 │
│ trial_29368    TERMINATED              291             5                    51                   14   0.3                         477        1           107.817           0.448301 │
│ trial_29368    TERMINATED              497             7                    31                   22   sqrt                       9854        1            98.6901          0.49251  │
│ trial_29368    TERMINATED              382             5                    22                   22   sqrt                       2356        1            76.5732          0.473146 │
│ trial_29368    TERMINATED              396             5                    41                   20   0.3                        2993        1           120.2             0.451955 │
│ trial_29368    TERMINATED              257             5                    52                   17   0.3                        7620        1           102.531           0.453416 │
│ trial_29368    TERMINATED              375             6                    37                   29   sqrt                       2382        1            75.7815          0.48776  │
│ trial_29368    TERMINATED              485             7                    31                   28   sqrt                       9271        1            98.9597          0.48776  │
│ trial_29368    TERMINATED              266             7                    40                   12   0.3                        1336        1           115.93            0.494702 │
│ trial_29368    TERMINATED              310             7                    48                   26   0.3                        3903        1           121.656           0.485568 │
│ trial_29368    TERMINATED              383             5                    50                   12   0.3                        2472        1           120.113           0.458897 │
│ trial_29368    TERMINATED              351             7                    40                   26   0.3                         133        1           121.877           0.491779 │
│ trial_29368    TERMINATED              291             7                    37                   28   sqrt                       1369        1            73.3639          0.489953 │
│ trial_29368    TERMINATED              489             5                    24                   29   0.3                        3261        1           122.209           0.440994 │
│ trial_29368    TERMINATED              337             5                    21                   29   0.3                        6167        1           114.731           0.441359 │
│ trial_29368    TERMINATED              226             5                    26                   15   sqrt                       6081        1            51.8276          0.465108 │
│ trial_29368    TERMINATED              221             7                    51                   26   sqrt                       9314        1            59.2091          0.486664 │
│ trial_29368    TERMINATED              266             7                    59                   13   sqrt                       5692        1            72.2827          0.500548 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'n_estimators': 266, 'max_depth': 7, 'min_samples_split': 59, 'min_samples_leaf': 13, 'max_features': 'sqrt', 'random_state': 5692}
Saved model to disk
[36m(train_brf_ray_tune pid=3768603)[0m [Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s[32m [repeated 23x across cluster][0m
[36m(train_brf_ray_tune pid=3768603)[0m [Parallel(n_jobs=20)]: Done 489 out of 489 | elapsed:    0.1s finished[32m [repeated 14x across cluster][0m
[36m(train_brf_ray_tune pid=3768603)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 10x across cluster][0m
[36m(train_brf_ray_tune pid=3768603)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 5x across cluster][0m
[36m(train_brf_ray_tune pid=3768603)[0m   _log_deprecation_warning([32m [repeated 5x across cluster][0m
2025-10-26 20:18:21.574923: 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`.
2025-10-26 20:18:21.587366: 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 00:00:1761506301.600880 3774180 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761506301.605238 3774180 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761506301.616104 3774180 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506301.616128 3774180 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506301.616139 3774180 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506301.616141 3774180 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:18:21.619525: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.8s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:19:21.862880: 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`.
2025-10-26 20:19:21.875417: 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 00:00:1761506361.888784 3775387 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761506361.894382 3775387 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761506361.905352 3775387 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506361.905372 3775387 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506361.905375 3775387 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506361.905378 3775387 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:19:21.908734: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:20:21.490702: 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`.
2025-10-26 20:20:21.503315: 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 00:00:1761506421.516861 3776586 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761506421.521304 3776586 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761506421.532188 3776586 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506421.532216 3776586 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506421.532220 3776586 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506421.532222 3776586 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:20:21.535585: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.8s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk

=== EJECUCIÓN 1 ===

--- TRAIN (ejecución 1) ---

--- TEST (ejecución 1) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.66 [%]
Global accuracy score (test) = 49.91 [%]
Global F1 score (train) = 66.85 [%]
Global F1 score (test) = 49.34 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.28      0.49      0.36       184
 CAMINAR CON MÓVIL O LIBRO       0.45      0.51      0.47       184
       CAMINAR USUAL SPEED       0.23      0.05      0.08       184
            CAMINAR ZIGZAG       0.61      0.52      0.56       184
          DE PIE BARRIENDO       0.52      0.57      0.54       184
   DE PIE DOBLANDO TOALLAS       0.42      0.34      0.37       184
    DE PIE MOVIENDO LIBROS       0.45      0.51      0.48       184
          DE PIE USANDO PC       0.51      0.68      0.58       184
        FASE REPOSO CON K5       0.77      0.77      0.77       184
INCREMENTAL CICLOERGOMETRO       0.82      0.67      0.74       184
           SENTADO LEYENDO       0.40      0.38      0.39       184
         SENTADO USANDO PC       0.26      0.21      0.23       184
      SENTADO VIENDO LA TV       0.52      0.37      0.43       184
   SUBIR Y BAJAR ESCALERAS       0.43      0.65      0.52       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.50      2737
                 macro avg       0.51      0.50      0.49      2737
              weighted avg       0.50      0.50      0.49      2737


Accuracy capturado en la ejecución 1: 49.91 [%]
F1-score capturado en la ejecución 1: 49.34 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.19 [%]
Global accuracy score (test) = 49.43 [%]
Global F1 score (train) = 66.35 [%]
Global F1 score (test) = 48.98 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.31      0.47      0.37       184
 CAMINAR CON MÓVIL O LIBRO       0.44      0.42      0.43       184
       CAMINAR USUAL SPEED       0.32      0.10      0.16       184
            CAMINAR ZIGZAG       0.58      0.54      0.56       184
          DE PIE BARRIENDO       0.52      0.54      0.53       184
   DE PIE DOBLANDO TOALLAS       0.37      0.31      0.34       184
    DE PIE MOVIENDO LIBROS       0.43      0.49      0.46       184
          DE PIE USANDO PC       0.49      0.69      0.58       184
        FASE REPOSO CON K5       0.77      0.75      0.76       184
INCREMENTAL CICLOERGOMETRO       0.83      0.68      0.75       184
           SENTADO LEYENDO       0.38      0.34      0.36       184
         SENTADO USANDO PC       0.28      0.20      0.23       184
      SENTADO VIENDO LA TV       0.49      0.40      0.44       184
   SUBIR Y BAJAR ESCALERAS       0.40      0.69      0.51       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.49      2737
                 macro avg       0.50      0.50      0.49      2737
              weighted avg       0.50      0.49      0.49      2737


Accuracy capturado en la ejecución 2: 49.43 [%]
F1-score capturado en la ejecución 2: 48.98 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.47 [%]
Global accuracy score (test) = 49.0 [%]
Global F1 score (train) = 66.58 [%]
Global F1 score (test) = 48.58 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.26      0.45      0.33       184
 CAMINAR CON MÓVIL O LIBRO       0.45      0.44      0.44       184
       CAMINAR USUAL SPEED       0.20      0.06      0.09       184
            CAMINAR ZIGZAG       0.62      0.51      0.56       184
          DE PIE BARRIENDO       0.54      0.58      0.56       184
   DE PIE DOBLANDO TOALLAS       0.43      0.35      0.39       184
    DE PIE MOVIENDO LIBROS       0.44      0.51      0.47       184
          DE PIE USANDO PC       0.50      0.67      0.57       184
        FASE REPOSO CON K5       0.78      0.78      0.78       184
INCREMENTAL CICLOERGOMETRO       0.82      0.67      0.74       184
           SENTADO LEYENDO       0.35      0.32      0.33       184
         SENTADO USANDO PC       0.27      0.20      0.23       184
      SENTADO VIENDO LA TV       0.49      0.39      0.43       184
   SUBIR Y BAJAR ESCALERAS       0.40      0.66      0.50       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.49      2737
                 macro avg       0.50      0.49      0.49      2737
              weighted avg       0.49      0.49      0.48      2737


Accuracy capturado en la ejecución 3: 49.0 [%]
F1-score capturado en la ejecución 3: 48.58 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
2025-10-26 20:21:21.899932: 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`.
2025-10-26 20:21:21.912590: 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 00:00:1761506481.926206 3777775 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761506481.930567 3777775 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761506481.941417 3777775 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506481.941440 3777775 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506481.941443 3777775 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506481.941445 3777775 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:21:21.944830: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:22:22.884056: 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`.
2025-10-26 20:22:22.896680: 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 00:00:1761506542.910499 3778962 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761506542.914924 3778962 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761506542.926205 3778962 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506542.926231 3778962 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506542.926235 3778962 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506542.926238 3778962 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:22:22.929653: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:23:24.228749: 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`.
2025-10-26 20:23:24.241287: 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 00:00:1761506604.254969 3780161 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761506604.259310 3780161 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761506604.270328 3780161 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506604.270353 3780161 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506604.270356 3780161 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506604.270359 3780161 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:23:24.273718: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.31 [%]
Global accuracy score (test) = 49.62 [%]
Global F1 score (train) = 66.47 [%]
Global F1 score (test) = 49.21 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.46      0.34       184
 CAMINAR CON MÓVIL O LIBRO       0.43      0.45      0.44       184
       CAMINAR USUAL SPEED       0.34      0.08      0.13       184
            CAMINAR ZIGZAG       0.61      0.53      0.57       184
          DE PIE BARRIENDO       0.54      0.56      0.55       184
   DE PIE DOBLANDO TOALLAS       0.40      0.34      0.37       184
    DE PIE MOVIENDO LIBROS       0.43      0.49      0.46       184
          DE PIE USANDO PC       0.48      0.67      0.56       184
        FASE REPOSO CON K5       0.77      0.76      0.77       184
INCREMENTAL CICLOERGOMETRO       0.83      0.68      0.75       184
           SENTADO LEYENDO       0.39      0.34      0.37       184
         SENTADO USANDO PC       0.28      0.20      0.23       184
      SENTADO VIENDO LA TV       0.50      0.42      0.46       184
   SUBIR Y BAJAR ESCALERAS       0.41      0.68      0.52       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.50      2737
                 macro avg       0.51      0.50      0.49      2737
              weighted avg       0.51      0.50      0.49      2737


Accuracy capturado en la ejecución 4: 49.62 [%]
F1-score capturado en la ejecución 4: 49.21 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.68 [%]
Global accuracy score (test) = 48.85 [%]
Global F1 score (train) = 66.89 [%]
Global F1 score (test) = 48.3 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.26      0.49      0.34       184
 CAMINAR CON MÓVIL O LIBRO       0.43      0.43      0.43       184
       CAMINAR USUAL SPEED       0.30      0.04      0.08       184
            CAMINAR ZIGZAG       0.60      0.53      0.56       184
          DE PIE BARRIENDO       0.52      0.56      0.54       184
   DE PIE DOBLANDO TOALLAS       0.37      0.32      0.35       184
    DE PIE MOVIENDO LIBROS       0.45      0.49      0.47       184
          DE PIE USANDO PC       0.49      0.67      0.56       184
        FASE REPOSO CON K5       0.77      0.76      0.76       184
INCREMENTAL CICLOERGOMETRO       0.83      0.68      0.75       184
           SENTADO LEYENDO       0.36      0.33      0.34       184
         SENTADO USANDO PC       0.29      0.22      0.25       184
      SENTADO VIENDO LA TV       0.49      0.39      0.43       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.65      0.51       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.49      2737
                 macro avg       0.50      0.49      0.48      2737
              weighted avg       0.50      0.49      0.48      2737


Accuracy capturado en la ejecución 5: 48.85 [%]
F1-score capturado en la ejecución 5: 48.3 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.4 [%]
Global accuracy score (test) = 49.07 [%]
Global F1 score (train) = 66.55 [%]
Global F1 score (test) = 48.62 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.45      0.30       184
 CAMINAR CON MÓVIL O LIBRO       0.42      0.44      0.43       184
       CAMINAR USUAL SPEED       0.14      0.02      0.04       184
            CAMINAR ZIGZAG       0.56      0.54      0.55       184
          DE PIE BARRIENDO       0.52      0.58      0.55       184
   DE PIE DOBLANDO TOALLAS       0.41      0.32      0.36       184
    DE PIE MOVIENDO LIBROS       0.43      0.51      0.47       184
          DE PIE USANDO PC       0.53      0.67      0.59       184
        FASE REPOSO CON K5       0.78      0.79      0.79       184
INCREMENTAL CICLOERGOMETRO       0.82      0.68      0.74       184
           SENTADO LEYENDO       0.38      0.34      0.36       184
         SENTADO USANDO PC       0.28      0.22      0.25       184
      SENTADO VIENDO LA TV       0.52      0.42      0.47       184
   SUBIR Y BAJAR ESCALERAS       0.48      0.59      0.53       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.49      2737
                 macro avg       0.50      0.49      0.49      2737
              weighted avg       0.49      0.49      0.48      2737


Accuracy capturado en la ejecución 6: 49.07 [%]
F1-score capturado en la ejecución 6: 48.62 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
2025-10-26 20:24:25.098090: 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`.
2025-10-26 20:24:25.110814: 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 00:00:1761506665.125092 3781369 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761506665.129604 3781369 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761506665.140966 3781369 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506665.140989 3781369 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506665.140991 3781369 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506665.140993 3781369 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:24:25.144421: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.8s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:25:25.579589: 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`.
2025-10-26 20:25:25.592132: 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 00:00:1761506725.605834 3782588 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761506725.610077 3782588 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761506725.621585 3782588 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506725.621607 3782588 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506725.621610 3782588 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506725.621612 3782588 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:25:25.625093: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:26:25.902304: 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`.
2025-10-26 20:26:25.914838: 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 00:00:1761506785.928516 3783772 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761506785.932897 3783772 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761506785.943922 3783772 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506785.943944 3783772 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506785.943951 3783772 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506785.943954 3783772 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:26:25.947141: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.87 [%]
Global accuracy score (test) = 49.18 [%]
Global F1 score (train) = 67.06 [%]
Global F1 score (test) = 48.51 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.48      0.35       184
 CAMINAR CON MÓVIL O LIBRO       0.36      0.39      0.37       184
       CAMINAR USUAL SPEED       0.12      0.03      0.04       184
            CAMINAR ZIGZAG       0.60      0.51      0.55       184
          DE PIE BARRIENDO       0.52      0.55      0.54       184
   DE PIE DOBLANDO TOALLAS       0.44      0.36      0.40       184
    DE PIE MOVIENDO LIBROS       0.44      0.51      0.47       184
          DE PIE USANDO PC       0.49      0.69      0.58       184
        FASE REPOSO CON K5       0.77      0.76      0.77       184
INCREMENTAL CICLOERGOMETRO       0.85      0.68      0.76       184
           SENTADO LEYENDO       0.41      0.37      0.39       184
         SENTADO USANDO PC       0.29      0.20      0.23       184
      SENTADO VIENDO LA TV       0.50      0.40      0.44       184
   SUBIR Y BAJAR ESCALERAS       0.43      0.67      0.52       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.49      2737
                 macro avg       0.49      0.49      0.49      2737
              weighted avg       0.49      0.49      0.48      2737


Accuracy capturado en la ejecución 7: 49.18 [%]
F1-score capturado en la ejecución 7: 48.51 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.19 [%]
Global accuracy score (test) = 50.16 [%]
Global F1 score (train) = 66.31 [%]
Global F1 score (test) = 49.63 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.30      0.47      0.36       184
 CAMINAR CON MÓVIL O LIBRO       0.44      0.47      0.45       184
       CAMINAR USUAL SPEED       0.22      0.07      0.10       184
            CAMINAR ZIGZAG       0.63      0.50      0.56       184
          DE PIE BARRIENDO       0.54      0.58      0.56       184
   DE PIE DOBLANDO TOALLAS       0.42      0.35      0.38       184
    DE PIE MOVIENDO LIBROS       0.45      0.51      0.48       184
          DE PIE USANDO PC       0.50      0.67      0.57       184
        FASE REPOSO CON K5       0.77      0.76      0.77       184
INCREMENTAL CICLOERGOMETRO       0.84      0.69      0.76       184
           SENTADO LEYENDO       0.38      0.40      0.39       184
         SENTADO USANDO PC       0.25      0.18      0.21       184
      SENTADO VIENDO LA TV       0.54      0.38      0.44       184
   SUBIR Y BAJAR ESCALERAS       0.43      0.72      0.53       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.50      2737
                 macro avg       0.51      0.50      0.50      2737
              weighted avg       0.51      0.50      0.49      2737


Accuracy capturado en la ejecución 8: 50.16 [%]
F1-score capturado en la ejecución 8: 49.63 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.32 [%]
Global accuracy score (test) = 49.76 [%]
Global F1 score (train) = 66.38 [%]
Global F1 score (test) = 49.19 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.46      0.34       184
 CAMINAR CON MÓVIL O LIBRO       0.43      0.42      0.43       184
       CAMINAR USUAL SPEED       0.33      0.07      0.12       184
            CAMINAR ZIGZAG       0.58      0.55      0.57       184
          DE PIE BARRIENDO       0.52      0.57      0.54       184
   DE PIE DOBLANDO TOALLAS       0.45      0.35      0.40       184
    DE PIE MOVIENDO LIBROS       0.44      0.51      0.47       184
          DE PIE USANDO PC       0.50      0.68      0.58       184
        FASE REPOSO CON K5       0.77      0.76      0.77       184
INCREMENTAL CICLOERGOMETRO       0.82      0.70      0.75       184
           SENTADO LEYENDO       0.33      0.26      0.29       184
         SENTADO USANDO PC       0.29      0.24      0.26       184
      SENTADO VIENDO LA TV       0.51      0.41      0.46       184
   SUBIR Y BAJAR ESCALERAS       0.45      0.70      0.54       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.50      2737
                 macro avg       0.51      0.50      0.49      2737
              weighted avg       0.50      0.50      0.49      2737


Accuracy capturado en la ejecución 9: 49.76 [%]
F1-score capturado en la ejecución 9: 49.19 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
2025-10-26 20:27:26.298492: 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`.
2025-10-26 20:27:26.311090: 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 00:00:1761506846.324749 3784955 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761506846.329007 3784955 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761506846.340595 3784955 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506846.340621 3784955 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506846.340624 3784955 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506846.340626 3784955 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:27:26.343918: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.6s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:28:25.814271: 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`.
2025-10-26 20:28:25.826797: 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 00:00:1761506905.840429 3786119 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761506905.844765 3786119 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761506905.855775 3786119 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506905.855797 3786119 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506905.855800 3786119 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506905.855801 3786119 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:28:25.859169: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:29:26.124323: 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`.
2025-10-26 20:29:26.137590: 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 00:00:1761506966.152181 3787316 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761506966.156759 3787316 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761506966.168272 3787316 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506966.168293 3787316 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506966.168296 3787316 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761506966.168298 3787316 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:29:26.171676: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.53 [%]
Global accuracy score (test) = 49.03 [%]
Global F1 score (train) = 66.72 [%]
Global F1 score (test) = 48.43 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.25      0.48      0.33       184
 CAMINAR CON MÓVIL O LIBRO       0.42      0.40      0.41       184
       CAMINAR USUAL SPEED       0.23      0.04      0.07       184
            CAMINAR ZIGZAG       0.64      0.52      0.57       184
          DE PIE BARRIENDO       0.52      0.56      0.54       184
   DE PIE DOBLANDO TOALLAS       0.41      0.34      0.37       184
    DE PIE MOVIENDO LIBROS       0.44      0.51      0.47       184
          DE PIE USANDO PC       0.50      0.67      0.57       184
        FASE REPOSO CON K5       0.78      0.80      0.79       184
INCREMENTAL CICLOERGOMETRO       0.82      0.68      0.74       184
           SENTADO LEYENDO       0.37      0.40      0.39       184
         SENTADO USANDO PC       0.27      0.20      0.23       184
      SENTADO VIENDO LA TV       0.51      0.33      0.40       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.67      0.51       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.49      2737
                 macro avg       0.50      0.49      0.48      2737
              weighted avg       0.50      0.49      0.48      2737


Accuracy capturado en la ejecución 10: 49.03 [%]
F1-score capturado en la ejecución 10: 48.43 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.36 [%]
Global accuracy score (test) = 50.38 [%]
Global F1 score (train) = 66.51 [%]
Global F1 score (test) = 49.91 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.26      0.48      0.34       184
 CAMINAR CON MÓVIL O LIBRO       0.42      0.43      0.43       184
       CAMINAR USUAL SPEED       0.24      0.06      0.10       184
            CAMINAR ZIGZAG       0.58      0.55      0.57       184
          DE PIE BARRIENDO       0.54      0.58      0.56       184
   DE PIE DOBLANDO TOALLAS       0.43      0.34      0.38       184
    DE PIE MOVIENDO LIBROS       0.44      0.51      0.47       184
          DE PIE USANDO PC       0.49      0.67      0.57       184
        FASE REPOSO CON K5       0.78      0.80      0.79       184
INCREMENTAL CICLOERGOMETRO       0.83      0.68      0.75       184
           SENTADO LEYENDO       0.44      0.40      0.42       184
         SENTADO USANDO PC       0.29      0.21      0.24       184
      SENTADO VIENDO LA TV       0.52      0.40      0.46       184
   SUBIR Y BAJAR ESCALERAS       0.48      0.66      0.56       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.50      2737
                 macro avg       0.51      0.51      0.50      2737
              weighted avg       0.51      0.50      0.50      2737


Accuracy capturado en la ejecución 11: 50.38 [%]
F1-score capturado en la ejecución 11: 49.91 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.58 [%]
Global accuracy score (test) = 49.87 [%]
Global F1 score (train) = 66.71 [%]
Global F1 score (test) = 49.42 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.43      0.33       184
 CAMINAR CON MÓVIL O LIBRO       0.43      0.48      0.45       184
       CAMINAR USUAL SPEED       0.21      0.05      0.08       184
            CAMINAR ZIGZAG       0.64      0.52      0.57       184
          DE PIE BARRIENDO       0.53      0.55      0.54       184
   DE PIE DOBLANDO TOALLAS       0.43      0.35      0.39       184
    DE PIE MOVIENDO LIBROS       0.44      0.51      0.47       184
          DE PIE USANDO PC       0.53      0.68      0.60       184
        FASE REPOSO CON K5       0.77      0.77      0.77       184
INCREMENTAL CICLOERGOMETRO       0.82      0.68      0.74       184
           SENTADO LEYENDO       0.36      0.33      0.34       184
         SENTADO USANDO PC       0.34      0.28      0.31       184
      SENTADO VIENDO LA TV       0.50      0.38      0.43       184
   SUBIR Y BAJAR ESCALERAS       0.41      0.70      0.52       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.50      2737
                 macro avg       0.51      0.50      0.49      2737
              weighted avg       0.50      0.50      0.49      2737


Accuracy capturado en la ejecución 12: 49.87 [%]
F1-score capturado en la ejecución 12: 49.42 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
2025-10-26 20:30:26.961995: 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`.
2025-10-26 20:30:26.974618: 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 00:00:1761507026.988430 3788532 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761507026.992669 3788532 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761507027.004232 3788532 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507027.004254 3788532 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507027.004257 3788532 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507027.004258 3788532 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:30:27.007579: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.6s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:31:26.855781: 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`.
2025-10-26 20:31:26.868176: 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 00:00:1761507086.881963 3789723 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761507086.886023 3789723 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761507086.896897 3789723 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507086.896919 3789723 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507086.896931 3789723 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507086.896933 3789723 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:31:26.900025: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.5s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:32:27.836582: 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`.
2025-10-26 20:32:27.849002: 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 00:00:1761507147.862613 3790924 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761507147.866980 3790924 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761507147.877698 3790924 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507147.877718 3790924 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507147.877721 3790924 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507147.877723 3790924 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:32:27.881052: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.6s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 67.0 [%]
Global accuracy score (test) = 48.52 [%]
Global F1 score (train) = 67.14 [%]
Global F1 score (test) = 48.19 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.24      0.40      0.30       184
 CAMINAR CON MÓVIL O LIBRO       0.44      0.39      0.41       184
       CAMINAR USUAL SPEED       0.40      0.10      0.16       184
            CAMINAR ZIGZAG       0.52      0.55      0.53       184
          DE PIE BARRIENDO       0.53      0.58      0.55       184
   DE PIE DOBLANDO TOALLAS       0.37      0.29      0.33       184
    DE PIE MOVIENDO LIBROS       0.44      0.51      0.47       184
          DE PIE USANDO PC       0.49      0.67      0.57       184
        FASE REPOSO CON K5       0.78      0.77      0.77       184
INCREMENTAL CICLOERGOMETRO       0.84      0.68      0.75       184
           SENTADO LEYENDO       0.37      0.34      0.35       184
         SENTADO USANDO PC       0.28      0.20      0.23       184
      SENTADO VIENDO LA TV       0.47      0.38      0.42       184
   SUBIR Y BAJAR ESCALERAS       0.41      0.64      0.50       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.49      2737
                 macro avg       0.50      0.49      0.48      2737
              weighted avg       0.50      0.49      0.48      2737


Accuracy capturado en la ejecución 13: 48.52 [%]
F1-score capturado en la ejecución 13: 48.19 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 0. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.56 [%]
Global accuracy score (test) = 49.65 [%]
Global F1 score (train) = 66.69 [%]
Global F1 score (test) = 49.32 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.25      0.45      0.32       184
 CAMINAR CON MÓVIL O LIBRO       0.43      0.45      0.44       184
       CAMINAR USUAL SPEED       0.20      0.06      0.09       184
            CAMINAR ZIGZAG       0.62      0.52      0.56       184
          DE PIE BARRIENDO       0.55      0.57      0.56       184
   DE PIE DOBLANDO TOALLAS       0.44      0.36      0.40       184
    DE PIE MOVIENDO LIBROS       0.43      0.51      0.46       184
          DE PIE USANDO PC       0.49      0.67      0.57       184
        FASE REPOSO CON K5       0.78      0.77      0.77       184
INCREMENTAL CICLOERGOMETRO       0.82      0.67      0.74       184
           SENTADO LEYENDO       0.37      0.35      0.36       184
         SENTADO USANDO PC       0.29      0.21      0.24       184
      SENTADO VIENDO LA TV       0.53      0.42      0.47       184
   SUBIR Y BAJAR ESCALERAS       0.45      0.68      0.54       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.50      2737
                 macro avg       0.51      0.50      0.49      2737
              weighted avg       0.50      0.50      0.49      2737


Accuracy capturado en la ejecución 14: 49.65 [%]
F1-score capturado en la ejecución 14: 49.32 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.62 [%]
Global accuracy score (test) = 48.78 [%]
Global F1 score (train) = 66.82 [%]
Global F1 score (test) = 48.43 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.26      0.43      0.32       184
 CAMINAR CON MÓVIL O LIBRO       0.41      0.43      0.42       184
       CAMINAR USUAL SPEED       0.26      0.07      0.11       184
            CAMINAR ZIGZAG       0.58      0.53      0.55       184
          DE PIE BARRIENDO       0.53      0.56      0.54       184
   DE PIE DOBLANDO TOALLAS       0.43      0.34      0.38       184
    DE PIE MOVIENDO LIBROS       0.42      0.48      0.45       184
          DE PIE USANDO PC       0.50      0.67      0.57       184
        FASE REPOSO CON K5       0.77      0.76      0.76       184
INCREMENTAL CICLOERGOMETRO       0.80      0.68      0.74       184
           SENTADO LEYENDO       0.39      0.34      0.36       184
         SENTADO USANDO PC       0.27      0.22      0.24       184
      SENTADO VIENDO LA TV       0.50      0.37      0.43       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.65      0.51       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.49      2737
                 macro avg       0.50      0.49      0.48      2737
              weighted avg       0.49      0.49      0.48      2737


Accuracy capturado en la ejecución 15: 48.78 [%]
F1-score capturado en la ejecución 15: 48.43 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
2025-10-26 20:33:27.963151: 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`.
2025-10-26 20:33:27.975628: 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 00:00:1761507207.989283 3792113 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761507207.993614 3792113 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761507208.004620 3792113 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507208.004643 3792113 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507208.004645 3792113 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507208.004647 3792113 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:33:28.008350: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:34:27.888757: 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`.
2025-10-26 20:34:27.901304: 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 00:00:1761507267.915095 3793312 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761507267.919258 3793312 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761507267.930265 3793312 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507267.930295 3793312 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507267.930297 3793312 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507267.930299 3793312 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:34:27.933780: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:35:28.147589: 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`.
2025-10-26 20:35:28.160124: 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 00:00:1761507328.173701 3794506 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761507328.177886 3794506 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761507328.188945 3794506 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507328.188972 3794506 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507328.188975 3794506 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507328.188977 3794506 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:35:28.192169: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 65.7 [%]
Global accuracy score (test) = 49.95 [%]
Global F1 score (train) = 65.77 [%]
Global F1 score (test) = 49.2 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.28      0.52      0.37       184
 CAMINAR CON MÓVIL O LIBRO       0.45      0.44      0.44       184
       CAMINAR USUAL SPEED       0.09      0.02      0.03       184
            CAMINAR ZIGZAG       0.62      0.51      0.56       184
          DE PIE BARRIENDO       0.54      0.57      0.56       184
   DE PIE DOBLANDO TOALLAS       0.42      0.34      0.37       184
    DE PIE MOVIENDO LIBROS       0.42      0.49      0.46       184
          DE PIE USANDO PC       0.50      0.67      0.58       184
        FASE REPOSO CON K5       0.77      0.77      0.77       184
INCREMENTAL CICLOERGOMETRO       0.81      0.67      0.73       184
           SENTADO LEYENDO       0.41      0.38      0.39       184
         SENTADO USANDO PC       0.30      0.22      0.25       184
      SENTADO VIENDO LA TV       0.54      0.42      0.47       184
   SUBIR Y BAJAR ESCALERAS       0.43      0.69      0.53       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.50      2737
                 macro avg       0.50      0.50      0.49      2737
              weighted avg       0.50      0.50      0.49      2737


Accuracy capturado en la ejecución 16: 49.95 [%]
F1-score capturado en la ejecución 16: 49.2 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.62 [%]
Global accuracy score (test) = 49.14 [%]
Global F1 score (train) = 66.75 [%]
Global F1 score (test) = 48.79 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.25      0.43      0.31       184
 CAMINAR CON MÓVIL O LIBRO       0.44      0.39      0.41       184
       CAMINAR USUAL SPEED       0.26      0.08      0.12       184
            CAMINAR ZIGZAG       0.56      0.55      0.56       184
          DE PIE BARRIENDO       0.54      0.58      0.56       184
   DE PIE DOBLANDO TOALLAS       0.44      0.36      0.40       184
    DE PIE MOVIENDO LIBROS       0.43      0.49      0.46       184
          DE PIE USANDO PC       0.50      0.67      0.57       184
        FASE REPOSO CON K5       0.77      0.77      0.77       184
INCREMENTAL CICLOERGOMETRO       0.82      0.68      0.74       184
           SENTADO LEYENDO       0.36      0.33      0.35       184
         SENTADO USANDO PC       0.28      0.20      0.23       184
      SENTADO VIENDO LA TV       0.51      0.41      0.45       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.65      0.51       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.49      2737
                 macro avg       0.50      0.49      0.49      2737
              weighted avg       0.50      0.49      0.48      2737


Accuracy capturado en la ejecución 17: 49.14 [%]
F1-score capturado en la ejecución 17: 48.79 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.41 [%]
Global accuracy score (test) = 49.91 [%]
Global F1 score (train) = 66.5 [%]
Global F1 score (test) = 49.34 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.28      0.49      0.36       184
 CAMINAR CON MÓVIL O LIBRO       0.46      0.49      0.47       184
       CAMINAR USUAL SPEED       0.26      0.06      0.10       184
            CAMINAR ZIGZAG       0.63      0.51      0.56       184
          DE PIE BARRIENDO       0.52      0.55      0.53       184
   DE PIE DOBLANDO TOALLAS       0.42      0.33      0.37       184
    DE PIE MOVIENDO LIBROS       0.43      0.51      0.47       184
          DE PIE USANDO PC       0.50      0.71      0.59       184
        FASE REPOSO CON K5       0.77      0.76      0.76       184
INCREMENTAL CICLOERGOMETRO       0.81      0.68      0.74       184
           SENTADO LEYENDO       0.37      0.33      0.35       184
         SENTADO USANDO PC       0.31      0.23      0.26       184
      SENTADO VIENDO LA TV       0.51      0.40      0.45       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.68      0.52       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.50      2737
                 macro avg       0.51      0.50      0.49      2737
              weighted avg       0.51      0.50      0.49      2737


Accuracy capturado en la ejecución 18: 49.91 [%]
F1-score capturado en la ejecución 18: 49.34 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
2025-10-26 20:36:28.810890: 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`.
2025-10-26 20:36:28.823450: 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 00:00:1761507388.836870 3795703 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761507388.841215 3795703 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761507388.852205 3795703 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507388.852226 3795703 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507388.852228 3795703 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507388.852230 3795703 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:36:28.855640: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:37:28.433926: 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`.
2025-10-26 20:37:28.446861: 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 00:00:1761507448.460778 3796882 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761507448.464937 3796882 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761507448.475933 3796882 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507448.475957 3796882 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507448.475960 3796882 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507448.475962 3796882 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:37:28.479257: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:38:27.821414: 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`.
2025-10-26 20:38:27.834033: 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 00:00:1761507507.847628 3798058 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761507507.851943 3798058 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761507507.862859 3798058 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507507.862880 3798058 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507507.862883 3798058 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507507.862885 3798058 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:38:27.866329: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 0. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.59 [%]
Global accuracy score (test) = 50.35 [%]
Global F1 score (train) = 66.7 [%]
Global F1 score (test) = 49.93 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.32      0.41      0.36       184
 CAMINAR CON MÓVIL O LIBRO       0.41      0.46      0.43       184
       CAMINAR USUAL SPEED       0.36      0.13      0.19       184
            CAMINAR ZIGZAG       0.60      0.55      0.57       184
          DE PIE BARRIENDO       0.54      0.58      0.56       184
   DE PIE DOBLANDO TOALLAS       0.42      0.34      0.37       184
    DE PIE MOVIENDO LIBROS       0.44      0.51      0.47       184
          DE PIE USANDO PC       0.50      0.69      0.58       184
        FASE REPOSO CON K5       0.77      0.75      0.76       184
INCREMENTAL CICLOERGOMETRO       0.84      0.69      0.76       184
           SENTADO LEYENDO       0.38      0.33      0.35       184
         SENTADO USANDO PC       0.28      0.21      0.24       184
      SENTADO VIENDO LA TV       0.49      0.41      0.45       184
   SUBIR Y BAJAR ESCALERAS       0.41      0.73      0.52       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.50      2737
                 macro avg       0.51      0.51      0.50      2737
              weighted avg       0.51      0.50      0.50      2737


Accuracy capturado en la ejecución 19: 50.35 [%]
F1-score capturado en la ejecución 19: 49.93 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.3 [%]
Global accuracy score (test) = 49.1 [%]
Global F1 score (train) = 66.42 [%]
Global F1 score (test) = 48.33 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.28      0.45      0.34       184
 CAMINAR CON MÓVIL O LIBRO       0.36      0.39      0.38       184
       CAMINAR USUAL SPEED       0.03      0.01      0.01       184
            CAMINAR ZIGZAG       0.63      0.53      0.58       184
          DE PIE BARRIENDO       0.54      0.54      0.54       184
   DE PIE DOBLANDO TOALLAS       0.40      0.38      0.39       184
    DE PIE MOVIENDO LIBROS       0.44      0.49      0.47       184
          DE PIE USANDO PC       0.51      0.70      0.59       184
        FASE REPOSO CON K5       0.77      0.75      0.76       184
INCREMENTAL CICLOERGOMETRO       0.82      0.68      0.75       184
           SENTADO LEYENDO       0.38      0.34      0.36       184
         SENTADO USANDO PC       0.31      0.22      0.26       184
      SENTADO VIENDO LA TV       0.50      0.41      0.45       184
   SUBIR Y BAJAR ESCALERAS       0.41      0.68      0.51       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.49      2737
                 macro avg       0.49      0.49      0.48      2737
              weighted avg       0.48      0.49      0.48      2737


Accuracy capturado en la ejecución 20: 49.1 [%]
F1-score capturado en la ejecución 20: 48.33 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.49 [%]
Global accuracy score (test) = 48.85 [%]
Global F1 score (train) = 66.6 [%]
Global F1 score (test) = 48.25 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.26      0.41      0.32       184
 CAMINAR CON MÓVIL O LIBRO       0.42      0.43      0.43       184
       CAMINAR USUAL SPEED       0.21      0.05      0.08       184
            CAMINAR ZIGZAG       0.56      0.54      0.55       184
          DE PIE BARRIENDO       0.50      0.56      0.53       184
   DE PIE DOBLANDO TOALLAS       0.42      0.35      0.38       184
    DE PIE MOVIENDO LIBROS       0.43      0.49      0.46       184
          DE PIE USANDO PC       0.49      0.68      0.57       184
        FASE REPOSO CON K5       0.77      0.76      0.76       184
INCREMENTAL CICLOERGOMETRO       0.82      0.67      0.74       184
           SENTADO LEYENDO       0.35      0.30      0.33       184
         SENTADO USANDO PC       0.27      0.20      0.23       184
      SENTADO VIENDO LA TV       0.53      0.45      0.48       184
   SUBIR Y BAJAR ESCALERAS       0.41      0.65      0.50       184
                    TROTAR       0.94      0.82      0.87       161

                  accuracy                           0.49      2737
                 macro avg       0.49      0.49      0.48      2737
              weighted avg       0.49      0.49      0.48      2737


Accuracy capturado en la ejecución 21: 48.85 [%]
F1-score capturado en la ejecución 21: 48.25 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
2025-10-26 20:39:28.525983: 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`.
2025-10-26 20:39:28.538842: 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 00:00:1761507568.553201 3799254 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761507568.557716 3799254 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761507568.569157 3799254 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507568.569179 3799254 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507568.569182 3799254 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507568.569184 3799254 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:39:28.572647: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.5s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:40:29.283887: 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`.
2025-10-26 20:40:29.296362: 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 00:00:1761507629.309897 3800460 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761507629.314257 3800460 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761507629.325065 3800460 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507629.325087 3800460 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507629.325089 3800460 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507629.325091 3800460 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:40:29.328435: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.6s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.5s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:41:29.793197: 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`.
2025-10-26 20:41:29.805931: 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 00:00:1761507689.819658 3801647 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761507689.823984 3801647 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761507689.834804 3801647 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507689.834836 3801647 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507689.834840 3801647 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507689.834843 3801647 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:41:29.838286: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.14 [%]
Global accuracy score (test) = 48.81 [%]
Global F1 score (train) = 66.21 [%]
Global F1 score (test) = 48.13 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.49      0.35       184
 CAMINAR CON MÓVIL O LIBRO       0.40      0.38      0.39       184
       CAMINAR USUAL SPEED       0.20      0.04      0.06       184
            CAMINAR ZIGZAG       0.60      0.55      0.57       184
          DE PIE BARRIENDO       0.53      0.54      0.54       184
   DE PIE DOBLANDO TOALLAS       0.42      0.36      0.38       184
    DE PIE MOVIENDO LIBROS       0.42      0.48      0.45       184
          DE PIE USANDO PC       0.47      0.68      0.56       184
        FASE REPOSO CON K5       0.77      0.77      0.77       184
INCREMENTAL CICLOERGOMETRO       0.83      0.68      0.75       184
           SENTADO LEYENDO       0.37      0.33      0.34       184
         SENTADO USANDO PC       0.31      0.21      0.25       184
      SENTADO VIENDO LA TV       0.47      0.37      0.41       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.67      0.52       184
                    TROTAR       0.93      0.81      0.87       161

                  accuracy                           0.49      2737
                 macro avg       0.49      0.49      0.48      2737
              weighted avg       0.49      0.49      0.48      2737


Accuracy capturado en la ejecución 22: 48.81 [%]
F1-score capturado en la ejecución 22: 48.13 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.99 [%]
Global accuracy score (test) = 49.36 [%]
Global F1 score (train) = 67.16 [%]
Global F1 score (test) = 48.65 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.26      0.48      0.34       184
 CAMINAR CON MÓVIL O LIBRO       0.39      0.40      0.39       184
       CAMINAR USUAL SPEED       0.16      0.03      0.05       184
            CAMINAR ZIGZAG       0.59      0.51      0.54       184
          DE PIE BARRIENDO       0.53      0.54      0.54       184
   DE PIE DOBLANDO TOALLAS       0.39      0.33      0.36       184
    DE PIE MOVIENDO LIBROS       0.42      0.51      0.46       184
          DE PIE USANDO PC       0.51      0.69      0.59       184
        FASE REPOSO CON K5       0.77      0.76      0.77       184
INCREMENTAL CICLOERGOMETRO       0.83      0.68      0.75       184
           SENTADO LEYENDO       0.38      0.33      0.35       184
         SENTADO USANDO PC       0.29      0.22      0.25       184
      SENTADO VIENDO LA TV       0.49      0.40      0.44       184
   SUBIR Y BAJAR ESCALERAS       0.49      0.75      0.60       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.49      2737
                 macro avg       0.50      0.50      0.49      2737
              weighted avg       0.49      0.49      0.48      2737


Accuracy capturado en la ejecución 23: 49.36 [%]
F1-score capturado en la ejecución 23: 48.65 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 0. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.32 [%]
Global accuracy score (test) = 49.58 [%]
Global F1 score (train) = 66.44 [%]
Global F1 score (test) = 48.82 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.28      0.52      0.36       184
 CAMINAR CON MÓVIL O LIBRO       0.46      0.42      0.44       184
       CAMINAR USUAL SPEED       0.14      0.03      0.05       184
            CAMINAR ZIGZAG       0.63      0.54      0.58       184
          DE PIE BARRIENDO       0.52      0.59      0.55       184
   DE PIE DOBLANDO TOALLAS       0.43      0.34      0.38       184
    DE PIE MOVIENDO LIBROS       0.43      0.52      0.47       184
          DE PIE USANDO PC       0.50      0.70      0.58       184
        FASE REPOSO CON K5       0.77      0.75      0.76       184
INCREMENTAL CICLOERGOMETRO       0.83      0.67      0.74       184
           SENTADO LEYENDO       0.38      0.35      0.37       184
         SENTADO USANDO PC       0.30      0.21      0.24       184
      SENTADO VIENDO LA TV       0.48      0.36      0.41       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.67      0.51       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.50      2737
                 macro avg       0.50      0.50      0.49      2737
              weighted avg       0.50      0.50      0.49      2737


Accuracy capturado en la ejecución 24: 49.58 [%]
F1-score capturado en la ejecución 24: 48.82 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
2025-10-26 20:42:30.457975: 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`.
2025-10-26 20:42:30.470411: 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 00:00:1761507750.484085 3802832 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761507750.488379 3802832 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761507750.499274 3802832 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507750.499293 3802832 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507750.499296 3802832 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507750.499299 3802832 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:42:30.502636: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.8s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.0s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:43:30.648389: 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`.
2025-10-26 20:43:30.660942: 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 00:00:1761507810.674805 3804044 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761507810.679254 3804044 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761507810.690408 3804044 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507810.690433 3804044 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507810.690435 3804044 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507810.690436 3804044 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:43:30.693868: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.6s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:44:30.251014: 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`.
2025-10-26 20:44:30.263420: 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 00:00:1761507870.277104 3805218 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761507870.281555 3805218 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761507870.292327 3805218 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507870.292349 3805218 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507870.292360 3805218 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507870.292361 3805218 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:44:30.295750: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.6s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.5s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 65.82 [%]
Global accuracy score (test) = 48.37 [%]
Global F1 score (train) = 65.87 [%]
Global F1 score (test) = 48.1 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.24      0.48      0.32       184
 CAMINAR CON MÓVIL O LIBRO       0.40      0.42      0.41       184
       CAMINAR USUAL SPEED       0.15      0.04      0.06       184
            CAMINAR ZIGZAG       0.65      0.53      0.58       184
          DE PIE BARRIENDO       0.55      0.57      0.56       184
   DE PIE DOBLANDO TOALLAS       0.41      0.33      0.37       184
    DE PIE MOVIENDO LIBROS       0.42      0.50      0.46       184
          DE PIE USANDO PC       0.52      0.67      0.59       184
        FASE REPOSO CON K5       0.77      0.75      0.76       184
INCREMENTAL CICLOERGOMETRO       0.83      0.68      0.75       184
           SENTADO LEYENDO       0.34      0.31      0.32       184
         SENTADO USANDO PC       0.28      0.23      0.25       184
      SENTADO VIENDO LA TV       0.46      0.35      0.40       184
   SUBIR Y BAJAR ESCALERAS       0.45      0.63      0.53       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.48      2737
                 macro avg       0.49      0.49      0.48      2737
              weighted avg       0.49      0.48      0.48      2737


Accuracy capturado en la ejecución 25: 48.37 [%]
F1-score capturado en la ejecución 25: 48.1 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.41 [%]
Global accuracy score (test) = 49.73 [%]
Global F1 score (train) = 66.57 [%]
Global F1 score (test) = 49.45 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.24      0.45      0.31       184
 CAMINAR CON MÓVIL O LIBRO       0.43      0.43      0.43       184
       CAMINAR USUAL SPEED       0.16      0.05      0.08       184
            CAMINAR ZIGZAG       0.61      0.52      0.56       184
          DE PIE BARRIENDO       0.52      0.58      0.55       184
   DE PIE DOBLANDO TOALLAS       0.42      0.34      0.37       184
    DE PIE MOVIENDO LIBROS       0.44      0.51      0.47       184
          DE PIE USANDO PC       0.50      0.70      0.58       184
        FASE REPOSO CON K5       0.78      0.77      0.77       184
INCREMENTAL CICLOERGOMETRO       0.83      0.67      0.74       184
           SENTADO LEYENDO       0.39      0.34      0.36       184
         SENTADO USANDO PC       0.31      0.24      0.27       184
      SENTADO VIENDO LA TV       0.51      0.41      0.45       184
   SUBIR Y BAJAR ESCALERAS       0.48      0.69      0.57       184
                    TROTAR       0.96      0.81      0.88       161

                  accuracy                           0.50      2737
                 macro avg       0.51      0.50      0.49      2737
              weighted avg       0.50      0.50      0.49      2737


Accuracy capturado en la ejecución 26: 49.73 [%]
F1-score capturado en la ejecución 26: 49.45 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.32 [%]
Global accuracy score (test) = 49.21 [%]
Global F1 score (train) = 66.5 [%]
Global F1 score (test) = 48.74 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.44      0.34       184
 CAMINAR CON MÓVIL O LIBRO       0.45      0.48      0.47       184
       CAMINAR USUAL SPEED       0.23      0.07      0.10       184
            CAMINAR ZIGZAG       0.63      0.51      0.56       184
          DE PIE BARRIENDO       0.54      0.57      0.55       184
   DE PIE DOBLANDO TOALLAS       0.40      0.31      0.35       184
    DE PIE MOVIENDO LIBROS       0.42      0.49      0.45       184
          DE PIE USANDO PC       0.51      0.67      0.58       184
        FASE REPOSO CON K5       0.77      0.77      0.77       184
INCREMENTAL CICLOERGOMETRO       0.82      0.68      0.75       184
           SENTADO LEYENDO       0.32      0.28      0.30       184
         SENTADO USANDO PC       0.26      0.21      0.23       184
      SENTADO VIENDO LA TV       0.52      0.41      0.46       184
   SUBIR Y BAJAR ESCALERAS       0.43      0.71      0.53       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.49      2737
                 macro avg       0.50      0.49      0.49      2737
              weighted avg       0.50      0.49      0.48      2737


Accuracy capturado en la ejecución 27: 49.21 [%]
F1-score capturado en la ejecución 27: 48.74 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
2025-10-26 20:45:30.390800: 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`.
2025-10-26 20:45:30.403194: 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 00:00:1761507930.416911 3806401 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761507930.421337 3806401 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761507930.432518 3806401 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507930.432543 3806401 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507930.432546 3806401 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507930.432547 3806401 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:45:30.435775: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-10-26 20:46:30.923872: 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`.
2025-10-26 20:46:30.936213: 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 00:00:1761507990.949659 3807597 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761507990.954040 3807597 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761507990.964971 3807597 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507990.964991 3807597 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507990.964994 3807597 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761507990.964996 3807597 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 20:46:30.968354: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done 266 out of 266 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 266 out of 266 | elapsed:    0.0s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.6 [%]
Global accuracy score (test) = 49.58 [%]
Global F1 score (train) = 66.74 [%]
Global F1 score (test) = 49.19 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.45      0.33       184
 CAMINAR CON MÓVIL O LIBRO       0.45      0.44      0.44       184
       CAMINAR USUAL SPEED       0.28      0.07      0.11       184
            CAMINAR ZIGZAG       0.62      0.52      0.57       184
          DE PIE BARRIENDO       0.51      0.57      0.54       184
   DE PIE DOBLANDO TOALLAS       0.41      0.33      0.36       184
    DE PIE MOVIENDO LIBROS       0.43      0.50      0.46       184
          DE PIE USANDO PC       0.50      0.68      0.58       184
        FASE REPOSO CON K5       0.78      0.76      0.77       184
INCREMENTAL CICLOERGOMETRO       0.83      0.68      0.75       184
           SENTADO LEYENDO       0.39      0.36      0.38       184
         SENTADO USANDO PC       0.30      0.23      0.26       184
      SENTADO VIENDO LA TV       0.53      0.40      0.45       184
   SUBIR Y BAJAR ESCALERAS       0.41      0.68      0.51       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.50      2737
                 macro avg       0.51      0.50      0.49      2737
              weighted avg       0.50      0.50      0.49      2737


Accuracy capturado en la ejecución 28: 49.58 [%]
F1-score capturado en la ejecución 28: 49.19 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.67 [%]
Global accuracy score (test) = 49.87 [%]
Global F1 score (train) = 66.8 [%]
Global F1 score (test) = 49.3 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.28      0.46      0.35       184
 CAMINAR CON MÓVIL O LIBRO       0.40      0.47      0.43       184
       CAMINAR USUAL SPEED       0.25      0.06      0.10       184
            CAMINAR ZIGZAG       0.61      0.55      0.58       184
          DE PIE BARRIENDO       0.51      0.57      0.54       184
   DE PIE DOBLANDO TOALLAS       0.41      0.36      0.38       184
    DE PIE MOVIENDO LIBROS       0.44      0.49      0.47       184
          DE PIE USANDO PC       0.49      0.68      0.57       184
        FASE REPOSO CON K5       0.77      0.77      0.77       184
INCREMENTAL CICLOERGOMETRO       0.83      0.68      0.75       184
           SENTADO LEYENDO       0.35      0.29      0.32       184
         SENTADO USANDO PC       0.29      0.21      0.25       184
      SENTADO VIENDO LA TV       0.51      0.44      0.47       184
   SUBIR Y BAJAR ESCALERAS       0.45      0.67      0.54       184
                    TROTAR       0.96      0.81      0.88       161

                  accuracy                           0.50      2737
                 macro avg       0.50      0.50      0.49      2737
              weighted avg       0.50      0.50      0.49      2737


Accuracy capturado en la ejecución 29: 49.87 [%]
F1-score capturado en la ejecución 29: 49.3 [%]

=== EJECUCIÓN 30 ===

--- TRAIN (ejecución 30) ---

--- TEST (ejecución 30) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2737, 91)
(23391, 91)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 66.78 [%]
Global accuracy score (test) = 48.81 [%]
Global F1 score (train) = 66.84 [%]
Global F1 score (test) = 48.41 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.23      0.45      0.31       184
 CAMINAR CON MÓVIL O LIBRO       0.45      0.43      0.44       184
       CAMINAR USUAL SPEED       0.22      0.05      0.09       184
            CAMINAR ZIGZAG       0.61      0.51      0.56       184
          DE PIE BARRIENDO       0.54      0.58      0.56       184
   DE PIE DOBLANDO TOALLAS       0.41      0.33      0.36       184
    DE PIE MOVIENDO LIBROS       0.41      0.49      0.45       184
          DE PIE USANDO PC       0.50      0.67      0.57       184
        FASE REPOSO CON K5       0.77      0.76      0.76       184
INCREMENTAL CICLOERGOMETRO       0.83      0.68      0.75       184
           SENTADO LEYENDO       0.34      0.33      0.34       184
         SENTADO USANDO PC       0.27      0.18      0.22       184
      SENTADO VIENDO LA TV       0.49      0.40      0.44       184
   SUBIR Y BAJAR ESCALERAS       0.47      0.69      0.56       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.49      2737
                 macro avg       0.50      0.49      0.48      2737
              weighted avg       0.49      0.49      0.48      2737


Accuracy capturado en la ejecución 30: 48.81 [%]
F1-score capturado en la ejecución 30: 48.41 [%]

=== RESUMEN FINAL ===
Accuracies: [49.91, 49.43, 49.0, 49.62, 48.85, 49.07, 49.18, 50.16, 49.76, 49.03, 50.38, 49.87, 48.52, 49.65, 48.78, 49.95, 49.14, 49.91, 50.35, 49.1, 48.85, 48.81, 49.36, 49.58, 48.37, 49.73, 49.21, 49.58, 49.87, 48.81]
F1-scores: [49.34, 48.98, 48.58, 49.21, 48.3, 48.62, 48.51, 49.63, 49.19, 48.43, 49.91, 49.42, 48.19, 49.32, 48.43, 49.2, 48.79, 49.34, 49.93, 48.33, 48.25, 48.13, 48.65, 48.82, 48.1, 49.45, 48.74, 49.19, 49.3, 48.41]
Accuracy mean: 49.3943 | std: 0.5257
F1 mean: 48.8897 | std: 0.5239

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_C/case_C_BRF_acc_gyr_17_classes/metrics_test.npz
