2025-11-07 09:23:00.947680: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:23:00.958918: 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 00:00:1762503780.978703 2312964 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762503780.983627 2312964 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762503780.994266 2312964 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762503780.994288 2312964 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762503780.994290 2312964 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762503780.994292 2312964 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:23:00.997571: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/tune/impl/tuner_internal.py:144: RayDeprecationWarning: The `RunConfig` class should be imported from `ray.tune` when passing it to the Tuner. Please update your imports. See this issue for more context and migration options: https://github.com/ray-project/ray/issues/49454. Disable these warnings by setting the environment variable: RAY_TRAIN_ENABLE_V2_MIGRATION_WARNINGS=0
  _log_deprecation_warning(
2025-11-07 09:23:10,919	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-07 09:23:11,600	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-07 09:23:11,668	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_e437 because trial dirname 'dir_ffa4e' already exists.
2025-11-07 09:23:11,670	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_29b5 because trial dirname 'dir_ffa4e' already exists.
2025-11-07 09:23:11,673	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_35cf because trial dirname 'dir_ffa4e' already exists.
2025-11-07 09:23:11,675	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_d30e because trial dirname 'dir_ffa4e' already exists.
2025-11-07 09:23:11,677	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_e23c because trial dirname 'dir_ffa4e' already exists.
2025-11-07 09:23:11,679	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_a027 because trial dirname 'dir_ffa4e' already exists.
2025-11-07 09:23:11,681	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_a083 because trial dirname 'dir_ffa4e' already exists.
2025-11-07 09:23:11,683	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_6e3c because trial dirname 'dir_ffa4e' already exists.
2025-11-07 09:23:11,686	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_e967 because trial dirname 'dir_ffa4e' already exists.
2025-11-07 09:23:11,688	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_1c84 because trial dirname 'dir_ffa4e' already exists.
2025-11-07 09:23:11,691	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_213e because trial dirname 'dir_ffa4e' already exists.
2025-11-07 09:23:11,693	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_079f because trial dirname 'dir_ffa4e' already exists.
2025-11-07 09:23:11,696	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_69c3 because trial dirname 'dir_ffa4e' already exists.
2025-11-07 09:23:11,700	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_ef7d because trial dirname 'dir_ffa4e' already exists.
2025-11-07 09:23:11,703	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_3bfc because trial dirname 'dir_ffa4e' already exists.
2025-11-07 09:23:11,705	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_a5c9 because trial dirname 'dir_ffa4e' already exists.
2025-11-07 09:23:11,708	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_bfa6 because trial dirname 'dir_ffa4e' already exists.
2025-11-07 09:23:11,713	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_2554 because trial dirname 'dir_ffa4e' already exists.
2025-11-07 09:23:11,721	INFO trial.py:182 -- Creating a new dirname dir_ffa4e_95bd because trial dirname 'dir_ffa4e' 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_17_classes/BalancedRF_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-07_09-23-10_214546_2312964/artifacts/2025-11-07_09-23-11/BalancedRF_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-07 09:23:11. Total running time: 0s
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 │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_ffa4e    PENDING               425             5                    31                   15   sqrt                       8734 │
│ trial_ffa4e    PENDING               386             5                    33                   15   0.3                        3764 │
│ trial_ffa4e    PENDING               237             5                    55                   13   0.3                          13 │
│ trial_ffa4e    PENDING               265             6                    28                   16   sqrt                       8896 │
│ trial_ffa4e    PENDING               360             6                    44                   17   0.3                        9848 │
│ trial_ffa4e    PENDING               424             7                    42                   26   0.3                         533 │
│ trial_ffa4e    PENDING               285             7                    21                   16   0.3                        5746 │
│ trial_ffa4e    PENDING               226             5                    44                   28   sqrt                       5390 │
│ trial_ffa4e    PENDING               314             5                    37                   28   sqrt                       9771 │
│ trial_ffa4e    PENDING               274             6                    34                   23   0.3                        6780 │
│ trial_ffa4e    PENDING               430             6                    55                   18   sqrt                       7897 │
│ trial_ffa4e    PENDING               291             5                    51                   10   0.3                        1519 │
│ trial_ffa4e    PENDING               392             5                    52                   28   sqrt                        218 │
│ trial_ffa4e    PENDING               370             6                    23                   22   sqrt                       1875 │
│ trial_ffa4e    PENDING               425             6                    56                   12   0.3                        6581 │
│ trial_ffa4e    PENDING               451             5                    28                   16   0.3                        2847 │
│ trial_ffa4e    PENDING               267             5                    49                   28   sqrt                       9942 │
│ trial_ffa4e    PENDING               320             7                    27                   24   0.3                        3764 │
│ trial_ffa4e    PENDING               495             5                    39                   20   sqrt                        440 │
│ trial_ffa4e    PENDING               483             7                    32                   19   0.3                         407 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_ffa4e started with configuration:
╭───────────────────────────────────╮
│ Trial trial_ffa4e config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               15 │
│ min_samples_split              33 │
│ n_estimators                  386 │
│ random_state                 3764 │
╰───────────────────────────────────╯
Trial trial_ffa4e started with configuration:
╭───────────────────────────────────╮
│ Trial trial_ffa4e config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               16 │
│ min_samples_split              28 │
│ n_estimators                  265 │
│ random_state                 8896 │
╰───────────────────────────────────╯
Trial trial_ffa4e started with configuration:
╭──────────────────────────────────╮
│ Trial trial_ffa4e config         │
├──────────────────────────────────┤
│ max_depth                      5 │
│ max_features                 0.3 │
│ min_samples_leaf              13 │
│ min_samples_split             55 │
│ n_estimators                 237 │
│ random_state                  13 │
╰──────────────────────────────────╯
Trial trial_ffa4e started with configuration:
╭───────────────────────────────────╮
│ Trial trial_ffa4e config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               28 │
│ min_samples_split              44 │
│ n_estimators                  226 │
│ random_state                 5390 │
╰───────────────────────────────────╯
Trial trial_ffa4e started with configuration:
╭───────────────────────────────────╮
│ Trial trial_ffa4e config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               16 │
│ min_samples_split              28 │
│ n_estimators                  451 │
│ random_state                 2847 │
╰───────────────────────────────────╯
Trial trial_ffa4e started with configuration:
╭───────────────────────────────────╮
│ Trial trial_ffa4e config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               28 │
│ min_samples_split              37 │
│ n_estimators                  314 │
│ random_state                 9771 │
╰───────────────────────────────────╯
Trial trial_ffa4e started with configuration:
╭───────────────────────────────────╮
│ Trial trial_ffa4e config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               17 │
│ min_samples_split              44 │
│ n_estimators                  360 │
│ random_state                 9848 │
╰───────────────────────────────────╯
Trial trial_ffa4e started with configuration:
╭───────────────────────────────────╮
│ Trial trial_ffa4e config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               20 │
│ min_samples_split              39 │
│ n_estimators                  495 │
│ random_state                  440 │
╰───────────────────────────────────╯
Trial trial_ffa4e started with configuration:
╭───────────────────────────────────╮
│ Trial trial_ffa4e config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               15 │
│ min_samples_split              31 │
│ n_estimators                  425 │
│ random_state                 8734 │
╰───────────────────────────────────╯
Trial trial_ffa4e started with configuration:
╭───────────────────────────────────╮
│ Trial trial_ffa4e config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               16 │
│ min_samples_split              21 │
│ n_estimators                  285 │
│ random_state                 5746 │
╰───────────────────────────────────╯
Trial trial_ffa4e started with configuration:
╭──────────────────────────────────╮
│ Trial trial_ffa4e config         │
├──────────────────────────────────┤
│ max_depth                      7 │
│ max_features                 0.3 │
│ min_samples_leaf              26 │
│ min_samples_split             42 │
│ n_estimators                 424 │
│ random_state                 533 │
╰──────────────────────────────────╯
Trial trial_ffa4e started with configuration:
╭──────────────────────────────────╮
│ Trial trial_ffa4e config         │
├──────────────────────────────────┤
│ max_depth                      7 │
│ max_features                 0.3 │
│ min_samples_leaf              19 │
│ min_samples_split             32 │
│ n_estimators                 483 │
│ random_state                 407 │
╰──────────────────────────────────╯
Trial trial_ffa4e started with configuration:
╭───────────────────────────────────╮
│ Trial trial_ffa4e config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               10 │
│ min_samples_split              51 │
│ n_estimators                  291 │
│ random_state                 1519 │
╰───────────────────────────────────╯
Trial trial_ffa4e started with configuration:
╭───────────────────────────────────╮
│ Trial trial_ffa4e config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               24 │
│ min_samples_split              27 │
│ n_estimators                  320 │
│ random_state                 3764 │
╰───────────────────────────────────╯
Trial trial_ffa4e started with configuration:
╭───────────────────────────────────╮
│ Trial trial_ffa4e config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               12 │
│ min_samples_split              56 │
│ n_estimators                  425 │
│ random_state                 6581 │
╰───────────────────────────────────╯
Trial trial_ffa4e started with configuration:
[36m(train_brf_ray_tune pid=2314611)[0m 2025-11-07 09:23:14.794019: 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=2314611)[0m 2025-11-07 09:23:14.815242: 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=2314611)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_brf_ray_tune pid=2314611)[0m E0000 00:00:1762503794.845184 2315787 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=2314611)[0m E0000 00:00:1762503794.853587 2315787 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=2314611)[0m W0000 00:00:1762503794.874354 2315787 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=2314611)[0m W0000 00:00:1762503794.874388 2315787 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=2314611)[0m W0000 00:00:1762503794.874390 2315787 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=2314611)[0m W0000 00:00:1762503794.874393 2315787 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=2314605)[0m 2025-11-07 09:23:14.953411: 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=2314605)[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=2314611)[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=2314611)[0m   return fit_method(estimator, *args, **kwargs)
[36m(train_brf_ray_tune pid=2314635)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=2314635)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.6s
[36m(train_brf_ray_tune pid=2314678)[0m 2025-11-07 09:23:15.473752: 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=2314678)[0m 2025-11-07 09:23:15.495316: 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=2314678)[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=2314678)[0m E0000 00:00:1762503795.523575 2315949 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=2314678)[0m E0000 00:00:1762503795.531671 2315949 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=2314678)[0m W0000 00:00:1762503795.552603 2315949 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=2314678)[0m 2025-11-07 09:23:15.559262: 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=2314678)[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=2314671)[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 18x across cluster][0m
[36m(train_brf_ray_tune pid=2314671)[0m   return fit_method(estimator, *args, **kwargs)[32m [repeated 18x across cluster][0m
[36m(train_brf_ray_tune pid=2314628)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 18x across cluster][0m
[36m(train_brf_ray_tune pid=2314669)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    3.6s[32m [repeated 13x across cluster][0m
[36m(train_brf_ray_tune pid=2314678)[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=2314678)[0m   return fit_method(estimator, *args, **kwargs)
[36m(train_brf_ray_tune pid=2314678)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=2314678)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    3.5s[32m [repeated 6x across cluster][0m
[36m(train_brf_ray_tune pid=2314647)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   21.1s[32m [repeated 6x across cluster][0m
[36m(train_brf_ray_tune pid=2314668)[0m [Parallel(n_jobs=-1)]: Done 226 out of 226 | elapsed:   26.1s finished
[36m(train_brf_ray_tune pid=2314668)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=2314611)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   26.8s[32m [repeated 3x across cluster][0m
[36m(train_brf_ray_tune pid=2314668)[0m [Parallel(n_jobs=20)]: Done 226 out of 226 | elapsed:    1.2s finished
[36m(train_brf_ray_tune pid=2314668)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=2314668)[0m [Parallel(n_jobs=20)]: Done 226 out of 226 | elapsed:    0.9s finished
[36m(train_brf_ray_tune pid=2314668)[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=2314668)[0m   _log_deprecation_warning(
╭───────────────────────────────────╮
│ Trial trial_ffa4e config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               22 │
│ min_samples_split              23 │
│ n_estimators                  370 │
│ random_state                 1875 │
╰───────────────────────────────────╯
Trial trial_ffa4e started with configuration:
╭───────────────────────────────────╮
│ Trial trial_ffa4e config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               28 │
│ min_samples_split              52 │
│ n_estimators                  392 │
│ random_state                  218 │
╰───────────────────────────────────╯
Trial trial_ffa4e started with configuration:
╭───────────────────────────────────╮
│ Trial trial_ffa4e config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               18 │
│ min_samples_split              55 │
│ n_estimators                  430 │
│ random_state                 7897 │
╰───────────────────────────────────╯
Trial trial_ffa4e started with configuration:
╭───────────────────────────────────╮
│ Trial trial_ffa4e config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               23 │
│ min_samples_split              34 │
│ n_estimators                  274 │
│ random_state                 6780 │
╰───────────────────────────────────╯
Trial trial_ffa4e started with configuration:
╭───────────────────────────────────╮
│ Trial trial_ffa4e config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               28 │
│ min_samples_split              49 │
│ n_estimators                  267 │
│ random_state                 9942 │
╰───────────────────────────────────╯

Trial status: 20 RUNNING
Current time: 2025-11-07 09:23:41. 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_ffa4e    RUNNING               425             5                    31                   15   sqrt                       8734 │
│ trial_ffa4e    RUNNING               386             5                    33                   15   0.3                        3764 │
│ trial_ffa4e    RUNNING               237             5                    55                   13   0.3                          13 │
│ trial_ffa4e    RUNNING               265             6                    28                   16   sqrt                       8896 │
│ trial_ffa4e    RUNNING               360             6                    44                   17   0.3                        9848 │
│ trial_ffa4e    RUNNING               424             7                    42                   26   0.3                         533 │
│ trial_ffa4e    RUNNING               285             7                    21                   16   0.3                        5746 │
│ trial_ffa4e    RUNNING               226             5                    44                   28   sqrt                       5390 │
│ trial_ffa4e    RUNNING               314             5                    37                   28   sqrt                       9771 │
│ trial_ffa4e    RUNNING               274             6                    34                   23   0.3                        6780 │
│ trial_ffa4e    RUNNING               430             6                    55                   18   sqrt                       7897 │
│ trial_ffa4e    RUNNING               291             5                    51                   10   0.3                        1519 │
│ trial_ffa4e    RUNNING               392             5                    52                   28   sqrt                        218 │
│ trial_ffa4e    RUNNING               370             6                    23                   22   sqrt                       1875 │
│ trial_ffa4e    RUNNING               425             6                    56                   12   0.3                        6581 │
│ trial_ffa4e    RUNNING               451             5                    28                   16   0.3                        2847 │
│ trial_ffa4e    RUNNING               267             5                    49                   28   sqrt                       9942 │
│ trial_ffa4e    RUNNING               320             7                    27                   24   0.3                        3764 │
│ trial_ffa4e    RUNNING               495             5                    39                   20   sqrt                        440 │
│ trial_ffa4e    RUNNING               483             7                    32                   19   0.3                         407 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:23:48. Total running time: 36s
[36m(train_brf_ray_tune pid=2314635)[0m [Parallel(n_jobs=-1)]: Done 265 out of 265 | elapsed:   30.2s finished
[36m(train_brf_ray_tune pid=2314635)[0m [Parallel(n_jobs=20)]: Done 265 out of 265 | elapsed:    0.5s finished
[36m(train_brf_ray_tune pid=2314635)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=2314635)[0m [Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.4s[32m [repeated 8x across cluster][0m
[36m(train_brf_ray_tune pid=2314635)[0m [Parallel(n_jobs=20)]: Done 265 out of 265 | elapsed:    0.6s finished
[36m(train_brf_ray_tune pid=2314635)[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=2314635)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=2314670)[0m [Parallel(n_jobs=20)]: Done 314 out of 314 | elapsed:    0.4s finished[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=2314670)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=2314670)[0m [Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.6s[32m [repeated 7x across cluster][0m
[36m(train_brf_ray_tune pid=2314670)[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=2314670)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=2314605)[0m [Parallel(n_jobs=-1)]: Done 237 out of 237 | elapsed:   43.1s finished[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=2314630)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=2314646)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   41.5s[32m [repeated 8x across cluster][0m
[36m(train_brf_ray_tune pid=2314671)[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=2314671)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=2314647)[0m [Parallel(n_jobs=-1)]: Done 370 out of 370 | elapsed:   46.9s finished[32m [repeated 10x across cluster][0m
[36m(train_brf_ray_tune pid=2314647)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 7x across cluster][0m
[36m(train_brf_ray_tune pid=2314678)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.6s[32m [repeated 17x across cluster][0m
[36m(train_brf_ray_tune pid=2314678)[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=2314678)[0m   _log_deprecation_warning([32m [repeated 3x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             33.5547 │
│ time_total_s                 33.5547 │
│ training_iteration                 1 │
│ test_accuracy                 0.4125 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:23:48. Total running time: 36s

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:23:51. Total running time: 39s
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             37.0574 │
│ time_total_s                 37.0574 │
│ training_iteration                 1 │
│ test_accuracy                0.44282 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:23:51. Total running time: 39s

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:23:57. Total running time: 45s
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             42.5816 │
│ time_total_s                 42.5816 │
│ training_iteration                 1 │
│ test_accuracy                0.41359 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:23:57. Total running time: 45s

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:24:03. Total running time: 51s
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             48.9077 │
│ time_total_s                 48.9077 │
│ training_iteration                 1 │
│ test_accuracy                0.42821 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:24:03. Total running time: 51s

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:24:03. Total running time: 52s
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             48.7398 │
│ time_total_s                 48.7398 │
│ training_iteration                 1 │
│ test_accuracy                0.41323 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:24:03. Total running time: 52s

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:24:04. Total running time: 52s
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             49.8143 │
│ time_total_s                 49.8143 │
│ training_iteration                 1 │
│ test_accuracy                 0.4114 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:24:04. Total running time: 52s

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:24:04. Total running time: 53s
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             50.0021 │
│ time_total_s                 50.0021 │
│ training_iteration                 1 │
│ test_accuracy                0.41651 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:24:05. Total running time: 53s

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:24:09. Total running time: 57s
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             54.4599 │
│ time_total_s                 54.4599 │
│ training_iteration                 1 │
│ test_accuracy                0.43186 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:24:09. Total running time: 57s

Trial status: 8 TERMINATED | 12 RUNNING
Current time: 2025-11-07 09:24:11. Total running time: 1min 0s
Logical resource usage: 12.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
[36m(train_brf_ray_tune pid=2314677)[0m [Parallel(n_jobs=-1)]: Done 430 out of 430 | elapsed:   51.2s finished[32m [repeated 5x across cluster][0m
[36m(train_brf_ray_tune pid=2314647)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=2314647)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.4s[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=2314637)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=2314647)[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=2314647)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=2314661)[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=2314661)[0m   _log_deprecation_warning(
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         n_estimators     max_depth     min_samples_split     min_samples_leaf   max_features       random_state     iter     total time (s)     test_accuracy │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_ffa4e    RUNNING                 386             5                    33                   15   0.3                        3764                                               │
│ trial_ffa4e    RUNNING                 360             6                    44                   17   0.3                        9848                                               │
│ trial_ffa4e    RUNNING                 424             7                    42                   26   0.3                         533                                               │
│ trial_ffa4e    RUNNING                 285             7                    21                   16   0.3                        5746                                               │
│ trial_ffa4e    RUNNING                 274             6                    34                   23   0.3                        6780                                               │
│ trial_ffa4e    RUNNING                 430             6                    55                   18   sqrt                       7897                                               │
│ trial_ffa4e    RUNNING                 291             5                    51                   10   0.3                        1519                                               │
│ trial_ffa4e    RUNNING                 425             6                    56                   12   0.3                        6581                                               │
│ trial_ffa4e    RUNNING                 451             5                    28                   16   0.3                        2847                                               │
│ trial_ffa4e    RUNNING                 320             7                    27                   24   0.3                        3764                                               │
│ trial_ffa4e    RUNNING                 495             5                    39                   20   sqrt                        440                                               │
│ trial_ffa4e    RUNNING                 483             7                    32                   19   0.3                         407                                               │
│ trial_ffa4e    TERMINATED              425             5                    31                   15   sqrt                       8734        1            48.9077          0.428206 │
│ trial_ffa4e    TERMINATED              237             5                    55                   13   0.3                          13        1            49.8143          0.411399 │
│ trial_ffa4e    TERMINATED              265             6                    28                   16   sqrt                       8896        1            37.0574          0.442821 │
│ trial_ffa4e    TERMINATED              226             5                    44                   28   sqrt                       5390        1            33.5547          0.412495 │
│ trial_ffa4e    TERMINATED              314             5                    37                   28   sqrt                       9771        1            42.5816          0.413592 │
│ trial_ffa4e    TERMINATED              392             5                    52                   28   sqrt                        218        1            48.7398          0.413226 │
│ trial_ffa4e    TERMINATED              370             6                    23                   22   sqrt                       1875        1            54.4599          0.43186  │
│ trial_ffa4e    TERMINATED              267             5                    49                   28   sqrt                       9942        1            50.0021          0.416514 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:24:14. Total running time: 1min 2s
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             59.3922 │
│ time_total_s                 59.3922 │
│ training_iteration                 1 │
│ test_accuracy                0.41213 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:24:14. Total running time: 1min 2s

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:24:14. Total running time: 1min 2s
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             59.6989 │
│ time_total_s                 59.6989 │
│ training_iteration                 1 │
│ test_accuracy                0.42674 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:24:14. Total running time: 1min 2s

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:24:15. Total running time: 1min 3s
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             60.0467 │
│ time_total_s                 60.0467 │
│ training_iteration                 1 │
│ test_accuracy                0.41798 │
╰──────────────────────────────────────╯
Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:24:15. Total running time: 1min 3s
[36m(train_brf_ray_tune pid=2314660)[0m [Parallel(n_jobs=20)]: Done 320 out of 320 | elapsed:    0.3s finished[32m [repeated 16x across cluster][0m
[36m(train_brf_ray_tune pid=2314658)[0m [Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.2s[32m [repeated 27x across cluster][0m
[36m(train_brf_ray_tune pid=2314658)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 13x across cluster][0m
[36m(train_brf_ray_tune pid=2314658)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 6x across cluster][0m
[36m(train_brf_ray_tune pid=2314658)[0m   _log_deprecation_warning([32m [repeated 6x across cluster][0m
2025-11-07 09:24:22,144	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_17_classes/BalancedRF_hyperparameters_tuning' in 0.0113s.
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              60.129 │
│ time_total_s                  60.129 │
│ training_iteration                 1 │
│ test_accuracy                0.43844 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:24:15. Total running time: 1min 3s
Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:24:15. Total running time: 1min 3s

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:24:16. Total running time: 1min 4s
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             61.8883 │
│ time_total_s                 61.8883 │
│ training_iteration                 1 │
│ test_accuracy                0.41432 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:24:16. Total running time: 1min 4s

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:24:17. Total running time: 1min 5s
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             62.7564 │
│ time_total_s                 62.7564 │
│ training_iteration                 1 │
│ test_accuracy                0.43624 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:24:17. Total running time: 1min 5s

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:24:18. Total running time: 1min 6s
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             63.5539 │
│ time_total_s                 63.5539 │
│ training_iteration                 1 │
│ test_accuracy                0.43442 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:24:18. Total running time: 1min 6s

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:24:19. Total running time: 1min 8s
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             65.3198 │
│ time_total_s                 65.3198 │
│ training_iteration                 1 │
│ test_accuracy                0.42528 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:24:19. Total running time: 1min 8s

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:24:19. Total running time: 1min 8s
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             65.4223 │
│ time_total_s                 65.4223 │
│ training_iteration                 1 │
│ test_accuracy                0.41432 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:24:19. Total running time: 1min 8s

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:24:20. Total running time: 1min 9s
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             66.2156 │
│ time_total_s                 66.2156 │
│ training_iteration                 1 │
│ test_accuracy                0.40848 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:24:20. Total running time: 1min 9s

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:24:21. Total running time: 1min 9s
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s               66.85 │
│ time_total_s                   66.85 │
│ training_iteration                 1 │
│ test_accuracy                0.44319 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:24:21. Total running time: 1min 9s

Trial trial_ffa4e finished iteration 1 at 2025-11-07 09:24:22. Total running time: 1min 10s
╭──────────────────────────────────────╮
│ Trial trial_ffa4e result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             67.4273 │
│ time_total_s                 67.4273 │
│ training_iteration                 1 │
│ test_accuracy                0.44063 │
╰──────────────────────────────────────╯

Trial trial_ffa4e completed after 1 iterations at 2025-11-07 09:24:22. Total running time: 1min 10s

Trial status: 20 TERMINATED
Current time: 2025-11-07 09:24:22. Total running time: 1min 10s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.7s 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 424 out of 424 | elapsed:    0.1s finished
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         n_estimators     max_depth     min_samples_split     min_samples_leaf   max_features       random_state     iter     total time (s)     test_accuracy │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_ffa4e    TERMINATED              425             5                    31                   15   sqrt                       8734        1            48.9077          0.428206 │
│ trial_ffa4e    TERMINATED              386             5                    33                   15   0.3                        3764        1            61.8883          0.414322 │
│ trial_ffa4e    TERMINATED              237             5                    55                   13   0.3                          13        1            49.8143          0.411399 │
│ trial_ffa4e    TERMINATED              265             6                    28                   16   sqrt                       8896        1            37.0574          0.442821 │
│ trial_ffa4e    TERMINATED              360             6                    44                   17   0.3                        9848        1            65.3198          0.425283 │
│ trial_ffa4e    TERMINATED              424             7                    42                   26   0.3                         533        1            66.85            0.443186 │
│ trial_ffa4e    TERMINATED              285             7                    21                   16   0.3                        5746        1            63.5539          0.434417 │
│ trial_ffa4e    TERMINATED              226             5                    44                   28   sqrt                       5390        1            33.5547          0.412495 │
│ trial_ffa4e    TERMINATED              314             5                    37                   28   sqrt                       9771        1            42.5816          0.413592 │
│ trial_ffa4e    TERMINATED              274             6                    34                   23   0.3                        6780        1            60.0467          0.417976 │
│ trial_ffa4e    TERMINATED              430             6                    55                   18   sqrt                       7897        1            60.1291          0.438436 │
│ trial_ffa4e    TERMINATED              291             5                    51                   10   0.3                        1519        1            59.3922          0.41213  │
│ trial_ffa4e    TERMINATED              392             5                    52                   28   sqrt                        218        1            48.7398          0.413226 │
│ trial_ffa4e    TERMINATED              370             6                    23                   22   sqrt                       1875        1            54.4599          0.43186  │
│ trial_ffa4e    TERMINATED              425             6                    56                   12   0.3                        6581        1            66.2157          0.408476 │
│ trial_ffa4e    TERMINATED              451             5                    28                   16   0.3                        2847        1            65.4222          0.414322 │
│ trial_ffa4e    TERMINATED              267             5                    49                   28   sqrt                       9942        1            50.0021          0.416514 │
│ trial_ffa4e    TERMINATED              320             7                    27                   24   0.3                        3764        1            62.7564          0.436244 │
│ trial_ffa4e    TERMINATED              495             5                    39                   20   sqrt                        440        1            59.6989          0.426745 │
│ trial_ffa4e    TERMINATED              483             7                    32                   19   0.3                         407        1            67.4273          0.440628 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'n_estimators': 424, 'max_depth': 7, 'min_samples_split': 42, 'min_samples_leaf': 26, 'max_features': 0.3, 'random_state': 533}
Saved model to disk
[36m(train_brf_ray_tune pid=2314646)[0m [Parallel(n_jobs=20)]: Done 483 out of 483 | elapsed:    0.1s finished[32m [repeated 17x across cluster][0m
[36m(train_brf_ray_tune pid=2314646)[0m [Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s[32m [repeated 29x across cluster][0m
[36m(train_brf_ray_tune pid=2314646)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 10x across cluster][0m
[36m(train_brf_ray_tune pid=2314646)[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=2314646)[0m   _log_deprecation_warning([32m [repeated 5x across cluster][0m
2025-11-07 09:24:42.869953: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:24:42.881090: 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 00:00:1762503882.894038 2318591 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762503882.898080 2318591 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762503882.908013 2318591 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762503882.908030 2318591 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762503882.908033 2318591 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762503882.908034 2318591 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:24:42.911097: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.8s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:25:10.705260: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:25:10.716621: 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 00:00:1762503910.729933 2319085 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762503910.734004 2319085 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762503910.743981 2319085 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762503910.744011 2319085 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762503910.744013 2319085 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762503910.744014 2319085 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:25:10.747083: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.5s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:25:38.346620: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:25:38.357739: 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 00:00:1762503938.370631 2319605 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762503938.374637 2319605 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762503938.384336 2319605 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762503938.384356 2319605 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762503938.384358 2319605 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762503938.384360 2319605 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:25:38.387402: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.8s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.5s 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 424 out of 424 | 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.66 [%]
Global accuracy score (test) = 42.75 [%]
Global F1 score (train) = 59.64 [%]
Global F1 score (test) = 41.17 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.17      0.11      0.14       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.49      0.37       184
       CAMINAR USUAL SPEED       0.13      0.02      0.04       184
            CAMINAR ZIGZAG       0.19      0.14      0.16       184
          DE PIE BARRIENDO       0.36      0.48      0.41       184
   DE PIE DOBLANDO TOALLAS       0.48      0.38      0.42       184
    DE PIE MOVIENDO LIBROS       0.42      0.47      0.44       184
          DE PIE USANDO PC       0.45      0.66      0.54       184
        FASE REPOSO CON K5       0.55      0.74      0.63       184
INCREMENTAL CICLOERGOMETRO       0.80      0.62      0.70       184
           SENTADO LEYENDO       0.41      0.29      0.34       184
         SENTADO USANDO PC       0.20      0.19      0.19       184
      SENTADO VIENDO LA TV       0.49      0.30      0.37       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.76      0.54       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.43      2737
                 macro avg       0.42      0.43      0.41      2737
              weighted avg       0.42      0.43      0.41      2737


Accuracy capturado en la ejecución 1: 42.75 [%]
F1-score capturado en la ejecución 1: 41.17 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.43 [%]
Global accuracy score (test) = 44.06 [%]
Global F1 score (train) = 59.35 [%]
Global F1 score (test) = 42.44 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.17      0.19       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.43      0.34       184
       CAMINAR USUAL SPEED       0.00      0.00      0.00       184
            CAMINAR ZIGZAG       0.23      0.17      0.20       184
          DE PIE BARRIENDO       0.33      0.47      0.39       184
   DE PIE DOBLANDO TOALLAS       0.47      0.36      0.41       184
    DE PIE MOVIENDO LIBROS       0.46      0.55      0.50       184
          DE PIE USANDO PC       0.45      0.66      0.54       184
        FASE REPOSO CON K5       0.60      0.77      0.67       184
INCREMENTAL CICLOERGOMETRO       0.79      0.63      0.70       184
           SENTADO LEYENDO       0.43      0.31      0.36       184
         SENTADO USANDO PC       0.24      0.21      0.22       184
      SENTADO VIENDO LA TV       0.52      0.33      0.41       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.78      0.54       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.43      0.44      0.42      2737
              weighted avg       0.42      0.44      0.42      2737


Accuracy capturado en la ejecución 2: 44.06 [%]
F1-score capturado en la ejecución 2: 42.44 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.43 [%]
Global accuracy score (test) = 43.99 [%]
Global F1 score (train) = 59.43 [%]
Global F1 score (test) = 42.72 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.13      0.16       184
 CAMINAR CON MÓVIL O LIBRO       0.28      0.52      0.36       184
       CAMINAR USUAL SPEED       0.33      0.09      0.14       184
            CAMINAR ZIGZAG       0.18      0.11      0.14       184
          DE PIE BARRIENDO       0.36      0.47      0.41       184
   DE PIE DOBLANDO TOALLAS       0.47      0.38      0.42       184
    DE PIE MOVIENDO LIBROS       0.45      0.53      0.49       184
          DE PIE USANDO PC       0.45      0.67      0.54       184
        FASE REPOSO CON K5       0.56      0.72      0.63       184
INCREMENTAL CICLOERGOMETRO       0.82      0.64      0.72       184
           SENTADO LEYENDO       0.44      0.33      0.38       184
         SENTADO USANDO PC       0.20      0.18      0.19       184
      SENTADO VIENDO LA TV       0.49      0.32      0.39       184
   SUBIR Y BAJAR ESCALERAS       0.46      0.76      0.57       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.45      0.44      0.43      2737
              weighted avg       0.44      0.44      0.42      2737


Accuracy capturado en la ejecución 3: 43.99 [%]
F1-score capturado en la ejecución 3: 42.72 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
2025-11-07 09:26:05.808935: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:26:05.820144: 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 00:00:1762503965.833282 2320088 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762503965.837276 2320088 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762503965.847059 2320088 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762503965.847078 2320088 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762503965.847081 2320088 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762503965.847083 2320088 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:26:05.850047: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.7s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:26:33.541903: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:26:33.553328: 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 00:00:1762503993.566835 2320596 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762503993.570904 2320596 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762503993.580903 2320596 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762503993.580921 2320596 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762503993.580923 2320596 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762503993.580924 2320596 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:26:33.583999: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.6s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:27:01.176318: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:27:01.187619: 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 00:00:1762504021.200809 2321083 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504021.204743 2321083 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504021.214459 2321083 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504021.214480 2321083 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504021.214482 2321083 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504021.214484 2321083 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:27:01.217548: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.6s 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 424 out of 424 | 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.85 [%]
Global accuracy score (test) = 44.32 [%]
Global F1 score (train) = 59.82 [%]
Global F1 score (test) = 42.84 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.14      0.16       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.47      0.36       184
       CAMINAR USUAL SPEED       0.22      0.03      0.06       184
            CAMINAR ZIGZAG       0.23      0.16      0.19       184
          DE PIE BARRIENDO       0.37      0.53      0.44       184
   DE PIE DOBLANDO TOALLAS       0.48      0.37      0.42       184
    DE PIE MOVIENDO LIBROS       0.46      0.53      0.49       184
          DE PIE USANDO PC       0.47      0.67      0.55       184
        FASE REPOSO CON K5       0.60      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.80      0.64      0.71       184
           SENTADO LEYENDO       0.43      0.33      0.38       184
         SENTADO USANDO PC       0.21      0.20      0.21       184
      SENTADO VIENDO LA TV       0.51      0.32      0.39       184
   SUBIR Y BAJAR ESCALERAS       0.41      0.76      0.53       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.45      0.43      2737
              weighted avg       0.44      0.44      0.42      2737


Accuracy capturado en la ejecución 4: 44.32 [%]
F1-score capturado en la ejecución 4: 42.84 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.34 [%]
Global accuracy score (test) = 43.77 [%]
Global F1 score (train) = 59.29 [%]
Global F1 score (test) = 42.37 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.14      0.17       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.52      0.39       184
       CAMINAR USUAL SPEED       0.26      0.05      0.09       184
            CAMINAR ZIGZAG       0.21      0.15      0.17       184
          DE PIE BARRIENDO       0.36      0.48      0.41       184
   DE PIE DOBLANDO TOALLAS       0.48      0.37      0.42       184
    DE PIE MOVIENDO LIBROS       0.44      0.51      0.47       184
          DE PIE USANDO PC       0.46      0.67      0.55       184
        FASE REPOSO CON K5       0.57      0.71      0.63       184
INCREMENTAL CICLOERGOMETRO       0.80      0.64      0.71       184
           SENTADO LEYENDO       0.39      0.26      0.31       184
         SENTADO USANDO PC       0.23      0.22      0.22       184
      SENTADO VIENDO LA TV       0.45      0.33      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.43      0.76      0.55       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.42      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 5: 43.77 [%]
F1-score capturado en la ejecución 5: 42.37 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.68 [%]
Global accuracy score (test) = 43.26 [%]
Global F1 score (train) = 59.7 [%]
Global F1 score (test) = 41.42 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.17      0.10      0.12       184
 CAMINAR CON MÓVIL O LIBRO       0.27      0.44      0.34       184
       CAMINAR USUAL SPEED       0.00      0.00      0.00       184
            CAMINAR ZIGZAG       0.22      0.16      0.19       184
          DE PIE BARRIENDO       0.34      0.51      0.41       184
   DE PIE DOBLANDO TOALLAS       0.50      0.38      0.43       184
    DE PIE MOVIENDO LIBROS       0.44      0.49      0.46       184
          DE PIE USANDO PC       0.46      0.67      0.54       184
        FASE REPOSO CON K5       0.58      0.77      0.66       184
INCREMENTAL CICLOERGOMETRO       0.80      0.62      0.70       184
           SENTADO LEYENDO       0.41      0.26      0.31       184
         SENTADO USANDO PC       0.23      0.21      0.22       184
      SENTADO VIENDO LA TV       0.53      0.34      0.42       184
   SUBIR Y BAJAR ESCALERAS       0.39      0.78      0.52       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.43      2737
                 macro avg       0.42      0.44      0.41      2737
              weighted avg       0.42      0.43      0.41      2737


Accuracy capturado en la ejecución 6: 43.26 [%]
F1-score capturado en la ejecución 6: 41.42 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
2025-11-07 09:27:28.970509: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:27:28.982266: 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 00:00:1762504048.995554 2321560 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504048.999618 2321560 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504049.010068 2321560 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504049.010088 2321560 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504049.010091 2321560 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504049.010092 2321560 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:27:29.013225: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.5s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:27:56.363496: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:27:56.375080: 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 00:00:1762504076.388395 2322062 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504076.392531 2322062 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504076.402341 2322062 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504076.402360 2322062 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504076.402362 2322062 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504076.402363 2322062 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:27:56.405695: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.7s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:28:24.131828: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:28:24.143021: 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 00:00:1762504104.156628 2322548 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504104.160815 2322548 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504104.170931 2322548 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504104.170952 2322548 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504104.170954 2322548 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504104.170956 2322548 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:28:24.174047: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.6s 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 424 out of 424 | 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.38 [%]
Global accuracy score (test) = 44.25 [%]
Global F1 score (train) = 59.33 [%]
Global F1 score (test) = 42.6 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.18      0.12      0.15       184
 CAMINAR CON MÓVIL O LIBRO       0.30      0.49      0.37       184
       CAMINAR USUAL SPEED       0.18      0.02      0.04       184
            CAMINAR ZIGZAG       0.21      0.15      0.17       184
          DE PIE BARRIENDO       0.37      0.52      0.43       184
   DE PIE DOBLANDO TOALLAS       0.46      0.36      0.40       184
    DE PIE MOVIENDO LIBROS       0.48      0.54      0.51       184
          DE PIE USANDO PC       0.46      0.68      0.55       184
        FASE REPOSO CON K5       0.59      0.72      0.65       184
INCREMENTAL CICLOERGOMETRO       0.80      0.64      0.71       184
           SENTADO LEYENDO       0.44      0.31      0.36       184
         SENTADO USANDO PC       0.23      0.21      0.22       184
      SENTADO VIENDO LA TV       0.49      0.33      0.39       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.77      0.54       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.45      0.43      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 7: 44.25 [%]
F1-score capturado en la ejecución 7: 42.6 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.72 [%]
Global accuracy score (test) = 44.46 [%]
Global F1 score (train) = 59.75 [%]
Global F1 score (test) = 43.22 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.15      0.18       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.50      0.37       184
       CAMINAR USUAL SPEED       0.23      0.05      0.08       184
            CAMINAR ZIGZAG       0.24      0.16      0.19       184
          DE PIE BARRIENDO       0.36      0.48      0.41       184
   DE PIE DOBLANDO TOALLAS       0.50      0.38      0.43       184
    DE PIE MOVIENDO LIBROS       0.44      0.51      0.47       184
          DE PIE USANDO PC       0.47      0.67      0.55       184
        FASE REPOSO CON K5       0.57      0.75      0.65       184
INCREMENTAL CICLOERGOMETRO       0.80      0.63      0.71       184
           SENTADO LEYENDO       0.45      0.31      0.37       184
         SENTADO USANDO PC       0.24      0.24      0.24       184
      SENTADO VIENDO LA TV       0.50      0.33      0.40       184
   SUBIR Y BAJAR ESCALERAS       0.45      0.76      0.56       184
                    TROTAR       0.98      0.80      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.45      0.45      0.43      2737
              weighted avg       0.44      0.44      0.43      2737


Accuracy capturado en la ejecución 8: 44.46 [%]
F1-score capturado en la ejecución 8: 43.22 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.5 [%]
Global accuracy score (test) = 42.71 [%]
Global F1 score (train) = 59.51 [%]
Global F1 score (test) = 41.26 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.12      0.08      0.09       184
 CAMINAR CON MÓVIL O LIBRO       0.28      0.49      0.35       184
       CAMINAR USUAL SPEED       0.22      0.05      0.09       184
            CAMINAR ZIGZAG       0.19      0.12      0.15       184
          DE PIE BARRIENDO       0.34      0.47      0.39       184
   DE PIE DOBLANDO TOALLAS       0.49      0.37      0.42       184
    DE PIE MOVIENDO LIBROS       0.42      0.44      0.43       184
          DE PIE USANDO PC       0.45      0.68      0.55       184
        FASE REPOSO CON K5       0.53      0.74      0.62       184
INCREMENTAL CICLOERGOMETRO       0.81      0.63      0.71       184
           SENTADO LEYENDO       0.43      0.31      0.36       184
         SENTADO USANDO PC       0.21      0.20      0.21       184
      SENTADO VIENDO LA TV       0.52      0.32      0.39       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.74      0.54       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.43      2737
                 macro avg       0.43      0.43      0.41      2737
              weighted avg       0.42      0.43      0.41      2737


Accuracy capturado en la ejecución 9: 42.71 [%]
F1-score capturado en la ejecución 9: 41.26 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
2025-11-07 09:28:51.787566: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:28:51.798801: 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 00:00:1762504131.811740 2323047 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504131.815847 2323047 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504131.825675 2323047 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504131.825694 2323047 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504131.825696 2323047 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504131.825698 2323047 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:28:51.828801: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.9s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:29:19.718026: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:29:19.729988: 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 00:00:1762504159.744076 2323548 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504159.748385 2323548 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504159.758951 2323548 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504159.758976 2323548 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504159.758978 2323548 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504159.758979 2323548 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:29:19.762211: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.7s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:29:47.290947: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:29:47.302337: 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 00:00:1762504187.315956 2324054 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504187.320091 2324054 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504187.330368 2324054 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504187.330387 2324054 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504187.330389 2324054 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504187.330399 2324054 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:29:47.333532: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.6s 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 424 out of 424 | 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.61 [%]
Global accuracy score (test) = 43.84 [%]
Global F1 score (train) = 59.58 [%]
Global F1 score (test) = 42.02 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.19      0.12      0.15       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.47      0.36       184
       CAMINAR USUAL SPEED       0.05      0.01      0.01       184
            CAMINAR ZIGZAG       0.23      0.16      0.19       184
          DE PIE BARRIENDO       0.35      0.48      0.41       184
   DE PIE DOBLANDO TOALLAS       0.48      0.36      0.41       184
    DE PIE MOVIENDO LIBROS       0.45      0.53      0.49       184
          DE PIE USANDO PC       0.47      0.68      0.55       184
        FASE REPOSO CON K5       0.60      0.77      0.67       184
INCREMENTAL CICLOERGOMETRO       0.79      0.64      0.70       184
           SENTADO LEYENDO       0.39      0.26      0.31       184
         SENTADO USANDO PC       0.22      0.22      0.22       184
      SENTADO VIENDO LA TV       0.48      0.34      0.40       184
   SUBIR Y BAJAR ESCALERAS       0.41      0.79      0.54       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.43      0.44      0.42      2737
              weighted avg       0.42      0.44      0.42      2737


Accuracy capturado en la ejecución 10: 43.84 [%]
F1-score capturado en la ejecución 10: 42.02 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.33 [%]
Global accuracy score (test) = 44.1 [%]
Global F1 score (train) = 59.29 [%]
Global F1 score (test) = 42.61 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.11      0.06      0.08       184
 CAMINAR CON MÓVIL O LIBRO       0.27      0.42      0.33       184
       CAMINAR USUAL SPEED       0.26      0.04      0.07       184
            CAMINAR ZIGZAG       0.23      0.18      0.20       184
          DE PIE BARRIENDO       0.34      0.48      0.40       184
   DE PIE DOBLANDO TOALLAS       0.47      0.36      0.41       184
    DE PIE MOVIENDO LIBROS       0.45      0.54      0.49       184
          DE PIE USANDO PC       0.47      0.67      0.56       184
        FASE REPOSO CON K5       0.61      0.76      0.67       184
INCREMENTAL CICLOERGOMETRO       0.79      0.62      0.70       184
           SENTADO LEYENDO       0.49      0.38      0.43       184
         SENTADO USANDO PC       0.24      0.22      0.23       184
      SENTADO VIENDO LA TV       0.54      0.34      0.42       184
   SUBIR Y BAJAR ESCALERAS       0.39      0.77      0.52       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.43      2737
              weighted avg       0.44      0.44      0.42      2737


Accuracy capturado en la ejecución 11: 44.1 [%]
F1-score capturado en la ejecución 11: 42.61 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.47 [%]
Global accuracy score (test) = 43.51 [%]
Global F1 score (train) = 59.39 [%]
Global F1 score (test) = 42.19 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.17      0.19       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.49      0.37       184
       CAMINAR USUAL SPEED       0.21      0.02      0.04       184
            CAMINAR ZIGZAG       0.23      0.18      0.20       184
          DE PIE BARRIENDO       0.31      0.48      0.38       184
   DE PIE DOBLANDO TOALLAS       0.51      0.38      0.43       184
    DE PIE MOVIENDO LIBROS       0.44      0.51      0.47       184
          DE PIE USANDO PC       0.46      0.66      0.54       184
        FASE REPOSO CON K5       0.60      0.72      0.66       184
INCREMENTAL CICLOERGOMETRO       0.82      0.64      0.72       184
           SENTADO LEYENDO       0.37      0.26      0.30       184
         SENTADO USANDO PC       0.21      0.20      0.20       184
      SENTADO VIENDO LA TV       0.49      0.30      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.45      0.76      0.56       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.42      2737
              weighted avg       0.44      0.44      0.42      2737


Accuracy capturado en la ejecución 12: 43.51 [%]
F1-score capturado en la ejecución 12: 42.19 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
2025-11-07 09:30:14.950818: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:30:14.962119: 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 00:00:1762504214.975778 2324542 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504214.979927 2324542 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504214.989750 2324542 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504214.989770 2324542 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504214.989772 2324542 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504214.989773 2324542 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:30:14.992876: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.7s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:30:42.676974: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:30:42.688387: 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 00:00:1762504242.701790 2325050 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504242.705779 2325050 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504242.715583 2325050 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504242.715605 2325050 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504242.715608 2325050 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504242.715609 2325050 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:30:42.718650: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.7s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:31:10.309134: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:31:10.320376: 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 00:00:1762504270.333466 2325529 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504270.337609 2325529 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504270.347472 2325529 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504270.347503 2325529 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504270.347506 2325529 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504270.347507 2325529 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:31:10.350630: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.7s 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 424 out of 424 | 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.36 [%]
Global accuracy score (test) = 43.59 [%]
Global F1 score (train) = 59.3 [%]
Global F1 score (test) = 41.87 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.17      0.10      0.12       184
 CAMINAR CON MÓVIL O LIBRO       0.30      0.49      0.37       184
       CAMINAR USUAL SPEED       0.04      0.01      0.01       184
            CAMINAR ZIGZAG       0.23      0.17      0.20       184
          DE PIE BARRIENDO       0.35      0.47      0.40       184
   DE PIE DOBLANDO TOALLAS       0.46      0.38      0.41       184
    DE PIE MOVIENDO LIBROS       0.44      0.53      0.48       184
          DE PIE USANDO PC       0.47      0.66      0.55       184
        FASE REPOSO CON K5       0.59      0.75      0.66       184
INCREMENTAL CICLOERGOMETRO       0.80      0.63      0.71       184
           SENTADO LEYENDO       0.39      0.26      0.31       184
         SENTADO USANDO PC       0.21      0.22      0.22       184
      SENTADO VIENDO LA TV       0.54      0.33      0.41       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.78      0.54       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.43      0.44      0.42      2737
              weighted avg       0.42      0.44      0.41      2737


Accuracy capturado en la ejecución 13: 43.59 [%]
F1-score capturado en la ejecución 13: 41.87 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 60.01 [%]
Global accuracy score (test) = 43.73 [%]
Global F1 score (train) = 60.02 [%]
Global F1 score (test) = 42.35 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.14      0.17       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.47      0.37       184
       CAMINAR USUAL SPEED       0.20      0.05      0.08       184
            CAMINAR ZIGZAG       0.24      0.17      0.20       184
          DE PIE BARRIENDO       0.36      0.48      0.41       184
   DE PIE DOBLANDO TOALLAS       0.47      0.36      0.41       184
    DE PIE MOVIENDO LIBROS       0.44      0.53      0.48       184
          DE PIE USANDO PC       0.46      0.67      0.55       184
        FASE REPOSO CON K5       0.59      0.71      0.65       184
INCREMENTAL CICLOERGOMETRO       0.80      0.64      0.71       184
           SENTADO LEYENDO       0.37      0.26      0.30       184
         SENTADO USANDO PC       0.22      0.21      0.22       184
      SENTADO VIENDO LA TV       0.46      0.32      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.78      0.54       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.42      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 14: 43.73 [%]
F1-score capturado en la ejecución 14: 42.35 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.45 [%]
Global accuracy score (test) = 43.95 [%]
Global F1 score (train) = 59.39 [%]
Global F1 score (test) = 42.56 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.18      0.13      0.15       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.51      0.38       184
       CAMINAR USUAL SPEED       0.22      0.04      0.07       184
            CAMINAR ZIGZAG       0.26      0.22      0.24       184
          DE PIE BARRIENDO       0.33      0.48      0.39       184
   DE PIE DOBLANDO TOALLAS       0.50      0.36      0.42       184
    DE PIE MOVIENDO LIBROS       0.45      0.54      0.49       184
          DE PIE USANDO PC       0.46      0.67      0.54       184
        FASE REPOSO CON K5       0.58      0.76      0.66       184
INCREMENTAL CICLOERGOMETRO       0.79      0.63      0.70       184
           SENTADO LEYENDO       0.36      0.26      0.30       184
         SENTADO USANDO PC       0.22      0.20      0.21       184
      SENTADO VIENDO LA TV       0.48      0.29      0.36       184
   SUBIR Y BAJAR ESCALERAS       0.47      0.75      0.58       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.43      2737
              weighted avg       0.44      0.44      0.42      2737


Accuracy capturado en la ejecución 15: 43.95 [%]
F1-score capturado en la ejecución 15: 42.56 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
2025-11-07 09:31:37.972482: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:31:37.983774: 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 00:00:1762504297.996970 2326031 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504298.001177 2326031 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504298.011482 2326031 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504298.011506 2326031 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504298.011509 2326031 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504298.011510 2326031 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:31:38.014715: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.7s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:32:05.597940: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:32:05.609312: 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 00:00:1762504325.622431 2326521 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504325.626561 2326521 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504325.636377 2326521 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504325.636396 2326521 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504325.636400 2326521 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504325.636403 2326521 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:32:05.639633: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.6s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:32:33.174402: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:32:33.185436: 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 00:00:1762504353.198332 2327022 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504353.202260 2327022 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504353.212001 2327022 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504353.212020 2327022 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504353.212022 2327022 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504353.212023 2327022 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:32:33.215148: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.7s 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 424 out of 424 | 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.7 [%]
Global accuracy score (test) = 43.66 [%]
Global F1 score (train) = 59.72 [%]
Global F1 score (test) = 42.37 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.18      0.14      0.16       184
 CAMINAR CON MÓVIL O LIBRO       0.28      0.42      0.34       184
       CAMINAR USUAL SPEED       0.17      0.03      0.05       184
            CAMINAR ZIGZAG       0.24      0.17      0.20       184
          DE PIE BARRIENDO       0.36      0.49      0.41       184
   DE PIE DOBLANDO TOALLAS       0.49      0.36      0.42       184
    DE PIE MOVIENDO LIBROS       0.42      0.48      0.45       184
          DE PIE USANDO PC       0.46      0.67      0.55       184
        FASE REPOSO CON K5       0.57      0.74      0.64       184
INCREMENTAL CICLOERGOMETRO       0.80      0.64      0.71       184
           SENTADO LEYENDO       0.44      0.33      0.38       184
         SENTADO USANDO PC       0.22      0.21      0.21       184
      SENTADO VIENDO LA TV       0.52      0.33      0.40       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.77      0.55       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.42      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 16: 43.66 [%]
F1-score capturado en la ejecución 16: 42.37 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.35 [%]
Global accuracy score (test) = 43.11 [%]
Global F1 score (train) = 59.33 [%]
Global F1 score (test) = 41.49 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.15      0.18       184
 CAMINAR CON MÓVIL O LIBRO       0.28      0.43      0.34       184
       CAMINAR USUAL SPEED       0.00      0.00      0.00       184
            CAMINAR ZIGZAG       0.23      0.15      0.18       184
          DE PIE BARRIENDO       0.35      0.49      0.41       184
   DE PIE DOBLANDO TOALLAS       0.48      0.38      0.42       184
    DE PIE MOVIENDO LIBROS       0.46      0.52      0.49       184
          DE PIE USANDO PC       0.46      0.67      0.54       184
        FASE REPOSO CON K5       0.56      0.71      0.63       184
INCREMENTAL CICLOERGOMETRO       0.80      0.64      0.71       184
           SENTADO LEYENDO       0.40      0.29      0.34       184
         SENTADO USANDO PC       0.22      0.21      0.21       184
      SENTADO VIENDO LA TV       0.45      0.29      0.36       184
   SUBIR Y BAJAR ESCALERAS       0.40      0.77      0.53       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.43      2737
                 macro avg       0.42      0.43      0.41      2737
              weighted avg       0.41      0.43      0.41      2737


Accuracy capturado en la ejecución 17: 43.11 [%]
F1-score capturado en la ejecución 17: 41.49 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.47 [%]
Global accuracy score (test) = 44.06 [%]
Global F1 score (train) = 59.4 [%]
Global F1 score (test) = 42.38 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.12      0.15       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.49      0.36       184
       CAMINAR USUAL SPEED       0.00      0.00      0.00       184
            CAMINAR ZIGZAG       0.21      0.15      0.17       184
          DE PIE BARRIENDO       0.37      0.49      0.42       184
   DE PIE DOBLANDO TOALLAS       0.48      0.38      0.42       184
    DE PIE MOVIENDO LIBROS       0.46      0.55      0.50       184
          DE PIE USANDO PC       0.46      0.64      0.54       184
        FASE REPOSO CON K5       0.59      0.74      0.66       184
INCREMENTAL CICLOERGOMETRO       0.80      0.63      0.71       184
           SENTADO LEYENDO       0.44      0.33      0.38       184
         SENTADO USANDO PC       0.21      0.21      0.21       184
      SENTADO VIENDO LA TV       0.50      0.32      0.39       184
   SUBIR Y BAJAR ESCALERAS       0.43      0.78      0.56       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.43      0.44      0.42      2737
              weighted avg       0.42      0.44      0.42      2737


Accuracy capturado en la ejecución 18: 44.06 [%]
F1-score capturado en la ejecución 18: 42.38 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
2025-11-07 09:33:00.854875: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:33:00.866169: 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 00:00:1762504380.879400 2327509 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504380.883506 2327509 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504380.893527 2327509 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504380.893549 2327509 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504380.893551 2327509 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504380.893552 2327509 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:33:00.896654: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.6s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:33:28.634590: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:33:28.645907: 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 00:00:1762504408.658800 2327987 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504408.662783 2327987 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504408.672600 2327987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504408.672621 2327987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504408.672623 2327987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504408.672626 2327987 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:33:28.675613: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.6s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:33:56.481501: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:33:56.492785: 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 00:00:1762504436.505930 2328495 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504436.510099 2328495 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504436.520078 2328495 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504436.520097 2328495 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504436.520099 2328495 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504436.520101 2328495 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:33:56.523323: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.7s 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 424 out of 424 | 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.58 [%]
Global accuracy score (test) = 43.59 [%]
Global F1 score (train) = 59.61 [%]
Global F1 score (test) = 42.07 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.13      0.16       184
 CAMINAR CON MÓVIL O LIBRO       0.28      0.48      0.35       184
       CAMINAR USUAL SPEED       0.13      0.02      0.04       184
            CAMINAR ZIGZAG       0.21      0.14      0.17       184
          DE PIE BARRIENDO       0.35      0.48      0.40       184
   DE PIE DOBLANDO TOALLAS       0.50      0.39      0.44       184
    DE PIE MOVIENDO LIBROS       0.45      0.54      0.49       184
          DE PIE USANDO PC       0.46      0.67      0.55       184
        FASE REPOSO CON K5       0.58      0.74      0.65       184
INCREMENTAL CICLOERGOMETRO       0.79      0.62      0.70       184
           SENTADO LEYENDO       0.40      0.27      0.32       184
         SENTADO USANDO PC       0.22      0.22      0.22       184
      SENTADO VIENDO LA TV       0.53      0.33      0.40       184
   SUBIR Y BAJAR ESCALERAS       0.41      0.74      0.53       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.43      0.44      0.42      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 19: 43.59 [%]
F1-score capturado en la ejecución 19: 42.07 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.62 [%]
Global accuracy score (test) = 44.54 [%]
Global F1 score (train) = 59.57 [%]
Global F1 score (test) = 43.05 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.18      0.21       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.47      0.37       184
       CAMINAR USUAL SPEED       0.17      0.02      0.04       184
            CAMINAR ZIGZAG       0.24      0.20      0.22       184
          DE PIE BARRIENDO       0.36      0.50      0.42       184
   DE PIE DOBLANDO TOALLAS       0.48      0.36      0.41       184
    DE PIE MOVIENDO LIBROS       0.44      0.47      0.46       184
          DE PIE USANDO PC       0.47      0.68      0.56       184
        FASE REPOSO CON K5       0.54      0.73      0.62       184
INCREMENTAL CICLOERGOMETRO       0.80      0.64      0.71       184
           SENTADO LEYENDO       0.46      0.36      0.40       184
         SENTADO USANDO PC       0.22      0.20      0.21       184
      SENTADO VIENDO LA TV       0.48      0.33      0.39       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.78      0.55       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.45      2737
                 macro avg       0.44      0.45      0.43      2737
              weighted avg       0.44      0.45      0.43      2737


Accuracy capturado en la ejecución 20: 44.54 [%]
F1-score capturado en la ejecución 20: 43.05 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.45 [%]
Global accuracy score (test) = 43.04 [%]
Global F1 score (train) = 59.43 [%]
Global F1 score (test) = 41.38 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.11      0.07      0.08       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.46      0.35       184
       CAMINAR USUAL SPEED       0.27      0.04      0.07       184
            CAMINAR ZIGZAG       0.19      0.13      0.16       184
          DE PIE BARRIENDO       0.36      0.47      0.41       184
   DE PIE DOBLANDO TOALLAS       0.47      0.36      0.41       184
    DE PIE MOVIENDO LIBROS       0.44      0.52      0.48       184
          DE PIE USANDO PC       0.45      0.67      0.54       184
        FASE REPOSO CON K5       0.58      0.72      0.64       184
INCREMENTAL CICLOERGOMETRO       0.79      0.64      0.70       184
           SENTADO LEYENDO       0.42      0.26      0.32       184
         SENTADO USANDO PC       0.23      0.22      0.22       184
      SENTADO VIENDO LA TV       0.46      0.35      0.40       184
   SUBIR Y BAJAR ESCALERAS       0.40      0.78      0.52       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.43      2737
                 macro avg       0.43      0.43      0.41      2737
              weighted avg       0.42      0.43      0.41      2737


Accuracy capturado en la ejecución 21: 43.04 [%]
F1-score capturado en la ejecución 21: 41.38 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
2025-11-07 09:34:24.203621: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:34:24.215060: 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 00:00:1762504464.228090 2328994 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504464.232151 2328994 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504464.241918 2328994 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504464.241935 2328994 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504464.241937 2328994 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504464.241938 2328994 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:34:24.244918: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.7s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:34:52.047818: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:34:52.058987: 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 00:00:1762504492.072074 2329494 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504492.076035 2329494 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504492.086003 2329494 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504492.086021 2329494 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504492.086024 2329494 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504492.086026 2329494 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:34:52.089116: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.7s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:35:19.868356: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:35:19.880021: 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 00:00:1762504519.893870 2329984 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504519.898039 2329984 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504519.908149 2329984 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504519.908169 2329984 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504519.908171 2329984 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504519.908172 2329984 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:35:19.911328: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.7s 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 424 out of 424 | 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.72 [%]
Global accuracy score (test) = 43.55 [%]
Global F1 score (train) = 59.7 [%]
Global F1 score (test) = 41.91 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.18      0.11      0.14       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.47      0.36       184
       CAMINAR USUAL SPEED       0.16      0.03      0.05       184
            CAMINAR ZIGZAG       0.23      0.16      0.19       184
          DE PIE BARRIENDO       0.35      0.50      0.41       184
   DE PIE DOBLANDO TOALLAS       0.51      0.36      0.43       184
    DE PIE MOVIENDO LIBROS       0.44      0.53      0.48       184
          DE PIE USANDO PC       0.46      0.67      0.55       184
        FASE REPOSO CON K5       0.58      0.76      0.66       184
INCREMENTAL CICLOERGOMETRO       0.80      0.64      0.71       184
           SENTADO LEYENDO       0.38      0.26      0.31       184
         SENTADO USANDO PC       0.21      0.20      0.20       184
      SENTADO VIENDO LA TV       0.45      0.32      0.37       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.76      0.54       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.43      0.44      0.42      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 22: 43.55 [%]
F1-score capturado en la ejecución 22: 41.91 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.63 [%]
Global accuracy score (test) = 43.22 [%]
Global F1 score (train) = 59.62 [%]
Global F1 score (test) = 41.84 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.24      0.18      0.20       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.45      0.35       184
       CAMINAR USUAL SPEED       0.15      0.02      0.04       184
            CAMINAR ZIGZAG       0.23      0.18      0.21       184
          DE PIE BARRIENDO       0.35      0.47      0.40       184
   DE PIE DOBLANDO TOALLAS       0.46      0.38      0.42       184
    DE PIE MOVIENDO LIBROS       0.43      0.46      0.45       184
          DE PIE USANDO PC       0.46      0.67      0.55       184
        FASE REPOSO CON K5       0.54      0.71      0.61       184
INCREMENTAL CICLOERGOMETRO       0.79      0.63      0.70       184
           SENTADO LEYENDO       0.39      0.26      0.31       184
         SENTADO USANDO PC       0.21      0.21      0.21       184
      SENTADO VIENDO LA TV       0.48      0.33      0.39       184
   SUBIR Y BAJAR ESCALERAS       0.42      0.77      0.54       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.43      2737
                 macro avg       0.43      0.44      0.42      2737
              weighted avg       0.42      0.43      0.41      2737


Accuracy capturado en la ejecución 23: 43.22 [%]
F1-score capturado en la ejecución 23: 41.84 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.6 [%]
Global accuracy score (test) = 43.66 [%]
Global F1 score (train) = 59.65 [%]
Global F1 score (test) = 41.98 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.18      0.09      0.12       184
 CAMINAR CON MÓVIL O LIBRO       0.27      0.46      0.34       184
       CAMINAR USUAL SPEED       0.14      0.02      0.04       184
            CAMINAR ZIGZAG       0.23      0.15      0.18       184
          DE PIE BARRIENDO       0.35      0.47      0.40       184
   DE PIE DOBLANDO TOALLAS       0.47      0.38      0.42       184
    DE PIE MOVIENDO LIBROS       0.44      0.53      0.48       184
          DE PIE USANDO PC       0.46      0.67      0.55       184
        FASE REPOSO CON K5       0.59      0.74      0.66       184
INCREMENTAL CICLOERGOMETRO       0.81      0.64      0.71       184
           SENTADO LEYENDO       0.45      0.34      0.39       184
         SENTADO USANDO PC       0.20      0.18      0.19       184
      SENTADO VIENDO LA TV       0.52      0.33      0.40       184
   SUBIR Y BAJAR ESCALERAS       0.39      0.78      0.52       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.43      0.44      0.42      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 24: 43.66 [%]
F1-score capturado en la ejecución 24: 41.98 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
2025-11-07 09:35:47.742367: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:35:47.753768: 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 00:00:1762504547.767159 2330486 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504547.771359 2330486 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504547.781376 2330486 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504547.781395 2330486 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504547.781397 2330486 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504547.781399 2330486 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:35:47.784586: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.6s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:36:15.313327: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:36:15.324600: 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 00:00:1762504575.337623 2330992 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504575.341735 2330992 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504575.351402 2330992 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504575.351419 2330992 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504575.351422 2330992 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504575.351424 2330992 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:36:15.354716: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.6s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:36:43.211820: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:36:43.222884: 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 00:00:1762504603.235911 2331523 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504603.239921 2331523 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504603.249738 2331523 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504603.249756 2331523 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504603.249758 2331523 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504603.249760 2331523 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:36:43.252962: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.6s 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 424 out of 424 | 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.32 [%]
Global accuracy score (test) = 43.37 [%]
Global F1 score (train) = 59.24 [%]
Global F1 score (test) = 41.9 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.16      0.11      0.13       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.45      0.35       184
       CAMINAR USUAL SPEED       0.10      0.02      0.03       184
            CAMINAR ZIGZAG       0.23      0.16      0.19       184
          DE PIE BARRIENDO       0.32      0.48      0.39       184
   DE PIE DOBLANDO TOALLAS       0.48      0.37      0.42       184
    DE PIE MOVIENDO LIBROS       0.44      0.53      0.48       184
          DE PIE USANDO PC       0.46      0.66      0.54       184
        FASE REPOSO CON K5       0.62      0.76      0.68       184
INCREMENTAL CICLOERGOMETRO       0.81      0.64      0.71       184
           SENTADO LEYENDO       0.40      0.26      0.32       184
         SENTADO USANDO PC       0.20      0.19      0.19       184
      SENTADO VIENDO LA TV       0.55      0.36      0.43       184
   SUBIR Y BAJAR ESCALERAS       0.41      0.76      0.53       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.43      2737
                 macro avg       0.43      0.44      0.42      2737
              weighted avg       0.43      0.43      0.42      2737


Accuracy capturado en la ejecución 25: 43.37 [%]
F1-score capturado en la ejecución 25: 41.9 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.47 [%]
Global accuracy score (test) = 43.92 [%]
Global F1 score (train) = 59.4 [%]
Global F1 score (test) = 42.58 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.14      0.17       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.46      0.36       184
       CAMINAR USUAL SPEED       0.32      0.04      0.07       184
            CAMINAR ZIGZAG       0.25      0.20      0.22       184
          DE PIE BARRIENDO       0.33      0.48      0.39       184
   DE PIE DOBLANDO TOALLAS       0.50      0.37      0.42       184
    DE PIE MOVIENDO LIBROS       0.46      0.55      0.50       184
          DE PIE USANDO PC       0.47      0.66      0.55       184
        FASE REPOSO CON K5       0.60      0.74      0.66       184
INCREMENTAL CICLOERGOMETRO       0.81      0.64      0.72       184
           SENTADO LEYENDO       0.41      0.26      0.31       184
         SENTADO USANDO PC       0.21      0.23      0.22       184
      SENTADO VIENDO LA TV       0.50      0.30      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.40      0.74      0.52       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.45      0.44      0.43      2737
              weighted avg       0.45      0.44      0.42      2737


Accuracy capturado en la ejecución 26: 43.92 [%]
F1-score capturado en la ejecución 26: 42.58 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.66 [%]
Global accuracy score (test) = 43.84 [%]
Global F1 score (train) = 59.68 [%]
Global F1 score (test) = 42.65 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.17      0.11      0.13       184
 CAMINAR CON MÓVIL O LIBRO       0.28      0.48      0.35       184
       CAMINAR USUAL SPEED       0.19      0.04      0.06       184
            CAMINAR ZIGZAG       0.23      0.17      0.19       184
          DE PIE BARRIENDO       0.34      0.50      0.40       184
   DE PIE DOBLANDO TOALLAS       0.48      0.37      0.42       184
    DE PIE MOVIENDO LIBROS       0.44      0.52      0.48       184
          DE PIE USANDO PC       0.47      0.66      0.55       184
        FASE REPOSO CON K5       0.61      0.73      0.67       184
INCREMENTAL CICLOERGOMETRO       0.80      0.62      0.70       184
           SENTADO LEYENDO       0.44      0.35      0.39       184
         SENTADO USANDO PC       0.22      0.22      0.22       184
      SENTADO VIENDO LA TV       0.52      0.32      0.39       184
   SUBIR Y BAJAR ESCALERAS       0.45      0.74      0.56       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.43      2737
              weighted avg       0.44      0.44      0.42      2737


Accuracy capturado en la ejecución 27: 43.84 [%]
F1-score capturado en la ejecución 27: 42.65 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
2025-11-07 09:37:10.944251: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:37:10.955839: 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 00:00:1762504630.969158 2332011 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504630.973355 2332011 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504630.983405 2332011 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504630.983425 2332011 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504630.983427 2332011 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504630.983436 2332011 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:37:10.986669: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    2.0s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.7s 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 424 out of 424 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-07 09:37:39.043015: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 09:37:39.054551: 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 00:00:1762504659.067668 2332533 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762504659.071842 2332533 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762504659.081767 2332533 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504659.081787 2332533 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504659.081789 2332533 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762504659.081792 2332533 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 09:37:39.085027: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done 424 out of 424 | elapsed:    4.6s 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 424 out of 424 | 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13. 13.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.38 [%]
Global accuracy score (test) = 42.56 [%]
Global F1 score (train) = 59.41 [%]
Global F1 score (test) = 40.96 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.11      0.15       184
 CAMINAR CON MÓVIL O LIBRO       0.27      0.44      0.34       184
       CAMINAR USUAL SPEED       0.09      0.01      0.02       184
            CAMINAR ZIGZAG       0.20      0.15      0.17       184
          DE PIE BARRIENDO       0.31      0.47      0.37       184
   DE PIE DOBLANDO TOALLAS       0.50      0.37      0.42       184
    DE PIE MOVIENDO LIBROS       0.44      0.55      0.49       184
          DE PIE USANDO PC       0.47      0.67      0.55       184
        FASE REPOSO CON K5       0.58      0.65      0.61       184
INCREMENTAL CICLOERGOMETRO       0.80      0.63      0.71       184
           SENTADO LEYENDO       0.38      0.26      0.31       184
         SENTADO USANDO PC       0.24      0.23      0.24       184
      SENTADO VIENDO LA TV       0.42      0.29      0.34       184
   SUBIR Y BAJAR ESCALERAS       0.40      0.78      0.53       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.43      2737
                 macro avg       0.42      0.43      0.41      2737
              weighted avg       0.42      0.43      0.41      2737


Accuracy capturado en la ejecución 28: 42.56 [%]
F1-score capturado en la ejecución 28: 40.96 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.73 [%]
Global accuracy score (test) = 43.62 [%]
Global F1 score (train) = 59.7 [%]
Global F1 score (test) = 42.04 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.14      0.17       184
 CAMINAR CON MÓVIL O LIBRO       0.27      0.47      0.35       184
       CAMINAR USUAL SPEED       0.07      0.01      0.02       184
            CAMINAR ZIGZAG       0.19      0.12      0.15       184
          DE PIE BARRIENDO       0.34      0.48      0.40       184
   DE PIE DOBLANDO TOALLAS       0.49      0.36      0.42       184
    DE PIE MOVIENDO LIBROS       0.45      0.55      0.50       184
          DE PIE USANDO PC       0.46      0.67      0.55       184
        FASE REPOSO CON K5       0.59      0.73      0.65       184
INCREMENTAL CICLOERGOMETRO       0.81      0.61      0.70       184
           SENTADO LEYENDO       0.42      0.30      0.35       184
         SENTADO USANDO PC       0.23      0.21      0.22       184
      SENTADO VIENDO LA TV       0.51      0.33      0.40       184
   SUBIR Y BAJAR ESCALERAS       0.43      0.77      0.56       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.43      0.44      0.42      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 29: 43.62 [%]
F1-score capturado en la ejecución 29: 42.04 [%]

=== 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, 42)
(23391, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[13. 13. 13. ... 13. 13.  1.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 59.06 [%]
Global accuracy score (test) = 43.55 [%]
Global F1 score (train) = 59.02 [%]
Global F1 score (test) = 42.22 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.14      0.17       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.50      0.37       184
       CAMINAR USUAL SPEED       0.29      0.06      0.10       184
            CAMINAR ZIGZAG       0.24      0.16      0.19       184
          DE PIE BARRIENDO       0.33      0.49      0.40       184
   DE PIE DOBLANDO TOALLAS       0.52      0.38      0.44       184
    DE PIE MOVIENDO LIBROS       0.43      0.46      0.44       184
          DE PIE USANDO PC       0.48      0.68      0.56       184
        FASE REPOSO CON K5       0.53      0.72      0.61       184
INCREMENTAL CICLOERGOMETRO       0.80      0.64      0.71       184
           SENTADO LEYENDO       0.40      0.29      0.33       184
         SENTADO USANDO PC       0.22      0.21      0.21       184
      SENTADO VIENDO LA TV       0.47      0.29      0.36       184
   SUBIR Y BAJAR ESCALERAS       0.44      0.77      0.56       184
                    TROTAR       0.98      0.81      0.89       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.42      2737
              weighted avg       0.44      0.44      0.42      2737


Accuracy capturado en la ejecución 30: 43.55 [%]
F1-score capturado en la ejecución 30: 42.22 [%]

=== RESUMEN FINAL ===
Accuracies: [42.75, 44.06, 43.99, 44.32, 43.77, 43.26, 44.25, 44.46, 42.71, 43.84, 44.1, 43.51, 43.59, 43.73, 43.95, 43.66, 43.11, 44.06, 43.59, 44.54, 43.04, 43.55, 43.22, 43.66, 43.37, 43.92, 43.84, 42.56, 43.62, 43.55]
F1-scores: [41.17, 42.44, 42.72, 42.84, 42.37, 41.42, 42.6, 43.22, 41.26, 42.02, 42.61, 42.19, 41.87, 42.35, 42.56, 42.37, 41.49, 42.38, 42.07, 43.05, 41.38, 41.91, 41.84, 41.98, 41.9, 42.58, 42.65, 40.96, 42.04, 42.22]
Accuracy mean: 43.6527 | std: 0.4891
F1 mean: 42.1487 | std: 0.5520

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_C/case_C_BRF_acc_17_classes/metrics_test.npz
