2025-11-06 17:34:01.606625: 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`.
2025-11-06 17:34:01.618429: 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 00:00:1762446841.632367 1623621 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762446841.636591 1623621 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762446841.647160 1623621 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762446841.647182 1623621 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762446841.647184 1623621 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762446841.647186 1623621 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:34:01.650422: 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(
2025-11-06 17:34:11,719	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-06 17:34:12,342	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-06 17:34:12,418	INFO trial.py:182 -- Creating a new dirname dir_6d33a_f30d because trial dirname 'dir_6d33a' already exists.
2025-11-06 17:34:12,421	INFO trial.py:182 -- Creating a new dirname dir_6d33a_0e9e because trial dirname 'dir_6d33a' already exists.
2025-11-06 17:34:12,423	INFO trial.py:182 -- Creating a new dirname dir_6d33a_7778 because trial dirname 'dir_6d33a' already exists.
2025-11-06 17:34:12,425	INFO trial.py:182 -- Creating a new dirname dir_6d33a_8e41 because trial dirname 'dir_6d33a' already exists.
2025-11-06 17:34:12,427	INFO trial.py:182 -- Creating a new dirname dir_6d33a_ce3e because trial dirname 'dir_6d33a' already exists.
2025-11-06 17:34:12,429	INFO trial.py:182 -- Creating a new dirname dir_6d33a_6988 because trial dirname 'dir_6d33a' already exists.
2025-11-06 17:34:12,431	INFO trial.py:182 -- Creating a new dirname dir_6d33a_dbcc because trial dirname 'dir_6d33a' already exists.
2025-11-06 17:34:12,433	INFO trial.py:182 -- Creating a new dirname dir_6d33a_cc59 because trial dirname 'dir_6d33a' already exists.
2025-11-06 17:34:12,436	INFO trial.py:182 -- Creating a new dirname dir_6d33a_6e00 because trial dirname 'dir_6d33a' already exists.
2025-11-06 17:34:12,438	INFO trial.py:182 -- Creating a new dirname dir_6d33a_bf04 because trial dirname 'dir_6d33a' already exists.
2025-11-06 17:34:12,441	INFO trial.py:182 -- Creating a new dirname dir_6d33a_f2e5 because trial dirname 'dir_6d33a' already exists.
2025-11-06 17:34:12,444	INFO trial.py:182 -- Creating a new dirname dir_6d33a_bdea because trial dirname 'dir_6d33a' already exists.
2025-11-06 17:34:12,447	INFO trial.py:182 -- Creating a new dirname dir_6d33a_6387 because trial dirname 'dir_6d33a' already exists.
2025-11-06 17:34:12,452	INFO trial.py:182 -- Creating a new dirname dir_6d33a_8780 because trial dirname 'dir_6d33a' already exists.
2025-11-06 17:34:12,456	INFO trial.py:182 -- Creating a new dirname dir_6d33a_95fc because trial dirname 'dir_6d33a' already exists.
2025-11-06 17:34:12,459	INFO trial.py:182 -- Creating a new dirname dir_6d33a_6314 because trial dirname 'dir_6d33a' already exists.
2025-11-06 17:34:12,466	INFO trial.py:182 -- Creating a new dirname dir_6d33a_eba9 because trial dirname 'dir_6d33a' already exists.
2025-11-06 17:34:12,469	INFO trial.py:182 -- Creating a new dirname dir_6d33a_50c4 because trial dirname 'dir_6d33a' already exists.
2025-11-06 17:34:12,474	INFO trial.py:182 -- Creating a new dirname dir_6d33a_f2a7 because trial dirname 'dir_6d33a' 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_M/case_M_BRF_acc_17_classes/BalancedRF_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-06_17-34-10_901926_1623621/artifacts/2025-11-06_17-34-12/BalancedRF_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-06 17:34:12. Total running time: 0s
Logical resource usage: 0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       n_estimators     max_depth     min_samples_split     min_samples_leaf   max_features       random_state │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_6d33a    PENDING               333             5                    59                   22   sqrt                       3739 │
│ trial_6d33a    PENDING               262             7                    54                   12   sqrt                       2680 │
│ trial_6d33a    PENDING               485             5                    49                   24   0.3                        4627 │
│ trial_6d33a    PENDING               379             7                    29                   24   sqrt                       2402 │
│ trial_6d33a    PENDING               436             7                    51                   22   sqrt                       2043 │
│ trial_6d33a    PENDING               384             7                    26                   18   sqrt                       4991 │
│ trial_6d33a    PENDING               394             6                    59                   28   0.3                        8749 │
│ trial_6d33a    PENDING               387             6                    34                   14   sqrt                       6020 │
│ trial_6d33a    PENDING               329             6                    31                   10   sqrt                       6140 │
│ trial_6d33a    PENDING               433             6                    21                   14   sqrt                       5607 │
│ trial_6d33a    PENDING               303             5                    54                   24   sqrt                       4427 │
│ trial_6d33a    PENDING               305             5                    26                   11   sqrt                       1150 │
│ trial_6d33a    PENDING               361             5                    39                   29   0.3                        7709 │
│ trial_6d33a    PENDING               458             7                    47                   12   sqrt                        632 │
│ trial_6d33a    PENDING               384             7                    24                   16   0.3                        3737 │
│ trial_6d33a    PENDING               218             6                    57                   15   sqrt                       6849 │
│ trial_6d33a    PENDING               495             7                    38                   15   sqrt                       6982 │
│ trial_6d33a    PENDING               405             6                    20                   27   0.3                        8884 │
│ trial_6d33a    PENDING               499             5                    24                   23   sqrt                       2238 │
│ trial_6d33a    PENDING               203             5                    52                   19   sqrt                       6020 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               12 │
│ min_samples_split              54 │
│ n_estimators                  262 │
│ random_state                 2680 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               22 │
│ min_samples_split              51 │
│ n_estimators                  436 │
│ random_state                 2043 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               15 │
│ min_samples_split              38 │
│ n_estimators                  495 │
│ random_state                 6982 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               16 │
│ min_samples_split              24 │
│ n_estimators                  384 │
│ random_state                 3737 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               23 │
│ min_samples_split              24 │
│ n_estimators                  499 │
│ random_state                 2238 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               14 │
│ min_samples_split              34 │
│ n_estimators                  387 │
│ random_state                 6020 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               22 │
│ min_samples_split              59 │
│ n_estimators                  333 │
│ random_state                 3739 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               12 │
│ min_samples_split              47 │
│ n_estimators                  458 │
│ random_state                  632 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               27 │
│ min_samples_split              20 │
│ n_estimators                  405 │
│ random_state                 8884 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               14 │
│ min_samples_split              21 │
│ n_estimators                  433 │
│ random_state                 5607 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               24 │
│ min_samples_split              29 │
│ n_estimators                  379 │
│ random_state                 2402 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               18 │
│ min_samples_split              26 │
│ n_estimators                  384 │
│ random_state                 4991 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               10 │
│ min_samples_split              31 │
│ n_estimators                  329 │
│ random_state                 6140 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               28 │
│ min_samples_split              59 │
│ n_estimators                  394 │
│ random_state                 8749 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               15 │
│ min_samples_split              57 │
│ n_estimators                  218 │
│ random_state                 6849 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
[36m(train_brf_ray_tune pid=1625331)[0m 2025-11-06 17:34:15.566238: 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=1625331)[0m 2025-11-06 17:34:15.588328: 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=1625331)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_brf_ray_tune pid=1625331)[0m E0000 00:00:1762446855.620448 1626469 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=1625331)[0m E0000 00:00:1762446855.628746 1626469 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=1625331)[0m W0000 00:00:1762446855.648014 1626469 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=1625331)[0m W0000 00:00:1762446855.648053 1626469 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=1625331)[0m W0000 00:00:1762446855.648055 1626469 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=1625331)[0m W0000 00:00:1762446855.648058 1626469 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=1625331)[0m 2025-11-06 17:34:15.654067: 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=1625331)[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=1625331)[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=1625331)[0m   return fit_method(estimator, *args, **kwargs)
[36m(train_brf_ray_tune pid=1625331)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=1625331)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    1.0s
[36m(train_brf_ray_tune pid=1625349)[0m 2025-11-06 17:34:16.186812: 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=1625349)[0m 2025-11-06 17:34:16.208253: 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=1625353)[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=1625353)[0m E0000 00:00:1762446856.154755 1626600 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=1625353)[0m E0000 00:00:1762446856.165628 1626600 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=1625349)[0m W0000 00:00:1762446856.265407 1626609 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=1625349)[0m 2025-11-06 17:34:16.278683: 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=1625349)[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=1625349)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=1625349)[0m   return fit_method(estimator, *args, **kwargs)[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=1625352)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=1625334)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    4.6s[32m [repeated 18x across cluster][0m
[36m(train_brf_ray_tune pid=1625359)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   11.4s[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=1625332)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   17.1s[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=1625353)[0m [Parallel(n_jobs=-1)]: Done 203 out of 203 | elapsed:   19.0s finished
[36m(train_brf_ray_tune pid=1625353)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=1625353)[0m [Parallel(n_jobs=20)]: Done 203 out of 203 | elapsed:    0.8s finished
[36m(train_brf_ray_tune pid=1625353)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=1625353)[0m [Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.2s[32m [repeated 10x across cluster][0m
[36m(train_brf_ray_tune pid=1625353)[0m [Parallel(n_jobs=20)]: Done 203 out of 203 | elapsed:    0.2s finished
[36m(train_brf_ray_tune pid=1625353)[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=1625353)[0m   _log_deprecation_warning(
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               11 │
│ min_samples_split              26 │
│ n_estimators                  305 │
│ random_state                 1150 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               24 │
│ min_samples_split              49 │
│ n_estimators                  485 │
│ random_state                 4627 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               19 │
│ min_samples_split              52 │
│ n_estimators                  203 │
│ random_state                 6020 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               29 │
│ min_samples_split              39 │
│ n_estimators                  361 │
│ random_state                 7709 │
╰───────────────────────────────────╯
Trial trial_6d33a started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d33a config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               24 │
│ min_samples_split              54 │
│ n_estimators                  303 │
│ random_state                 4427 │
╰───────────────────────────────────╯

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:34:42. Total running time: 29s
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             26.3946 │
│ time_total_s                 26.3946 │
│ training_iteration                 1 │
│ test_accuracy                0.42126 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:34:42. Total running time: 29s

Trial status: 19 RUNNING | 1 TERMINATED
Current time: 2025-11-06 17:34:42. Total running time: 30s
Logical resource usage: 19.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
[36m(train_brf_ray_tune pid=1625351)[0m [Parallel(n_jobs=-1)]: Done 218 out of 218 | elapsed:   25.8s finished
[36m(train_brf_ray_tune pid=1625351)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=1625351)[0m [Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.7s[32m [repeated 5x across cluster][0m
[36m(train_brf_ray_tune pid=1625351)[0m [Parallel(n_jobs=20)]: Done 218 out of 218 | elapsed:    1.3s finished
[36m(train_brf_ray_tune pid=1625351)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=1625351)[0m [Parallel(n_jobs=20)]: Done 218 out of 218 | elapsed:    0.4s finished
[36m(train_brf_ray_tune pid=1625351)[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=1625351)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=1625357)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   32.7s[32m [repeated 14x across cluster][0m
[36m(train_brf_ray_tune pid=1625359)[0m [Parallel(n_jobs=20)]: Done 305 out of 305 | elapsed:    0.7s finished[32m [repeated 9x across cluster][0m
[36m(train_brf_ray_tune pid=1625349)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 6x across cluster][0m
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         n_estimators     max_depth     min_samples_split     min_samples_leaf   max_features       random_state     iter     total time (s)     test_accuracy │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_6d33a    RUNNING                 333             5                    59                   22   sqrt                       3739                                               │
│ trial_6d33a    RUNNING                 262             7                    54                   12   sqrt                       2680                                               │
│ trial_6d33a    RUNNING                 485             5                    49                   24   0.3                        4627                                               │
│ trial_6d33a    RUNNING                 379             7                    29                   24   sqrt                       2402                                               │
│ trial_6d33a    RUNNING                 436             7                    51                   22   sqrt                       2043                                               │
│ trial_6d33a    RUNNING                 384             7                    26                   18   sqrt                       4991                                               │
│ trial_6d33a    RUNNING                 394             6                    59                   28   0.3                        8749                                               │
│ trial_6d33a    RUNNING                 387             6                    34                   14   sqrt                       6020                                               │
│ trial_6d33a    RUNNING                 329             6                    31                   10   sqrt                       6140                                               │
│ trial_6d33a    RUNNING                 433             6                    21                   14   sqrt                       5607                                               │
│ trial_6d33a    RUNNING                 303             5                    54                   24   sqrt                       4427                                               │
│ trial_6d33a    RUNNING                 305             5                    26                   11   sqrt                       1150                                               │
│ trial_6d33a    RUNNING                 361             5                    39                   29   0.3                        7709                                               │
│ trial_6d33a    RUNNING                 458             7                    47                   12   sqrt                        632                                               │
│ trial_6d33a    RUNNING                 384             7                    24                   16   0.3                        3737                                               │
│ trial_6d33a    RUNNING                 218             6                    57                   15   sqrt                       6849                                               │
│ trial_6d33a    RUNNING                 495             7                    38                   15   sqrt                       6982                                               │
│ trial_6d33a    RUNNING                 405             6                    20                   27   0.3                        8884                                               │
│ trial_6d33a    RUNNING                 499             5                    24                   23   sqrt                       2238                                               │
│ trial_6d33a    TERMINATED              203             5                    52                   19   sqrt                       6020        1            26.3946          0.421264 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:34:48. Total running time: 35s
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             32.6678 │
│ time_total_s                 32.6678 │
│ training_iteration                 1 │
│ test_accuracy                0.45086 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:34:48. Total running time: 35s

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:34:51. Total running time: 38s
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             36.0958 │
│ time_total_s                 36.0958 │
│ training_iteration                 1 │
│ test_accuracy                0.47534 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:34:51. Total running time: 39s

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:34:52. Total running time: 40s
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             37.0878 │
│ time_total_s                 37.0878 │
│ training_iteration                 1 │
│ test_accuracy                0.42821 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:34:52. Total running time: 40s

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:34:53. Total running time: 40s
[36m(train_brf_ray_tune pid=1625349)[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=1625349)[0m   _log_deprecation_warning([32m [repeated 3x across cluster][0m
[36m(train_brf_ray_tune pid=1625342)[0m [Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:   38.9s[32m [repeated 11x across cluster][0m
[36m(train_brf_ray_tune pid=1625332)[0m [Parallel(n_jobs=20)]: Done 333 out of 333 | elapsed:    1.0s finished[32m [repeated 3x across cluster][0m
[36m(train_brf_ray_tune pid=1625332)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=1625332)[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=1625332)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=1625355)[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=1625355)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=1625354)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.5s[32m [repeated 14x across cluster][0m
[36m(train_brf_ray_tune pid=1625354)[0m [Parallel(n_jobs=20)]: Done 379 out of 379 | elapsed:    0.7s finished[32m [repeated 10x across cluster][0m
[36m(train_brf_ray_tune pid=1625354)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 6x across cluster][0m
[36m(train_brf_ray_tune pid=1625358)[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=1625358)[0m   _log_deprecation_warning([32m [repeated 6x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             37.5233 │
│ time_total_s                 37.5233 │
│ training_iteration                 1 │
│ test_accuracy                0.43113 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:34:53. Total running time: 40s

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:34:55. Total running time: 42s
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             39.8885 │
│ time_total_s                 39.8885 │
│ training_iteration                 1 │
│ test_accuracy                 0.4293 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:34:55. Total running time: 42s

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:35:01. Total running time: 49s
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             46.3314 │
│ time_total_s                 46.3314 │
│ training_iteration                 1 │
│ test_accuracy                0.45305 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:35:01. Total running time: 49s

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:35:01. Total running time: 49s
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             46.6523 │
│ time_total_s                 46.6523 │
│ training_iteration                 1 │
│ test_accuracy                0.45561 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:35:02. Total running time: 49s

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:35:03. Total running time: 50s
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              47.691 │
│ time_total_s                  47.691 │
│ training_iteration                 1 │
│ test_accuracy                0.47242 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:35:03. Total running time: 50s

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:35:04. Total running time: 52s
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             49.1712 │
│ time_total_s                 49.1712 │
│ training_iteration                 1 │
│ test_accuracy                 0.4662 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:35:04. Total running time: 52s

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:35:05. Total running time: 53s
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             50.3597 │
│ time_total_s                 50.3597 │
│ training_iteration                 1 │
│ test_accuracy                0.43223 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:35:05. Total running time: 53s

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:35:06. Total running time: 53s
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             50.8578 │
│ time_total_s                 50.8578 │
│ training_iteration                 1 │
│ test_accuracy                0.47534 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:35:06. Total running time: 53s

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:35:06. Total running time: 53s
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             50.8252 │
│ time_total_s                 50.8252 │
│ training_iteration                 1 │
│ test_accuracy                0.47607 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:35:06. Total running time: 53s

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:35:07. Total running time: 55s
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              52.479 │
│ time_total_s                  52.479 │
│ training_iteration                 1 │
│ test_accuracy                0.45159 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:35:07. Total running time: 55s

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:35:07. Total running time: 55s
[36m(train_brf_ray_tune pid=1625333)[0m [Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.3s[32m [repeated 30x across cluster][0m
[36m(train_brf_ray_tune pid=1625360)[0m [Parallel(n_jobs=-1)]: Done 361 out of 361 | elapsed:   48.7s finished[32m [repeated 18x across cluster][0m
[36m(train_brf_ray_tune pid=1625333)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 12x across cluster][0m
2025-11-06 17:35:11,484	INFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_BRF_acc_17_classes/BalancedRF_hyperparameters_tuning' in 0.0112s.
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             52.7375 │
│ time_total_s                 52.7375 │
│ training_iteration                 1 │
│ test_accuracy                0.46913 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:35:07. Total running time: 55s

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:35:09. Total running time: 57s
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             53.7518 │
│ time_total_s                 53.7518 │
│ training_iteration                 1 │
│ test_accuracy                0.42565 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:35:09. Total running time: 57s

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:35:10. Total running time: 58s
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             55.5161 │
│ time_total_s                 55.5161 │
│ training_iteration                 1 │
│ test_accuracy                0.46547 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:35:10. Total running time: 58s

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:35:11. Total running time: 59s
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             55.9841 │
│ time_total_s                 55.9841 │
│ training_iteration                 1 │
│ test_accuracy                0.45342 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:35:11. Total running time: 59s

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:35:11. Total running time: 59s
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              55.759 │
│ time_total_s                  55.759 │
│ training_iteration                 1 │
│ test_accuracy                0.41725 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:35:11. Total running time: 59s

Trial trial_6d33a finished iteration 1 at 2025-11-06 17:35:11. Total running time: 59s
╭──────────────────────────────────────╮
│ Trial trial_6d33a result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             55.9628 │
│ time_total_s                 55.9628 │
│ training_iteration                 1 │
│ test_accuracy                0.44684 │
╰──────────────────────────────────────╯

Trial trial_6d33a completed after 1 iterations at 2025-11-06 17:35:11. Total running time: 59s

Trial status: 20 TERMINATED
Current time: 2025-11-06 17:35:11. Total running time: 59s
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:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.3s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.0s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.3s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | 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_6d33a    TERMINATED              333             5                    59                   22   sqrt                       3739        1            39.8885          0.429302 │
│ trial_6d33a    TERMINATED              262             7                    54                   12   sqrt                       2680        1            36.0958          0.475338 │
│ trial_6d33a    TERMINATED              485             5                    49                   24   0.3                        4627        1            55.759           0.417245 │
│ trial_6d33a    TERMINATED              379             7                    29                   24   sqrt                       2402        1            47.691           0.472415 │
│ trial_6d33a    TERMINATED              436             7                    51                   22   sqrt                       2043        1            50.8578          0.475338 │
│ trial_6d33a    TERMINATED              384             7                    26                   18   sqrt                       4991        1            49.1712          0.466204 │
│ trial_6d33a    TERMINATED              394             6                    59                   28   0.3                        8749        1            55.9628          0.44684  │
│ trial_6d33a    TERMINATED              387             6                    34                   14   sqrt                       6020        1            46.6523          0.455608 │
│ trial_6d33a    TERMINATED              329             6                    31                   10   sqrt                       6140        1            46.3314          0.453051 │
│ trial_6d33a    TERMINATED              433             6                    21                   14   sqrt                       5607        1            52.479           0.451589 │
│ trial_6d33a    TERMINATED              303             5                    54                   24   sqrt                       4427        1            37.5233          0.431129 │
│ trial_6d33a    TERMINATED              305             5                    26                   11   sqrt                       1150        1            37.0878          0.428206 │
│ trial_6d33a    TERMINATED              361             5                    39                   29   0.3                        7709        1            53.7518          0.425649 │
│ trial_6d33a    TERMINATED              458             7                    47                   12   sqrt                        632        1            50.8252          0.476069 │
│ trial_6d33a    TERMINATED              384             7                    24                   16   0.3                        3737        1            55.5161          0.465473 │
│ trial_6d33a    TERMINATED              218             6                    57                   15   sqrt                       6849        1            32.6678          0.450859 │
│ trial_6d33a    TERMINATED              495             7                    38                   15   sqrt                       6982        1            52.7375          0.469127 │
│ trial_6d33a    TERMINATED              405             6                    20                   27   0.3                        8884        1            55.9841          0.453416 │
│ trial_6d33a    TERMINATED              499             5                    24                   23   sqrt                       2238        1            50.3597          0.432225 │
│ trial_6d33a    TERMINATED              203             5                    52                   19   sqrt                       6020        1            26.3946          0.421264 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'n_estimators': 458, 'max_depth': 7, 'min_samples_split': 47, 'min_samples_leaf': 12, 'max_features': 'sqrt', 'random_state': 632}
Saved model to disk
[36m(train_brf_ray_tune pid=1625352)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 7x across cluster][0m
[36m(train_brf_ray_tune pid=1625352)[0m   _log_deprecation_warning([32m [repeated 7x across cluster][0m
[36m(train_brf_ray_tune pid=1625352)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s[32m [repeated 23x across cluster][0m
[36m(train_brf_ray_tune pid=1625352)[0m [Parallel(n_jobs=20)]: Done 394 out of 394 | elapsed:    0.1s finished[32m [repeated 14x across cluster][0m
[36m(train_brf_ray_tune pid=1625352)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 10x across cluster][0m
2025-11-06 17:35:31.287947: 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`.
2025-11-06 17:35:31.299132: 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 00:00:1762446931.312316 1629088 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762446931.316546 1629088 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762446931.326679 1629088 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762446931.326701 1629088 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762446931.326703 1629088 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762446931.326705 1629088 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:35:31.329853: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.3s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.0s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.2s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:35:57.974749: 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`.
2025-11-06 17:35:57.986033: 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 00:00:1762446957.999405 1629575 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762446958.003791 1629575 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762446958.014298 1629575 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762446958.014317 1629575 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762446958.014320 1629575 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762446958.014322 1629575 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:35:58.017658: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.3s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.9s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.2s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:36:24.629437: 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`.
2025-11-06 17:36:24.640720: 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 00:00:1762446984.653701 1630069 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762446984.657764 1630069 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762446984.667547 1630069 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762446984.667564 1630069 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762446984.667567 1630069 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762446984.667568 1630069 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:36:24.670612: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.2s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.0s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.3s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 54.08 [%]
Global accuracy score (test) = 47.28 [%]
Global F1 score (train) = 54.13 [%]
Global F1 score (test) = 47.64 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.72      0.61       184
 CAMINAR CON MÓVIL O LIBRO       0.75      0.39      0.51       184
       CAMINAR USUAL SPEED       0.56      0.32      0.41       184
            CAMINAR ZIGZAG       0.29      0.46      0.36       184
          DE PIE BARRIENDO       0.64      0.49      0.56       184
   DE PIE DOBLANDO TOALLAS       0.35      0.48      0.41       184
    DE PIE MOVIENDO LIBROS       0.32      0.53      0.40       184
          DE PIE USANDO PC       0.25      0.28      0.26       184
        FASE REPOSO CON K5       0.59      0.75      0.66       184
INCREMENTAL CICLOERGOMETRO       0.71      0.68      0.70       184
           SENTADO LEYENDO       0.51      0.47      0.49       184
         SENTADO USANDO PC       0.33      0.25      0.28       184
      SENTADO VIENDO LA TV       0.22      0.14      0.17       184
   SUBIR Y BAJAR ESCALERAS       0.73      0.57      0.64       184
                    TROTAR       0.92      0.55      0.69       161

                  accuracy                           0.47      2737
                 macro avg       0.51      0.47      0.48      2737
              weighted avg       0.51      0.47      0.47      2737


Accuracy capturado en la ejecución 1: 47.28 [%]
F1-score capturado en la ejecución 1: 47.64 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.88 [%]
Global accuracy score (test) = 47.68 [%]
Global F1 score (train) = 53.89 [%]
Global F1 score (test) = 48.07 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.52      0.71      0.60       184
 CAMINAR CON MÓVIL O LIBRO       0.75      0.40      0.52       184
       CAMINAR USUAL SPEED       0.57      0.32      0.41       184
            CAMINAR ZIGZAG       0.29      0.47      0.36       184
          DE PIE BARRIENDO       0.63      0.53      0.58       184
   DE PIE DOBLANDO TOALLAS       0.35      0.48      0.40       184
    DE PIE MOVIENDO LIBROS       0.33      0.55      0.41       184
          DE PIE USANDO PC       0.26      0.26      0.26       184
        FASE REPOSO CON K5       0.60      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.71      0.68      0.69       184
           SENTADO LEYENDO       0.48      0.46      0.47       184
         SENTADO USANDO PC       0.37      0.32      0.34       184
      SENTADO VIENDO LA TV       0.18      0.11      0.14       184
   SUBIR Y BAJAR ESCALERAS       0.77      0.57      0.65       184
                    TROTAR       0.96      0.55      0.70       161

                  accuracy                           0.48      2737
                 macro avg       0.52      0.48      0.48      2737
              weighted avg       0.51      0.48      0.48      2737


Accuracy capturado en la ejecución 2: 47.68 [%]
F1-score capturado en la ejecución 2: 48.07 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.87 [%]
Global accuracy score (test) = 47.31 [%]
Global F1 score (train) = 53.88 [%]
Global F1 score (test) = 47.79 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.71      0.61       184
 CAMINAR CON MÓVIL O LIBRO       0.74      0.39      0.51       184
       CAMINAR USUAL SPEED       0.57      0.30      0.40       184
            CAMINAR ZIGZAG       0.28      0.48      0.36       184
          DE PIE BARRIENDO       0.61      0.54      0.57       184
   DE PIE DOBLANDO TOALLAS       0.35      0.48      0.40       184
    DE PIE MOVIENDO LIBROS       0.32      0.53      0.40       184
          DE PIE USANDO PC       0.22      0.24      0.23       184
        FASE REPOSO CON K5       0.61      0.75      0.67       184
INCREMENTAL CICLOERGOMETRO       0.71      0.67      0.69       184
           SENTADO LEYENDO       0.49      0.46      0.47       184
         SENTADO USANDO PC       0.42      0.33      0.37       184
      SENTADO VIENDO LA TV       0.19      0.12      0.15       184
   SUBIR Y BAJAR ESCALERAS       0.76      0.55      0.64       184
                    TROTAR       0.97      0.55      0.70       161

                  accuracy                           0.47      2737
                 macro avg       0.52      0.47      0.48      2737
              weighted avg       0.51      0.47      0.48      2737


Accuracy capturado en la ejecución 3: 47.31 [%]
F1-score capturado en la ejecución 3: 47.79 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
2025-11-06 17:36:51.249134: 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`.
2025-11-06 17:36:51.260628: 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 00:00:1762447011.274734 1630546 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447011.279110 1630546 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447011.289924 1630546 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447011.289953 1630546 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447011.289956 1630546 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447011.289966 1630546 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:36:51.293138: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.2s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.8s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.1s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:37:17.851142: 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`.
2025-11-06 17:37:17.862712: 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 00:00:1762447037.875945 1631018 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447037.880187 1631018 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447037.890133 1631018 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447037.890156 1631018 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447037.890158 1631018 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447037.890160 1631018 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:37:17.893332: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.3s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.0s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.2s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:37:44.371949: 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`.
2025-11-06 17:37:44.383304: 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 00:00:1762447064.396418 1631492 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447064.400528 1631492 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447064.410231 1631492 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447064.410249 1631492 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447064.410252 1631492 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447064.410254 1631492 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:37:44.413331: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.2s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.0s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.3s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.88 [%]
Global accuracy score (test) = 46.8 [%]
Global F1 score (train) = 53.92 [%]
Global F1 score (test) = 47.11 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.71      0.61       184
 CAMINAR CON MÓVIL O LIBRO       0.75      0.39      0.51       184
       CAMINAR USUAL SPEED       0.54      0.32      0.40       184
            CAMINAR ZIGZAG       0.28      0.46      0.35       184
          DE PIE BARRIENDO       0.60      0.52      0.56       184
   DE PIE DOBLANDO TOALLAS       0.35      0.48      0.41       184
    DE PIE MOVIENDO LIBROS       0.32      0.53      0.40       184
          DE PIE USANDO PC       0.18      0.19      0.19       184
        FASE REPOSO CON K5       0.62      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.71      0.67      0.69       184
           SENTADO LEYENDO       0.49      0.47      0.48       184
         SENTADO USANDO PC       0.39      0.33      0.36       184
      SENTADO VIENDO LA TV       0.17      0.10      0.13       184
   SUBIR Y BAJAR ESCALERAS       0.73      0.55      0.63       184
                    TROTAR       0.88      0.55      0.68       161

                  accuracy                           0.47      2737
                 macro avg       0.50      0.47      0.47      2737
              weighted avg       0.50      0.47      0.47      2737


Accuracy capturado en la ejecución 4: 46.8 [%]
F1-score capturado en la ejecución 4: 47.11 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.69 [%]
Global accuracy score (test) = 47.2 [%]
Global F1 score (train) = 53.67 [%]
Global F1 score (test) = 47.51 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.70      0.60       184
 CAMINAR CON MÓVIL O LIBRO       0.75      0.42      0.54       184
       CAMINAR USUAL SPEED       0.56      0.30      0.39       184
            CAMINAR ZIGZAG       0.31      0.47      0.38       184
          DE PIE BARRIENDO       0.61      0.54      0.57       184
   DE PIE DOBLANDO TOALLAS       0.35      0.48      0.41       184
    DE PIE MOVIENDO LIBROS       0.32      0.54      0.41       184
          DE PIE USANDO PC       0.20      0.21      0.20       184
        FASE REPOSO CON K5       0.60      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.68      0.67      0.68       184
           SENTADO LEYENDO       0.49      0.46      0.47       184
         SENTADO USANDO PC       0.39      0.29      0.33       184
      SENTADO VIENDO LA TV       0.21      0.15      0.17       184
   SUBIR Y BAJAR ESCALERAS       0.67      0.55      0.61       184
                    TROTAR       0.96      0.55      0.70       161

                  accuracy                           0.47      2737
                 macro avg       0.51      0.47      0.48      2737
              weighted avg       0.50      0.47      0.47      2737


Accuracy capturado en la ejecución 5: 47.2 [%]
F1-score capturado en la ejecución 5: 47.51 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.94 [%]
Global accuracy score (test) = 47.68 [%]
Global F1 score (train) = 53.95 [%]
Global F1 score (test) = 48.02 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.72      0.61       184
 CAMINAR CON MÓVIL O LIBRO       0.73      0.42      0.53       184
       CAMINAR USUAL SPEED       0.57      0.33      0.42       184
            CAMINAR ZIGZAG       0.31      0.49      0.38       184
          DE PIE BARRIENDO       0.61      0.51      0.55       184
   DE PIE DOBLANDO TOALLAS       0.35      0.48      0.40       184
    DE PIE MOVIENDO LIBROS       0.32      0.53      0.40       184
          DE PIE USANDO PC       0.22      0.23      0.23       184
        FASE REPOSO CON K5       0.61      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.71      0.68      0.70       184
           SENTADO LEYENDO       0.48      0.47      0.47       184
         SENTADO USANDO PC       0.39      0.31      0.35       184
      SENTADO VIENDO LA TV       0.20      0.13      0.16       184
   SUBIR Y BAJAR ESCALERAS       0.73      0.57      0.64       184
                    TROTAR       0.93      0.55      0.69       161

                  accuracy                           0.48      2737
                 macro avg       0.51      0.48      0.48      2737
              weighted avg       0.51      0.48      0.48      2737


Accuracy capturado en la ejecución 6: 47.68 [%]
F1-score capturado en la ejecución 6: 48.02 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
2025-11-06 17:38:11.077918: 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`.
2025-11-06 17:38:11.089134: 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 00:00:1762447091.102616 1631993 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447091.106814 1631993 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447091.116569 1631993 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447091.116588 1631993 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447091.116590 1631993 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447091.116592 1631993 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:38:11.119816: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.3s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.1s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.4s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:38:37.618309: 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`.
2025-11-06 17:38:37.629350: 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 00:00:1762447117.642325 1632463 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447117.646235 1632463 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447117.656318 1632463 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447117.656338 1632463 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447117.656340 1632463 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447117.656342 1632463 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:38:37.659477: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.2s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.9s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.2s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:39:04.005457: 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`.
2025-11-06 17:39:04.016593: 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 00:00:1762447144.029418 1632942 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447144.033370 1632942 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447144.043259 1632942 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447144.043279 1632942 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447144.043281 1632942 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447144.043283 1632942 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:39:04.046322: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.3s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.0s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.3s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.9 [%]
Global accuracy score (test) = 47.61 [%]
Global F1 score (train) = 53.93 [%]
Global F1 score (test) = 48.06 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.51      0.70      0.59       184
 CAMINAR CON MÓVIL O LIBRO       0.75      0.40      0.52       184
       CAMINAR USUAL SPEED       0.57      0.30      0.40       184
            CAMINAR ZIGZAG       0.29      0.47      0.36       184
          DE PIE BARRIENDO       0.63      0.53      0.58       184
   DE PIE DOBLANDO TOALLAS       0.35      0.49      0.41       184
    DE PIE MOVIENDO LIBROS       0.33      0.55      0.41       184
          DE PIE USANDO PC       0.25      0.27      0.26       184
        FASE REPOSO CON K5       0.61      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.75      0.68      0.71       184
           SENTADO LEYENDO       0.48      0.47      0.48       184
         SENTADO USANDO PC       0.36      0.30      0.33       184
      SENTADO VIENDO LA TV       0.21      0.14      0.17       184
   SUBIR Y BAJAR ESCALERAS       0.72      0.55      0.62       184
                    TROTAR       0.96      0.55      0.70       161

                  accuracy                           0.48      2737
                 macro avg       0.52      0.48      0.48      2737
              weighted avg       0.51      0.48      0.48      2737


Accuracy capturado en la ejecución 7: 47.61 [%]
F1-score capturado en la ejecución 7: 48.06 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.85 [%]
Global accuracy score (test) = 47.13 [%]
Global F1 score (train) = 53.85 [%]
Global F1 score (test) = 47.56 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.54      0.72      0.62       184
 CAMINAR CON MÓVIL O LIBRO       0.74      0.41      0.52       184
       CAMINAR USUAL SPEED       0.55      0.31      0.40       184
            CAMINAR ZIGZAG       0.29      0.48      0.36       184
          DE PIE BARRIENDO       0.63      0.52      0.57       184
   DE PIE DOBLANDO TOALLAS       0.35      0.48      0.41       184
    DE PIE MOVIENDO LIBROS       0.31      0.51      0.39       184
          DE PIE USANDO PC       0.23      0.24      0.24       184
        FASE REPOSO CON K5       0.61      0.75      0.67       184
INCREMENTAL CICLOERGOMETRO       0.71      0.67      0.69       184
           SENTADO LEYENDO       0.49      0.45      0.47       184
         SENTADO USANDO PC       0.37      0.32      0.34       184
      SENTADO VIENDO LA TV       0.17      0.11      0.14       184
   SUBIR Y BAJAR ESCALERAS       0.73      0.56      0.63       184
                    TROTAR       0.94      0.55      0.70       161

                  accuracy                           0.47      2737
                 macro avg       0.51      0.47      0.48      2737
              weighted avg       0.51      0.47      0.47      2737


Accuracy capturado en la ejecución 8: 47.13 [%]
F1-score capturado en la ejecución 8: 47.56 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.85 [%]
Global accuracy score (test) = 47.17 [%]
Global F1 score (train) = 53.87 [%]
Global F1 score (test) = 47.58 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.72      0.61       184
 CAMINAR CON MÓVIL O LIBRO       0.75      0.43      0.54       184
       CAMINAR USUAL SPEED       0.57      0.31      0.40       184
            CAMINAR ZIGZAG       0.30      0.48      0.37       184
          DE PIE BARRIENDO       0.60      0.49      0.54       184
   DE PIE DOBLANDO TOALLAS       0.35      0.48      0.41       184
    DE PIE MOVIENDO LIBROS       0.32      0.53      0.40       184
          DE PIE USANDO PC       0.21      0.22      0.22       184
        FASE REPOSO CON K5       0.61      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.71      0.68      0.70       184
           SENTADO LEYENDO       0.49      0.47      0.48       184
         SENTADO USANDO PC       0.35      0.29      0.31       184
      SENTADO VIENDO LA TV       0.20      0.14      0.16       184
   SUBIR Y BAJAR ESCALERAS       0.77      0.55      0.64       184
                    TROTAR       0.90      0.55      0.68       161

                  accuracy                           0.47      2737
                 macro avg       0.51      0.47      0.48      2737
              weighted avg       0.51      0.47      0.47      2737


Accuracy capturado en la ejecución 9: 47.17 [%]
F1-score capturado en la ejecución 9: 47.58 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
2025-11-06 17:39:30.726260: 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`.
2025-11-06 17:39:30.737790: 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 00:00:1762447170.750634 1633428 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447170.754714 1633428 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447170.764572 1633428 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447170.764591 1633428 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447170.764593 1633428 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447170.764594 1633428 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:39:30.767681: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.2s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.0s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.2s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:39:57.335112: 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`.
2025-11-06 17:39:57.346935: 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 00:00:1762447197.360793 1633921 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447197.365229 1633921 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447197.375548 1633921 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447197.375573 1633921 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447197.375585 1633921 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447197.375587 1633921 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:39:57.378726: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.3s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.0s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.3s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:40:23.792476: 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`.
2025-11-06 17:40:23.803821: 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 00:00:1762447223.816746 1634404 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447223.820695 1634404 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447223.830457 1634404 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447223.830474 1634404 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447223.830477 1634404 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447223.830479 1634404 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:40:23.833432: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.3s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.0s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.2s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.68 [%]
Global accuracy score (test) = 46.91 [%]
Global F1 score (train) = 53.73 [%]
Global F1 score (test) = 47.29 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.70      0.60       184
 CAMINAR CON MÓVIL O LIBRO       0.74      0.39      0.51       184
       CAMINAR USUAL SPEED       0.57      0.30      0.39       184
            CAMINAR ZIGZAG       0.28      0.46      0.35       184
          DE PIE BARRIENDO       0.65      0.49      0.56       184
   DE PIE DOBLANDO TOALLAS       0.34      0.48      0.40       184
    DE PIE MOVIENDO LIBROS       0.32      0.54      0.41       184
          DE PIE USANDO PC       0.22      0.24      0.23       184
        FASE REPOSO CON K5       0.61      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.71      0.68      0.69       184
           SENTADO LEYENDO       0.48      0.46      0.47       184
         SENTADO USANDO PC       0.35      0.29      0.32       184
      SENTADO VIENDO LA TV       0.21      0.13      0.16       184
   SUBIR Y BAJAR ESCALERAS       0.72      0.59      0.65       184
                    TROTAR       0.87      0.55      0.68       161

                  accuracy                           0.47      2737
                 macro avg       0.51      0.47      0.47      2737
              weighted avg       0.50      0.47      0.47      2737


Accuracy capturado en la ejecución 10: 46.91 [%]
F1-score capturado en la ejecución 10: 47.29 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 54.02 [%]
Global accuracy score (test) = 47.13 [%]
Global F1 score (train) = 54.08 [%]
Global F1 score (test) = 47.44 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.71      0.61       184
 CAMINAR CON MÓVIL O LIBRO       0.74      0.40      0.52       184
       CAMINAR USUAL SPEED       0.57      0.30      0.40       184
            CAMINAR ZIGZAG       0.30      0.47      0.36       184
          DE PIE BARRIENDO       0.57      0.53      0.55       184
   DE PIE DOBLANDO TOALLAS       0.35      0.48      0.41       184
    DE PIE MOVIENDO LIBROS       0.33      0.53      0.40       184
          DE PIE USANDO PC       0.21      0.23      0.22       184
        FASE REPOSO CON K5       0.61      0.76      0.68       184
INCREMENTAL CICLOERGOMETRO       0.70      0.68      0.69       184
           SENTADO LEYENDO       0.49      0.46      0.47       184
         SENTADO USANDO PC       0.37      0.28      0.32       184
      SENTADO VIENDO LA TV       0.21      0.13      0.16       184
   SUBIR Y BAJAR ESCALERAS       0.72      0.57      0.63       184
                    TROTAR       0.96      0.55      0.70       161

                  accuracy                           0.47      2737
                 macro avg       0.51      0.47      0.47      2737
              weighted avg       0.51      0.47      0.47      2737


Accuracy capturado en la ejecución 11: 47.13 [%]
F1-score capturado en la ejecución 11: 47.44 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.83 [%]
Global accuracy score (test) = 47.17 [%]
Global F1 score (train) = 53.84 [%]
Global F1 score (test) = 47.54 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.51      0.72      0.60       184
 CAMINAR CON MÓVIL O LIBRO       0.73      0.40      0.52       184
       CAMINAR USUAL SPEED       0.55      0.30      0.39       184
            CAMINAR ZIGZAG       0.29      0.48      0.36       184
          DE PIE BARRIENDO       0.59      0.52      0.55       184
   DE PIE DOBLANDO TOALLAS       0.35      0.48      0.41       184
    DE PIE MOVIENDO LIBROS       0.34      0.53      0.42       184
          DE PIE USANDO PC       0.19      0.22      0.20       184
        FASE REPOSO CON K5       0.62      0.75      0.68       184
INCREMENTAL CICLOERGOMETRO       0.73      0.69      0.71       184
           SENTADO LEYENDO       0.49      0.46      0.47       184
         SENTADO USANDO PC       0.37      0.28      0.32       184
      SENTADO VIENDO LA TV       0.21      0.13      0.16       184
   SUBIR Y BAJAR ESCALERAS       0.76      0.57      0.65       184
                    TROTAR       0.94      0.55      0.70       161

                  accuracy                           0.47      2737
                 macro avg       0.51      0.47      0.48      2737
              weighted avg       0.51      0.47      0.47      2737


Accuracy capturado en la ejecución 12: 47.17 [%]
F1-score capturado en la ejecución 12: 47.54 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
2025-11-06 17:40:50.290697: 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`.
2025-11-06 17:40:50.301816: 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 00:00:1762447250.314816 1634876 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447250.318879 1634876 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447250.328656 1634876 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447250.328673 1634876 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447250.328675 1634876 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447250.328676 1634876 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:40:50.331712: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.3s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.1s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.3s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:41:16.842021: 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`.
2025-11-06 17:41:16.853699: 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 00:00:1762447276.867894 1635347 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447276.872241 1635347 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447276.882728 1635347 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447276.882748 1635347 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447276.882752 1635347 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447276.882754 1635347 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:41:16.886020: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.2s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.0s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.3s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:41:43.455710: 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`.
2025-11-06 17:41:43.466990: 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 00:00:1762447303.479991 1635849 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447303.483922 1635849 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447303.493788 1635849 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447303.493807 1635849 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447303.493810 1635849 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447303.493811 1635849 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:41:43.496940: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.2s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.9s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.2s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.8 [%]
Global accuracy score (test) = 47.64 [%]
Global F1 score (train) = 53.8 [%]
Global F1 score (test) = 48.07 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.52      0.72      0.61       184
 CAMINAR CON MÓVIL O LIBRO       0.75      0.41      0.53       184
       CAMINAR USUAL SPEED       0.57      0.31      0.40       184
            CAMINAR ZIGZAG       0.30      0.48      0.37       184
          DE PIE BARRIENDO       0.60      0.46      0.52       184
   DE PIE DOBLANDO TOALLAS       0.35      0.48      0.40       184
    DE PIE MOVIENDO LIBROS       0.32      0.55      0.40       184
          DE PIE USANDO PC       0.23      0.23      0.23       184
        FASE REPOSO CON K5       0.61      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.74      0.70      0.72       184
           SENTADO LEYENDO       0.49      0.46      0.47       184
         SENTADO USANDO PC       0.39      0.33      0.36       184
      SENTADO VIENDO LA TV       0.24      0.17      0.20       184
   SUBIR Y BAJAR ESCALERAS       0.77      0.56      0.65       184
                    TROTAR       0.86      0.55      0.67       161

                  accuracy                           0.48      2737
                 macro avg       0.52      0.48      0.48      2737
              weighted avg       0.51      0.48      0.48      2737


Accuracy capturado en la ejecución 13: 47.64 [%]
F1-score capturado en la ejecución 13: 48.07 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.8 [%]
Global accuracy score (test) = 48.08 [%]
Global F1 score (train) = 53.86 [%]
Global F1 score (test) = 48.62 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.54      0.72      0.62       184
 CAMINAR CON MÓVIL O LIBRO       0.75      0.42      0.54       184
       CAMINAR USUAL SPEED       0.56      0.31      0.40       184
            CAMINAR ZIGZAG       0.30      0.47      0.36       184
          DE PIE BARRIENDO       0.63      0.55      0.59       184
   DE PIE DOBLANDO TOALLAS       0.36      0.48      0.41       184
    DE PIE MOVIENDO LIBROS       0.32      0.55      0.40       184
          DE PIE USANDO PC       0.22      0.24      0.23       184
        FASE REPOSO CON K5       0.61      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.73      0.67      0.70       184
           SENTADO LEYENDO       0.50      0.47      0.48       184
         SENTADO USANDO PC       0.41      0.32      0.36       184
      SENTADO VIENDO LA TV       0.22      0.15      0.18       184
   SUBIR Y BAJAR ESCALERAS       0.76      0.56      0.65       184
                    TROTAR       0.98      0.55      0.71       161

                  accuracy                           0.48      2737
                 macro avg       0.53      0.48      0.49      2737
              weighted avg       0.52      0.48      0.48      2737


Accuracy capturado en la ejecución 14: 48.08 [%]
F1-score capturado en la ejecución 14: 48.62 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.69 [%]
Global accuracy score (test) = 46.88 [%]
Global F1 score (train) = 53.72 [%]
Global F1 score (test) = 47.17 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.71      0.61       184
 CAMINAR CON MÓVIL O LIBRO       0.74      0.39      0.51       184
       CAMINAR USUAL SPEED       0.57      0.30      0.39       184
            CAMINAR ZIGZAG       0.29      0.45      0.35       184
          DE PIE BARRIENDO       0.61      0.53      0.57       184
   DE PIE DOBLANDO TOALLAS       0.35      0.48      0.40       184
    DE PIE MOVIENDO LIBROS       0.32      0.53      0.40       184
          DE PIE USANDO PC       0.23      0.27      0.25       184
        FASE REPOSO CON K5       0.61      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.71      0.68      0.70       184
           SENTADO LEYENDO       0.49      0.45      0.47       184
         SENTADO USANDO PC       0.37      0.28      0.32       184
      SENTADO VIENDO LA TV       0.16      0.11      0.13       184
   SUBIR Y BAJAR ESCALERAS       0.66      0.57      0.61       184
                    TROTAR       0.92      0.55      0.69       161

                  accuracy                           0.47      2737
                 macro avg       0.50      0.47      0.47      2737
              weighted avg       0.50      0.47      0.47      2737


Accuracy capturado en la ejecución 15: 46.88 [%]
F1-score capturado en la ejecución 15: 47.17 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
2025-11-06 17:42:10.033925: 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`.
2025-11-06 17:42:10.045311: 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 00:00:1762447330.058578 1636320 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447330.062640 1636320 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447330.072561 1636320 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447330.072582 1636320 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447330.072584 1636320 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447330.072585 1636320 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:42:10.075606: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.2s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.1s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.3s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:42:36.757368: 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`.
2025-11-06 17:42:36.768961: 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 00:00:1762447356.782580 1636798 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447356.786702 1636798 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447356.796737 1636798 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447356.796759 1636798 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447356.796762 1636798 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447356.796763 1636798 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:42:36.799921: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.3s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.0s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.3s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:43:03.302354: 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`.
2025-11-06 17:43:03.313981: 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 00:00:1762447383.327221 1637271 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447383.331343 1637271 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447383.341901 1637271 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447383.341924 1637271 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447383.341926 1637271 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447383.341928 1637271 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:43:03.345203: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.2s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.9s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.2s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.76 [%]
Global accuracy score (test) = 48.3 [%]
Global F1 score (train) = 53.78 [%]
Global F1 score (test) = 48.81 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.73      0.61       184
 CAMINAR CON MÓVIL O LIBRO       0.73      0.40      0.52       184
       CAMINAR USUAL SPEED       0.58      0.33      0.42       184
            CAMINAR ZIGZAG       0.29      0.48      0.37       184
          DE PIE BARRIENDO       0.64      0.55      0.59       184
   DE PIE DOBLANDO TOALLAS       0.36      0.48      0.41       184
    DE PIE MOVIENDO LIBROS       0.34      0.54      0.42       184
          DE PIE USANDO PC       0.23      0.26      0.24       184
        FASE REPOSO CON K5       0.61      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.71      0.68      0.70       184
           SENTADO LEYENDO       0.49      0.46      0.48       184
         SENTADO USANDO PC       0.41      0.29      0.34       184
      SENTADO VIENDO LA TV       0.21      0.15      0.18       184
   SUBIR Y BAJAR ESCALERAS       0.77      0.59      0.67       184
                    TROTAR       0.99      0.55      0.71       161

                  accuracy                           0.48      2737
                 macro avg       0.53      0.48      0.49      2737
              weighted avg       0.52      0.48      0.49      2737


Accuracy capturado en la ejecución 16: 48.3 [%]
F1-score capturado en la ejecución 16: 48.81 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.9 [%]
Global accuracy score (test) = 47.24 [%]
Global F1 score (train) = 53.94 [%]
Global F1 score (test) = 47.73 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.51      0.70      0.59       184
 CAMINAR CON MÓVIL O LIBRO       0.74      0.39      0.51       184
       CAMINAR USUAL SPEED       0.56      0.30      0.39       184
            CAMINAR ZIGZAG       0.29      0.49      0.37       184
          DE PIE BARRIENDO       0.60      0.52      0.56       184
   DE PIE DOBLANDO TOALLAS       0.35      0.48      0.40       184
    DE PIE MOVIENDO LIBROS       0.32      0.54      0.40       184
          DE PIE USANDO PC       0.22      0.24      0.23       184
        FASE REPOSO CON K5       0.62      0.75      0.68       184
INCREMENTAL CICLOERGOMETRO       0.73      0.68      0.71       184
           SENTADO LEYENDO       0.49      0.47      0.48       184
         SENTADO USANDO PC       0.38      0.29      0.33       184
      SENTADO VIENDO LA TV       0.20      0.13      0.16       184
   SUBIR Y BAJAR ESCALERAS       0.78      0.55      0.65       184
                    TROTAR       0.97      0.55      0.70       161

                  accuracy                           0.47      2737
                 macro avg       0.52      0.47      0.48      2737
              weighted avg       0.51      0.47      0.48      2737


Accuracy capturado en la ejecución 17: 47.24 [%]
F1-score capturado en la ejecución 17: 47.73 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.71 [%]
Global accuracy score (test) = 47.13 [%]
Global F1 score (train) = 53.74 [%]
Global F1 score (test) = 47.7 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.52      0.70      0.60       184
 CAMINAR CON MÓVIL O LIBRO       0.75      0.41      0.53       184
       CAMINAR USUAL SPEED       0.55      0.30      0.39       184
            CAMINAR ZIGZAG       0.30      0.47      0.36       184
          DE PIE BARRIENDO       0.62      0.51      0.56       184
   DE PIE DOBLANDO TOALLAS       0.35      0.48      0.41       184
    DE PIE MOVIENDO LIBROS       0.29      0.52      0.38       184
          DE PIE USANDO PC       0.21      0.22      0.21       184
        FASE REPOSO CON K5       0.60      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.71      0.67      0.69       184
           SENTADO LEYENDO       0.49      0.45      0.47       184
         SENTADO USANDO PC       0.40      0.31      0.35       184
      SENTADO VIENDO LA TV       0.22      0.15      0.18       184
   SUBIR Y BAJAR ESCALERAS       0.78      0.58      0.66       184
                    TROTAR       0.94      0.55      0.70       161

                  accuracy                           0.47      2737
                 macro avg       0.52      0.47      0.48      2737
              weighted avg       0.51      0.47      0.48      2737


Accuracy capturado en la ejecución 18: 47.13 [%]
F1-score capturado en la ejecución 18: 47.7 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
2025-11-06 17:43:29.886808: 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`.
2025-11-06 17:43:29.898127: 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 00:00:1762447409.911750 1637772 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447409.916246 1637772 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447409.927002 1637772 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447409.927027 1637772 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447409.927030 1637772 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447409.927032 1637772 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:43:29.930501: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.2s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.0s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.2s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:43:56.555041: 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`.
2025-11-06 17:43:56.566345: 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 00:00:1762447436.579934 1638245 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447436.584264 1638245 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447436.594567 1638245 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447436.594593 1638245 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447436.594596 1638245 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447436.594598 1638245 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:43:56.597873: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.3s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.0s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.3s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:44:23.129267: 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`.
2025-11-06 17:44:23.140556: 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 00:00:1762447463.153663 1638738 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447463.157855 1638738 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447463.167964 1638738 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447463.167983 1638738 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447463.167985 1638738 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447463.167987 1638738 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:44:23.171331: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.3s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.1s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.4s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 54.02 [%]
Global accuracy score (test) = 47.83 [%]
Global F1 score (train) = 54.03 [%]
Global F1 score (test) = 48.18 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.72      0.61       184
 CAMINAR CON MÓVIL O LIBRO       0.73      0.39      0.51       184
       CAMINAR USUAL SPEED       0.58      0.33      0.42       184
            CAMINAR ZIGZAG       0.29      0.47      0.36       184
          DE PIE BARRIENDO       0.61      0.53      0.57       184
   DE PIE DOBLANDO TOALLAS       0.36      0.48      0.41       184
    DE PIE MOVIENDO LIBROS       0.32      0.53      0.40       184
          DE PIE USANDO PC       0.24      0.27      0.25       184
        FASE REPOSO CON K5       0.61      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.70      0.68      0.69       184
           SENTADO LEYENDO       0.48      0.47      0.48       184
         SENTADO USANDO PC       0.39      0.29      0.33       184
      SENTADO VIENDO LA TV       0.21      0.14      0.17       184
   SUBIR Y BAJAR ESCALERAS       0.75      0.58      0.65       184
                    TROTAR       0.94      0.55      0.70       161

                  accuracy                           0.48      2737
                 macro avg       0.52      0.48      0.48      2737
              weighted avg       0.51      0.48      0.48      2737


Accuracy capturado en la ejecución 19: 47.83 [%]
F1-score capturado en la ejecución 19: 48.18 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.61 [%]
Global accuracy score (test) = 47.42 [%]
Global F1 score (train) = 53.61 [%]
Global F1 score (test) = 47.8 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.50      0.71      0.59       184
 CAMINAR CON MÓVIL O LIBRO       0.75      0.42      0.54       184
       CAMINAR USUAL SPEED       0.56      0.32      0.40       184
            CAMINAR ZIGZAG       0.30      0.45      0.36       184
          DE PIE BARRIENDO       0.60      0.54      0.57       184
   DE PIE DOBLANDO TOALLAS       0.36      0.48      0.41       184
    DE PIE MOVIENDO LIBROS       0.33      0.54      0.41       184
          DE PIE USANDO PC       0.20      0.22      0.21       184
        FASE REPOSO CON K5       0.62      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.70      0.67      0.69       184
           SENTADO LEYENDO       0.50      0.47      0.48       184
         SENTADO USANDO PC       0.39      0.31      0.34       184
      SENTADO VIENDO LA TV       0.20      0.14      0.16       184
   SUBIR Y BAJAR ESCALERAS       0.73      0.57      0.64       184
                    TROTAR       0.94      0.55      0.70       161

                  accuracy                           0.47      2737
                 macro avg       0.51      0.47      0.48      2737
              weighted avg       0.51      0.47      0.48      2737


Accuracy capturado en la ejecución 20: 47.42 [%]
F1-score capturado en la ejecución 20: 47.8 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 54.08 [%]
Global accuracy score (test) = 47.17 [%]
Global F1 score (train) = 54.09 [%]
Global F1 score (test) = 47.57 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.52      0.69      0.59       184
 CAMINAR CON MÓVIL O LIBRO       0.74      0.40      0.52       184
       CAMINAR USUAL SPEED       0.57      0.31      0.40       184
            CAMINAR ZIGZAG       0.30      0.48      0.37       184
          DE PIE BARRIENDO       0.60      0.51      0.55       184
   DE PIE DOBLANDO TOALLAS       0.35      0.48      0.40       184
    DE PIE MOVIENDO LIBROS       0.32      0.53      0.40       184
          DE PIE USANDO PC       0.21      0.23      0.22       184
        FASE REPOSO CON K5       0.61      0.75      0.67       184
INCREMENTAL CICLOERGOMETRO       0.72      0.69      0.70       184
           SENTADO LEYENDO       0.49      0.46      0.47       184
         SENTADO USANDO PC       0.40      0.32      0.35       184
      SENTADO VIENDO LA TV       0.20      0.13      0.16       184
   SUBIR Y BAJAR ESCALERAS       0.69      0.55      0.61       184
                    TROTAR       0.96      0.55      0.70       161

                  accuracy                           0.47      2737
                 macro avg       0.51      0.47      0.48      2737
              weighted avg       0.51      0.47      0.47      2737


Accuracy capturado en la ejecución 21: 47.17 [%]
F1-score capturado en la ejecución 21: 47.57 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
2025-11-06 17:44:49.686080: 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`.
2025-11-06 17:44:49.697285: 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 00:00:1762447489.710296 1639208 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447489.714368 1639208 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447489.724168 1639208 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447489.724189 1639208 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447489.724191 1639208 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447489.724192 1639208 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:44:49.727283: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.3s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.9s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.2s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:45:16.200541: 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`.
2025-11-06 17:45:16.211822: 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 00:00:1762447516.225672 1639705 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447516.229787 1639705 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447516.239784 1639705 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447516.239802 1639705 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447516.239805 1639705 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447516.239807 1639705 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:45:16.242906: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.3s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.9s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.2s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:45:42.633111: 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`.
2025-11-06 17:45:42.644307: 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 00:00:1762447542.657406 1640178 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447542.661490 1640178 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447542.671354 1640178 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447542.671374 1640178 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447542.671376 1640178 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447542.671378 1640178 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:45:42.674452: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.2s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.9s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.1s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.57 [%]
Global accuracy score (test) = 46.84 [%]
Global F1 score (train) = 53.58 [%]
Global F1 score (test) = 47.29 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.52      0.70      0.60       184
 CAMINAR CON MÓVIL O LIBRO       0.74      0.41      0.52       184
       CAMINAR USUAL SPEED       0.56      0.31      0.40       184
            CAMINAR ZIGZAG       0.29      0.47      0.36       184
          DE PIE BARRIENDO       0.64      0.51      0.57       184
   DE PIE DOBLANDO TOALLAS       0.35      0.49      0.41       184
    DE PIE MOVIENDO LIBROS       0.32      0.53      0.40       184
          DE PIE USANDO PC       0.19      0.21      0.20       184
        FASE REPOSO CON K5       0.62      0.76      0.68       184
INCREMENTAL CICLOERGOMETRO       0.72      0.67      0.69       184
           SENTADO LEYENDO       0.48      0.46      0.47       184
         SENTADO USANDO PC       0.38      0.30      0.34       184
      SENTADO VIENDO LA TV       0.17      0.11      0.13       184
   SUBIR Y BAJAR ESCALERAS       0.74      0.55      0.63       184
                    TROTAR       0.94      0.55      0.70       161

                  accuracy                           0.47      2737
                 macro avg       0.51      0.47      0.47      2737
              weighted avg       0.51      0.47      0.47      2737


Accuracy capturado en la ejecución 22: 46.84 [%]
F1-score capturado en la ejecución 22: 47.29 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.9 [%]
Global accuracy score (test) = 47.9 [%]
Global F1 score (train) = 53.93 [%]
Global F1 score (test) = 48.31 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.72      0.61       184
 CAMINAR CON MÓVIL O LIBRO       0.75      0.42      0.54       184
       CAMINAR USUAL SPEED       0.57      0.30      0.39       184
            CAMINAR ZIGZAG       0.29      0.47      0.36       184
          DE PIE BARRIENDO       0.62      0.55      0.58       184
   DE PIE DOBLANDO TOALLAS       0.35      0.48      0.40       184
    DE PIE MOVIENDO LIBROS       0.32      0.55      0.40       184
          DE PIE USANDO PC       0.25      0.29      0.27       184
        FASE REPOSO CON K5       0.61      0.75      0.67       184
INCREMENTAL CICLOERGOMETRO       0.73      0.69      0.71       184
           SENTADO LEYENDO       0.49      0.46      0.47       184
         SENTADO USANDO PC       0.39      0.30      0.34       184
      SENTADO VIENDO LA TV       0.19      0.11      0.14       184
   SUBIR Y BAJAR ESCALERAS       0.77      0.55      0.64       184
                    TROTAR       0.99      0.55      0.71       161

                  accuracy                           0.48      2737
                 macro avg       0.52      0.48      0.48      2737
              weighted avg       0.52      0.48      0.48      2737


Accuracy capturado en la ejecución 23: 47.9 [%]
F1-score capturado en la ejecución 23: 48.31 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.65 [%]
Global accuracy score (test) = 46.77 [%]
Global F1 score (train) = 53.62 [%]
Global F1 score (test) = 47.08 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.52      0.72      0.60       184
 CAMINAR CON MÓVIL O LIBRO       0.74      0.40      0.52       184
       CAMINAR USUAL SPEED       0.56      0.30      0.39       184
            CAMINAR ZIGZAG       0.29      0.46      0.35       184
          DE PIE BARRIENDO       0.57      0.48      0.52       184
   DE PIE DOBLANDO TOALLAS       0.35      0.48      0.41       184
    DE PIE MOVIENDO LIBROS       0.33      0.53      0.40       184
          DE PIE USANDO PC       0.21      0.21      0.21       184
        FASE REPOSO CON K5       0.61      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.72      0.68      0.70       184
           SENTADO LEYENDO       0.47      0.46      0.47       184
         SENTADO USANDO PC       0.39      0.32      0.35       184
      SENTADO VIENDO LA TV       0.20      0.14      0.16       184
   SUBIR Y BAJAR ESCALERAS       0.72      0.55      0.63       184
                    TROTAR       0.90      0.55      0.68       161

                  accuracy                           0.47      2737
                 macro avg       0.50      0.47      0.47      2737
              weighted avg       0.50      0.47      0.47      2737


Accuracy capturado en la ejecución 24: 46.77 [%]
F1-score capturado en la ejecución 24: 47.08 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
2025-11-06 17:46:09.150840: 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`.
2025-11-06 17:46:09.162345: 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 00:00:1762447569.176027 1640657 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447569.180250 1640657 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447569.190251 1640657 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447569.190278 1640657 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447569.190280 1640657 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447569.190282 1640657 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:46:09.193475: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.3s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.0s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.2s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:46:35.807628: 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`.
2025-11-06 17:46:35.819065: 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 00:00:1762447595.832374 1641128 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447595.836693 1641128 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447595.846699 1641128 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447595.846719 1641128 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447595.846721 1641128 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447595.846723 1641128 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:46:35.849893: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.3s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.9s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.2s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:47:02.444312: 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`.
2025-11-06 17:47:02.455686: 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 00:00:1762447622.469044 1641632 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447622.473186 1641632 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447622.483174 1641632 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447622.483195 1641632 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447622.483197 1641632 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447622.483199 1641632 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:47:02.486340: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.2s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.9s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.1s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.8 [%]
Global accuracy score (test) = 47.24 [%]
Global F1 score (train) = 53.85 [%]
Global F1 score (test) = 47.64 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.72      0.61       184
 CAMINAR CON MÓVIL O LIBRO       0.74      0.40      0.52       184
       CAMINAR USUAL SPEED       0.58      0.33      0.42       184
            CAMINAR ZIGZAG       0.29      0.47      0.36       184
          DE PIE BARRIENDO       0.61      0.47      0.53       184
   DE PIE DOBLANDO TOALLAS       0.34      0.48      0.40       184
    DE PIE MOVIENDO LIBROS       0.30      0.53      0.38       184
          DE PIE USANDO PC       0.24      0.26      0.25       184
        FASE REPOSO CON K5       0.61      0.75      0.67       184
INCREMENTAL CICLOERGOMETRO       0.71      0.68      0.70       184
           SENTADO LEYENDO       0.49      0.46      0.48       184
         SENTADO USANDO PC       0.36      0.29      0.32       184
      SENTADO VIENDO LA TV       0.21      0.13      0.16       184
   SUBIR Y BAJAR ESCALERAS       0.77      0.58      0.66       184
                    TROTAR       0.86      0.55      0.67       161

                  accuracy                           0.47      2737
                 macro avg       0.51      0.47      0.48      2737
              weighted avg       0.51      0.47      0.47      2737


Accuracy capturado en la ejecución 25: 47.24 [%]
F1-score capturado en la ejecución 25: 47.64 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.95 [%]
Global accuracy score (test) = 47.83 [%]
Global F1 score (train) = 53.99 [%]
Global F1 score (test) = 48.25 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.71      0.61       184
 CAMINAR CON MÓVIL O LIBRO       0.75      0.40      0.52       184
       CAMINAR USUAL SPEED       0.56      0.33      0.41       184
            CAMINAR ZIGZAG       0.30      0.48      0.37       184
          DE PIE BARRIENDO       0.64      0.53      0.58       184
   DE PIE DOBLANDO TOALLAS       0.35      0.48      0.41       184
    DE PIE MOVIENDO LIBROS       0.32      0.53      0.40       184
          DE PIE USANDO PC       0.22      0.21      0.22       184
        FASE REPOSO CON K5       0.60      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.72      0.69      0.71       184
           SENTADO LEYENDO       0.49      0.46      0.48       184
         SENTADO USANDO PC       0.42      0.36      0.38       184
      SENTADO VIENDO LA TV       0.20      0.15      0.17       184
   SUBIR Y BAJAR ESCALERAS       0.71      0.56      0.62       184
                    TROTAR       0.94      0.55      0.70       161

                  accuracy                           0.48      2737
                 macro avg       0.52      0.48      0.48      2737
              weighted avg       0.51      0.48      0.48      2737


Accuracy capturado en la ejecución 26: 47.83 [%]
F1-score capturado en la ejecución 26: 48.25 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.69 [%]
Global accuracy score (test) = 47.53 [%]
Global F1 score (train) = 53.73 [%]
Global F1 score (test) = 48.03 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.71      0.61       184
 CAMINAR CON MÓVIL O LIBRO       0.73      0.40      0.52       184
       CAMINAR USUAL SPEED       0.59      0.34      0.43       184
            CAMINAR ZIGZAG       0.30      0.47      0.36       184
          DE PIE BARRIENDO       0.63      0.53      0.58       184
   DE PIE DOBLANDO TOALLAS       0.34      0.48      0.40       184
    DE PIE MOVIENDO LIBROS       0.32      0.54      0.40       184
          DE PIE USANDO PC       0.22      0.22      0.22       184
        FASE REPOSO CON K5       0.62      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.73      0.68      0.70       184
           SENTADO LEYENDO       0.49      0.46      0.47       184
         SENTADO USANDO PC       0.37      0.33      0.35       184
      SENTADO VIENDO LA TV       0.21      0.14      0.17       184
   SUBIR Y BAJAR ESCALERAS       0.73      0.55      0.63       184
                    TROTAR       0.96      0.55      0.70       161

                  accuracy                           0.48      2737
                 macro avg       0.52      0.48      0.48      2737
              weighted avg       0.51      0.48      0.48      2737


Accuracy capturado en la ejecución 27: 47.53 [%]
F1-score capturado en la ejecución 27: 48.03 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
2025-11-06 17:47:29.030666: 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`.
2025-11-06 17:47:29.042033: 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 00:00:1762447649.055188 1642104 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447649.059163 1642104 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447649.069235 1642104 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447649.069254 1642104 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447649.069256 1642104 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447649.069258 1642104 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:47:29.072497: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.2s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    2.9s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.2s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | elapsed:    0.1s finished
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Saved model to disk
2025-11-06 17:47:55.552936: 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`.
2025-11-06 17:47:55.564234: 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 00:00:1762447675.577751 1642583 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762447675.581864 1642583 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762447675.591835 1642583 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447675.591856 1642583 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447675.591858 1642583 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762447675.591861 1642583 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 17:47:55.595014: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/sklearn/base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    1.3s
[Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:    3.1s
[Parallel(n_jobs=-1)]: Done 458 out of 458 | elapsed:    3.4s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s
[Parallel(n_jobs=20)]: Done 458 out of 458 | 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.95 [%]
Global accuracy score (test) = 47.1 [%]
Global F1 score (train) = 54.02 [%]
Global F1 score (test) = 47.63 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.71      0.61       184
 CAMINAR CON MÓVIL O LIBRO       0.73      0.40      0.51       184
       CAMINAR USUAL SPEED       0.60      0.32      0.41       184
            CAMINAR ZIGZAG       0.29      0.48      0.36       184
          DE PIE BARRIENDO       0.64      0.49      0.56       184
   DE PIE DOBLANDO TOALLAS       0.35      0.47      0.40       184
    DE PIE MOVIENDO LIBROS       0.32      0.55      0.40       184
          DE PIE USANDO PC       0.19      0.22      0.20       184
        FASE REPOSO CON K5       0.61      0.74      0.67       184
INCREMENTAL CICLOERGOMETRO       0.71      0.67      0.69       184
           SENTADO LEYENDO       0.50      0.46      0.48       184
         SENTADO USANDO PC       0.38      0.28      0.32       184
      SENTADO VIENDO LA TV       0.20      0.14      0.17       184
   SUBIR Y BAJAR ESCALERAS       0.76      0.59      0.66       184
                    TROTAR       0.92      0.55      0.69       161

                  accuracy                           0.47      2737
                 macro avg       0.52      0.47      0.48      2737
              weighted avg       0.51      0.47      0.47      2737


Accuracy capturado en la ejecución 28: 47.1 [%]
F1-score capturado en la ejecución 28: 47.63 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.84 [%]
Global accuracy score (test) = 47.86 [%]
Global F1 score (train) = 53.88 [%]
Global F1 score (test) = 48.29 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.73      0.61       184
 CAMINAR CON MÓVIL O LIBRO       0.75      0.42      0.54       184
       CAMINAR USUAL SPEED       0.58      0.34      0.43       184
            CAMINAR ZIGZAG       0.31      0.48      0.37       184
          DE PIE BARRIENDO       0.61      0.51      0.55       184
   DE PIE DOBLANDO TOALLAS       0.36      0.48      0.41       184
    DE PIE MOVIENDO LIBROS       0.32      0.54      0.40       184
          DE PIE USANDO PC       0.22      0.24      0.23       184
        FASE REPOSO CON K5       0.61      0.75      0.67       184
INCREMENTAL CICLOERGOMETRO       0.74      0.69      0.72       184
           SENTADO LEYENDO       0.48      0.47      0.48       184
         SENTADO USANDO PC       0.36      0.30      0.33       184
      SENTADO VIENDO LA TV       0.19      0.12      0.15       184
   SUBIR Y BAJAR ESCALERAS       0.77      0.57      0.65       184
                    TROTAR       0.94      0.55      0.70       161

                  accuracy                           0.48      2737
                 macro avg       0.52      0.48      0.48      2737
              weighted avg       0.51      0.48      0.48      2737


Accuracy capturado en la ejecución 29: 47.86 [%]
F1-score capturado en la ejecución 29: 48.29 [%]

=== 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)
(23506, 42)
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[8. 8. 8. ... 3. 3. 3.]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 53.8 [%]
Global accuracy score (test) = 47.53 [%]
Global F1 score (train) = 53.85 [%]
Global F1 score (test) = 47.93 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.71      0.60       184
 CAMINAR CON MÓVIL O LIBRO       0.75      0.43      0.54       184
       CAMINAR USUAL SPEED       0.58      0.31      0.40       184
            CAMINAR ZIGZAG       0.31      0.49      0.38       184
          DE PIE BARRIENDO       0.66      0.51      0.58       184
   DE PIE DOBLANDO TOALLAS       0.34      0.48      0.40       184
    DE PIE MOVIENDO LIBROS       0.32      0.54      0.40       184
          DE PIE USANDO PC       0.21      0.23      0.22       184
        FASE REPOSO CON K5       0.61      0.75      0.67       184
INCREMENTAL CICLOERGOMETRO       0.70      0.68      0.69       184
           SENTADO LEYENDO       0.49      0.46      0.47       184
         SENTADO USANDO PC       0.37      0.28      0.32       184
      SENTADO VIENDO LA TV       0.21      0.14      0.17       184
   SUBIR Y BAJAR ESCALERAS       0.74      0.57      0.64       184
                    TROTAR       0.92      0.55      0.69       161

                  accuracy                           0.48      2737
                 macro avg       0.52      0.48      0.48      2737
              weighted avg       0.51      0.48      0.48      2737


Accuracy capturado en la ejecución 30: 47.53 [%]
F1-score capturado en la ejecución 30: 47.93 [%]

=== RESUMEN FINAL ===
Accuracies: [47.28, 47.68, 47.31, 46.8, 47.2, 47.68, 47.61, 47.13, 47.17, 46.91, 47.13, 47.17, 47.64, 48.08, 46.88, 48.3, 47.24, 47.13, 47.83, 47.42, 47.17, 46.84, 47.9, 46.77, 47.24, 47.83, 47.53, 47.1, 47.86, 47.53]
F1-scores: [47.64, 48.07, 47.79, 47.11, 47.51, 48.02, 48.06, 47.56, 47.58, 47.29, 47.44, 47.54, 48.07, 48.62, 47.17, 48.81, 47.73, 47.7, 48.18, 47.8, 47.57, 47.29, 48.31, 47.08, 47.64, 48.25, 48.03, 47.63, 48.29, 47.93]
Accuracy mean: 47.3787 | std: 0.3892
F1 mean: 47.7903 | std: 0.4209

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_BRF_acc_17_classes/metrics_test.npz
