2025-11-06 13:02:01.016030: 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 13:02:01.027673: 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 00:00:1762430521.042117 1283073 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762430521.046444 1283073 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762430521.056813 1283073 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430521.056839 1283073 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430521.056841 1283073 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430521.056843 1283073 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:02:01.060085: 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 13:02:10,536	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-06 13:02:11,240	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-06 13:02:11,304	INFO trial.py:182 -- Creating a new dirname dir_6d110_5fcd because trial dirname 'dir_6d110' already exists.
2025-11-06 13:02:11,306	INFO trial.py:182 -- Creating a new dirname dir_6d110_dfbd because trial dirname 'dir_6d110' already exists.
2025-11-06 13:02:11,309	INFO trial.py:182 -- Creating a new dirname dir_6d110_bd94 because trial dirname 'dir_6d110' already exists.
2025-11-06 13:02:11,311	INFO trial.py:182 -- Creating a new dirname dir_6d110_d125 because trial dirname 'dir_6d110' already exists.
2025-11-06 13:02:11,313	INFO trial.py:182 -- Creating a new dirname dir_6d110_c5d2 because trial dirname 'dir_6d110' already exists.
2025-11-06 13:02:11,316	INFO trial.py:182 -- Creating a new dirname dir_6d110_0a19 because trial dirname 'dir_6d110' already exists.
2025-11-06 13:02:11,318	INFO trial.py:182 -- Creating a new dirname dir_6d110_5af4 because trial dirname 'dir_6d110' already exists.
2025-11-06 13:02:11,321	INFO trial.py:182 -- Creating a new dirname dir_6d110_2794 because trial dirname 'dir_6d110' already exists.
2025-11-06 13:02:11,324	INFO trial.py:182 -- Creating a new dirname dir_6d110_3048 because trial dirname 'dir_6d110' already exists.
2025-11-06 13:02:11,326	INFO trial.py:182 -- Creating a new dirname dir_6d110_d31d because trial dirname 'dir_6d110' already exists.
2025-11-06 13:02:11,330	INFO trial.py:182 -- Creating a new dirname dir_6d110_d77e because trial dirname 'dir_6d110' already exists.
2025-11-06 13:02:11,333	INFO trial.py:182 -- Creating a new dirname dir_6d110_05e4 because trial dirname 'dir_6d110' already exists.
2025-11-06 13:02:11,336	INFO trial.py:182 -- Creating a new dirname dir_6d110_b949 because trial dirname 'dir_6d110' already exists.
2025-11-06 13:02:11,341	INFO trial.py:182 -- Creating a new dirname dir_6d110_b2c2 because trial dirname 'dir_6d110' already exists.
2025-11-06 13:02:11,346	INFO trial.py:182 -- Creating a new dirname dir_6d110_ccc8 because trial dirname 'dir_6d110' already exists.
2025-11-06 13:02:11,349	INFO trial.py:182 -- Creating a new dirname dir_6d110_4110 because trial dirname 'dir_6d110' already exists.
2025-11-06 13:02:11,358	INFO trial.py:182 -- Creating a new dirname dir_6d110_40f1 because trial dirname 'dir_6d110' already exists.
2025-11-06 13:02:11,366	INFO trial.py:182 -- Creating a new dirname dir_6d110_4780 because trial dirname 'dir_6d110' already exists.
2025-11-06 13:02:11,376	INFO trial.py:182 -- Creating a new dirname dir_6d110_0ea0 because trial dirname 'dir_6d110' 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_PI/case_PI_BRF_acc_17_classes/BalancedRF_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-06_13-02-09_831213_1283073/artifacts/2025-11-06_13-02-11/BalancedRF_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-06 13:02:11. Total running time: 0s
Logical resource usage: 14.0/20 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_6d110    PENDING               291             5                    39                   17   sqrt                       5213 │
│ trial_6d110    PENDING               262             6                    48                   13   0.3                        9253 │
│ trial_6d110    PENDING               475             7                    20                   16   0.3                        3916 │
│ trial_6d110    PENDING               304             6                    29                   16   sqrt                       1508 │
│ trial_6d110    PENDING               386             5                    40                   14   sqrt                       5394 │
│ trial_6d110    PENDING               392             6                    53                   27   0.3                        9960 │
│ trial_6d110    PENDING               431             5                    40                   15   0.3                        6650 │
│ trial_6d110    PENDING               371             7                    33                   27   sqrt                       4330 │
│ trial_6d110    PENDING               233             6                    43                   20   sqrt                       3961 │
│ trial_6d110    PENDING               365             6                    27                   21   0.3                         806 │
│ trial_6d110    PENDING               498             6                    20                   25   0.3                        6990 │
│ trial_6d110    PENDING               246             7                    20                   20   0.3                        7867 │
│ trial_6d110    PENDING               418             5                    38                   18   0.3                        5968 │
│ trial_6d110    PENDING               303             7                    24                   19   sqrt                       8779 │
│ trial_6d110    PENDING               461             5                    27                   10   sqrt                       5549 │
│ trial_6d110    PENDING               425             6                    37                   12   0.3                        3847 │
│ trial_6d110    PENDING               260             7                    46                   16   0.3                        1306 │
│ trial_6d110    PENDING               403             6                    20                   22   0.3                          25 │
│ trial_6d110    PENDING               412             6                    30                   14   sqrt                       2871 │
│ trial_6d110    PENDING               261             5                    50                   10   sqrt                        229 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_6d110 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d110 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               16 │
│ min_samples_split              20 │
│ n_estimators                  475 │
│ random_state                 3916 │
╰───────────────────────────────────╯
Trial trial_6d110 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d110 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               19 │
│ min_samples_split              24 │
│ n_estimators                  303 │
│ random_state                 8779 │
╰───────────────────────────────────╯
Trial trial_6d110 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d110 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               17 │
│ min_samples_split              39 │
│ n_estimators                  291 │
│ random_state                 5213 │
╰───────────────────────────────────╯
Trial trial_6d110 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d110 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               20 │
│ min_samples_split              43 │
│ n_estimators                  233 │
│ random_state                 3961 │
╰───────────────────────────────────╯
Trial trial_6d110 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d110 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               14 │
│ min_samples_split              30 │
│ n_estimators                  412 │
│ random_state                 2871 │
╰───────────────────────────────────╯
Trial trial_6d110 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d110 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               13 │
│ min_samples_split              48 │
│ n_estimators                  262 │
│ random_state                 9253 │
╰───────────────────────────────────╯
Trial trial_6d110 started with configuration:
╭──────────────────────────────────╮
│ Trial trial_6d110 config         │
├──────────────────────────────────┤
│ max_depth                      6 │
│ max_features                 0.3 │
│ min_samples_leaf              21 │
│ min_samples_split             27 │
│ n_estimators                 365 │
│ random_state                 806 │
╰──────────────────────────────────╯
Trial trial_6d110 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d110 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               14 │
│ min_samples_split              40 │
│ n_estimators                  386 │
│ random_state                 5394 │
╰───────────────────────────────────╯
Trial trial_6d110 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d110 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               25 │
│ min_samples_split              20 │
│ n_estimators                  498 │
│ random_state                 6990 │
╰───────────────────────────────────╯
Trial trial_6d110 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d110 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               10 │
│ min_samples_split              27 │
│ n_estimators                  461 │
│ random_state                 5549 │
╰───────────────────────────────────╯
Trial trial_6d110 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d110 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               27 │
│ min_samples_split              53 │
│ n_estimators                  392 │
│ random_state                 9960 │
╰───────────────────────────────────╯
Trial trial_6d110 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d110 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                  0.3 │
│ min_samples_leaf               12 │
│ min_samples_split              37 │
│ n_estimators                  425 │
│ random_state                 3847 │
╰───────────────────────────────────╯
Trial trial_6d110 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d110 config          │
├───────────────────────────────────┤
│ max_depth                       6 │
│ max_features                 sqrt │
│ min_samples_leaf               16 │
│ min_samples_split              29 │
│ n_estimators                  304 │
│ random_state                 1508 │
╰───────────────────────────────────╯
Trial trial_6d110 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d110 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               20 │
│ min_samples_split              20 │
│ n_estimators                  246 │
│ random_state                 7867 │
╰───────────────────────────────────╯
Trial trial_6d110 started with configuration:
╭──────────────────────────────────╮
│ Trial trial_6d110 config         │
├──────────────────────────────────┤
│ max_depth                      6 │
│ max_features                 0.3 │
│ min_samples_leaf              22 │
│ min_samples_split             20 │
│ n_estimators                 403 │
│ random_state                  25 │
╰──────────────────────────────────╯
Trial trial_6d110 started with configuration:
[36m(train_brf_ray_tune pid=1285152)[0m 2025-11-06 13:02:14.708177: 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=1285152)[0m 2025-11-06 13:02:14.731407: 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=1285164)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_brf_ray_tune pid=1285164)[0m E0000 00:00:1762430534.720256 1286335 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=1285164)[0m E0000 00:00:1762430534.728079 1286335 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=1285152)[0m W0000 00:00:1762430534.788686 1286333 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=1285152)[0m W0000 00:00:1762430534.788750 1286333 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=1285152)[0m W0000 00:00:1762430534.788752 1286333 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=1285152)[0m W0000 00:00:1762430534.788755 1286333 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=1285152)[0m 2025-11-06 13:02:14.795119: 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=1285152)[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=1285109)[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=1285109)[0m   return fit_method(estimator, *args, **kwargs)
[36m(train_brf_ray_tune pid=1285109)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=1285109)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    0.6s
[36m(train_brf_ray_tune pid=1285146)[0m 2025-11-06 13:02:15.190825: 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=1285166)[0m 2025-11-06 13:02:15.291493: 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=1285166)[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=1285166)[0m E0000 00:00:1762430535.320676 1286481 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=1285166)[0m E0000 00:00:1762430535.328842 1286481 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=1285166)[0m W0000 00:00:1762430535.349610 1286481 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=1285146)[0m 2025-11-06 13:02:15.299602: 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=1285146)[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=1285110)[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=1285110)[0m   return fit_method(estimator, *args, **kwargs)[32m [repeated 19x across cluster][0m
[36m(train_brf_ray_tune pid=1285166)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 16x across cluster][0m
[36m(train_brf_ray_tune pid=1285146)[0m [Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    4.3s[32m [repeated 11x across cluster][0m
[36m(train_brf_ray_tune pid=1285135)[0m [Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 3x across cluster][0m
[36m(train_brf_ray_tune pid=1285109)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   12.9s[32m [repeated 8x across cluster][0m
[36m(train_brf_ray_tune pid=1285165)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   15.9s[32m [repeated 7x across cluster][0m
[36m(train_brf_ray_tune pid=1285109)[0m [Parallel(n_jobs=-1)]: Done 233 out of 233 | elapsed:   21.1s finished
[36m(train_brf_ray_tune pid=1285109)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=1285109)[0m [Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.6s[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=1285109)[0m [Parallel(n_jobs=20)]: Done 233 out of 233 | elapsed:    0.8s finished
[36m(train_brf_ray_tune pid=1285109)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=1285109)[0m [Parallel(n_jobs=20)]: Done 233 out of 233 | elapsed:    0.9s finished
[36m(train_brf_ray_tune pid=1285109)[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=1285109)[0m   _log_deprecation_warning(
╭───────────────────────────────────╮
│ Trial trial_6d110 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                 sqrt │
│ min_samples_leaf               10 │
│ min_samples_split              50 │
│ n_estimators                  261 │
│ random_state                  229 │
╰───────────────────────────────────╯
Trial trial_6d110 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d110 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                  0.3 │
│ min_samples_leaf               16 │
│ min_samples_split              46 │
│ n_estimators                  260 │
│ random_state                 1306 │
╰───────────────────────────────────╯
Trial trial_6d110 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d110 config          │
├───────────────────────────────────┤
│ max_depth                       7 │
│ max_features                 sqrt │
│ min_samples_leaf               27 │
│ min_samples_split              33 │
│ n_estimators                  371 │
│ random_state                 4330 │
╰───────────────────────────────────╯
Trial trial_6d110 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d110 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               18 │
│ min_samples_split              38 │
│ n_estimators                  418 │
│ random_state                 5968 │
╰───────────────────────────────────╯
Trial trial_6d110 started with configuration:
╭───────────────────────────────────╮
│ Trial trial_6d110 config          │
├───────────────────────────────────┤
│ max_depth                       5 │
│ max_features                  0.3 │
│ min_samples_leaf               15 │
│ min_samples_split              40 │
│ n_estimators                  431 │
│ random_state                 6650 │
╰───────────────────────────────────╯

Trial status: 20 RUNNING
Current time: 2025-11-06 13:02:41. Total running time: 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       n_estimators     max_depth     min_samples_split     min_samples_leaf   max_features       random_state │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_6d110    RUNNING               291             5                    39                   17   sqrt                       5213 │
│ trial_6d110    RUNNING               262             6                    48                   13   0.3                        9253 │
│ trial_6d110    RUNNING               475             7                    20                   16   0.3                        3916 │
│ trial_6d110    RUNNING               304             6                    29                   16   sqrt                       1508 │
│ trial_6d110    RUNNING               386             5                    40                   14   sqrt                       5394 │
│ trial_6d110    RUNNING               392             6                    53                   27   0.3                        9960 │
│ trial_6d110    RUNNING               431             5                    40                   15   0.3                        6650 │
│ trial_6d110    RUNNING               371             7                    33                   27   sqrt                       4330 │
│ trial_6d110    RUNNING               233             6                    43                   20   sqrt                       3961 │
│ trial_6d110    RUNNING               365             6                    27                   21   0.3                         806 │
│ trial_6d110    RUNNING               498             6                    20                   25   0.3                        6990 │
│ trial_6d110    RUNNING               246             7                    20                   20   0.3                        7867 │
│ trial_6d110    RUNNING               418             5                    38                   18   0.3                        5968 │
│ trial_6d110    RUNNING               303             7                    24                   19   sqrt                       8779 │
│ trial_6d110    RUNNING               461             5                    27                   10   sqrt                       5549 │
│ trial_6d110    RUNNING               425             6                    37                   12   0.3                        3847 │
│ trial_6d110    RUNNING               260             7                    46                   16   0.3                        1306 │
│ trial_6d110    RUNNING               403             6                    20                   22   0.3                          25 │
│ trial_6d110    RUNNING               412             6                    30                   14   sqrt                       2871 │
│ trial_6d110    RUNNING               261             5                    50                   10   sqrt                        229 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:02:43. Total running time: 31s
[36m(train_brf_ray_tune pid=1285165)[0m [Parallel(n_jobs=-1)]: Done 261 out of 261 | elapsed:   25.9s finished
[36m(train_brf_ray_tune pid=1285165)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=1285146)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   26.8s[32m [repeated 6x across cluster][0m
[36m(train_brf_ray_tune pid=1285165)[0m [Parallel(n_jobs=20)]: Done 261 out of 261 | elapsed:    0.8s finished
[36m(train_brf_ray_tune pid=1285165)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[36m(train_brf_ray_tune pid=1285165)[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=1285165)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=1285165)[0m [Parallel(n_jobs=20)]: Done 261 out of 261 | elapsed:    0.9s finished[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=1285143)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=1285152)[0m [Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:   33.8s[32m [repeated 11x across cluster][0m
[36m(train_brf_ray_tune pid=1285143)[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=1285143)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=1285132)[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=1285132)[0m   _log_deprecation_warning(
[36m(train_brf_ray_tune pid=1285164)[0m [Parallel(n_jobs=20)]: Done 303 out of 303 | elapsed:    0.6s finished[32m [repeated 8x across cluster][0m
[36m(train_brf_ray_tune pid=1285132)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=1285136)[0m [Parallel(n_jobs=-1)]: Done 410 tasks      | elapsed:   39.0s[32m [repeated 15x across cluster][0m
[36m(train_brf_ray_tune pid=1285137)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=1285137)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=1285136)[0m [Parallel(n_jobs=-1)]: Done 461 out of 461 | elapsed:   42.5s finished[32m [repeated 6x across cluster][0m
[36m(train_brf_ray_tune pid=1285122)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=1285122)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.7s[32m [repeated 8x across cluster][0m
[36m(train_brf_ray_tune pid=1285110)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=1285110)[0m   _log_deprecation_warning([32m [repeated 4x across cluster][0m
[36m(train_brf_ray_tune pid=1285145)[0m [Parallel(n_jobs=-1)]: Done 246 out of 246 | elapsed:   47.1s finished[32m [repeated 10x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             28.6242 │
│ time_total_s                 28.6242 │
│ training_iteration                 1 │
│ test_accuracy                0.58946 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:02:43. Total running time: 31s

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:02:49. Total running time: 38s
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             35.2212 │
│ time_total_s                 35.2212 │
│ training_iteration                 1 │
│ test_accuracy                0.56731 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:02:49. Total running time: 38s

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:02:53. Total running time: 42s
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             39.2529 │
│ time_total_s                 39.2529 │
│ training_iteration                 1 │
│ test_accuracy                 0.5949 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:02:54. Total running time: 42s

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:02:56. Total running time: 44s
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             41.7534 │
│ time_total_s                 41.7534 │
│ training_iteration                 1 │
│ test_accuracy                0.56814 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:02:56. Total running time: 44s

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:02:56. Total running time: 44s
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             41.9711 │
│ time_total_s                 41.9711 │
│ training_iteration                 1 │
│ test_accuracy                0.60326 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:02:56. Total running time: 45s

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:03:00. Total running time: 49s
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             46.3019 │
│ time_total_s                 46.3019 │
│ training_iteration                 1 │
│ test_accuracy                0.59783 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:03:00. Total running time: 49s

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:03:03. Total running time: 52s
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             49.1955 │
│ time_total_s                 49.1955 │
│ training_iteration                 1 │
│ test_accuracy                0.58737 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:03:03. Total running time: 52s

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:03:04. Total running time: 53s
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s               50.37 │
│ time_total_s                   50.37 │
│ training_iteration                 1 │
│ test_accuracy                0.56438 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:03:04. Total running time: 53s

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:03:05. Total running time: 53s
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             50.5957 │
│ time_total_s                 50.5957 │
│ training_iteration                 1 │
│ test_accuracy                0.57441 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:03:05. Total running time: 53s

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:03:05. Total running time: 54s
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              51.142 │
│ time_total_s                  51.142 │
│ training_iteration                 1 │
│ test_accuracy                0.60619 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:03:06. Total running time: 54s

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:03:08. Total running time: 57s
[36m(train_brf_ray_tune pid=1285145)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 8x across cluster][0m
[36m(train_brf_ray_tune pid=1285145)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.2s[32m [repeated 18x across cluster][0m
[36m(train_brf_ray_tune pid=1285166)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=1285166)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_brf_ray_tune pid=1285146)[0m [Parallel(n_jobs=20)]: Done 403 out of 403 | elapsed:    0.2s finished[32m [repeated 9x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             53.8191 │
│ time_total_s                 53.8191 │
│ training_iteration                 1 │
│ test_accuracy                0.59156 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:03:08. Total running time: 57s

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:03:10. Total running time: 59s
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             55.9321 │
│ time_total_s                 55.9321 │
│ training_iteration                 1 │
│ test_accuracy                0.58361 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:03:10. Total running time: 59s

Trial status: 12 TERMINATED | 8 RUNNING
Current time: 2025-11-06 13:03:11. Total running time: 1min 0s
Logical resource usage: 8.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         n_estimators     max_depth     min_samples_split     min_samples_leaf   max_features       random_state     iter     total time (s)     test_accuracy │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_6d110    RUNNING                 475             7                    20                   16   0.3                        3916                                               │
│ trial_6d110    RUNNING                 392             6                    53                   27   0.3                        9960                                               │
│ trial_6d110    RUNNING                 431             5                    40                   15   0.3                        6650                                               │
│ trial_6d110    RUNNING                 365             6                    27                   21   0.3                         806                                               │
│ trial_6d110    RUNNING                 498             6                    20                   25   0.3                        6990                                               │
│ trial_6d110    RUNNING                 418             5                    38                   18   0.3                        5968                                               │
│ trial_6d110    RUNNING                 425             6                    37                   12   0.3                        3847                                               │
│ trial_6d110    RUNNING                 403             6                    20                   22   0.3                          25                                               │
│ trial_6d110    TERMINATED              291             5                    39                   17   sqrt                       5213        1            41.7534          0.568144 │
│ trial_6d110    TERMINATED              262             6                    48                   13   0.3                        9253        1            49.1955          0.587375 │
│ trial_6d110    TERMINATED              304             6                    29                   16   sqrt                       1508        1            39.2529          0.5949   │
│ trial_6d110    TERMINATED              386             5                    40                   14   sqrt                       5394        1            50.37            0.564381 │
│ trial_6d110    TERMINATED              371             7                    33                   27   sqrt                       4330        1            51.142           0.606187 │
│ trial_6d110    TERMINATED              233             6                    43                   20   sqrt                       3961        1            28.6242          0.589465 │
│ trial_6d110    TERMINATED              246             7                    20                   20   0.3                        7867        1            53.8191          0.591555 │
│ trial_6d110    TERMINATED              303             7                    24                   19   sqrt                       8779        1            41.9711          0.603261 │
│ trial_6d110    TERMINATED              461             5                    27                   10   sqrt                       5549        1            50.5957          0.574415 │
│ trial_6d110    TERMINATED              260             7                    46                   16   0.3                        1306        1            55.9321          0.583612 │
│ trial_6d110    TERMINATED              412             6                    30                   14   sqrt                       2871        1            46.3019          0.597826 │
│ trial_6d110    TERMINATED              261             5                    50                   10   sqrt                        229        1            35.2212          0.567308 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:03:13. Total running time: 1min 1s
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             58.4651 │
│ time_total_s                 58.4651 │
│ training_iteration                 1 │
│ test_accuracy                0.58905 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:03:13. Total running time: 1min 1s

[36m(train_brf_ray_tune pid=1285116)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 11x across cluster][0m
[36m(train_brf_ray_tune pid=1285163)[0m [Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.1s[32m [repeated 20x across cluster][0m
2025-11-06 13:03:16,991	INFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_PI/case_PI_BRF_acc_17_classes/BalancedRF_hyperparameters_tuning' in 0.0093s.
Trial trial_6d110 finished iteration 1 at 2025-11-06 13:03:13. Total running time: 1min 2s
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             59.0634 │
│ time_total_s                 59.0634 │
│ training_iteration                 1 │
│ test_accuracy                 0.5857 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:03:13. Total running time: 1min 2s

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:03:13. Total running time: 1min 2s
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             59.2874 │
│ time_total_s                 59.2874 │
│ training_iteration                 1 │
│ test_accuracy                0.58361 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:03:13. Total running time: 1min 2s

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:03:14. Total running time: 1min 2s
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              59.308 │
│ time_total_s                  59.308 │
│ training_iteration                 1 │
│ test_accuracy                0.57065 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:03:14. Total running time: 1min 2s

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:03:14. Total running time: 1min 3s
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             60.2095 │
│ time_total_s                 60.2095 │
│ training_iteration                 1 │
│ test_accuracy                0.56689 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:03:14. Total running time: 1min 3s

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:03:16. Total running time: 1min 4s
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              61.607 │
│ time_total_s                  61.607 │
│ training_iteration                 1 │
│ test_accuracy                0.58445 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:03:16. Total running time: 1min 4s

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:03:16. Total running time: 1min 5s
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             62.5011 │
│ time_total_s                 62.5011 │
│ training_iteration                 1 │
│ test_accuracy                0.59406 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:03:16. Total running time: 1min 5s

Trial trial_6d110 finished iteration 1 at 2025-11-06 13:03:16. Total running time: 1min 5s
╭──────────────────────────────────────╮
│ Trial trial_6d110 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             62.4656 │
│ time_total_s                 62.4656 │
│ training_iteration                 1 │
│ test_accuracy                0.58194 │
╰──────────────────────────────────────╯

Trial trial_6d110 completed after 1 iterations at 2025-11-06 13:03:16. Total running time: 1min 5s

Trial status: 20 TERMINATED
Current time: 2025-11-06 13:03:16. Total running time: 1min 5s
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:    1.2s
[Parallel(n_jobs=-1)]: Done 371 out of 371 | elapsed:    2.8s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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_6d110    TERMINATED              291             5                    39                   17   sqrt                       5213        1            41.7534          0.568144 │
│ trial_6d110    TERMINATED              262             6                    48                   13   0.3                        9253        1            49.1955          0.587375 │
│ trial_6d110    TERMINATED              475             7                    20                   16   0.3                        3916        1            62.5011          0.594064 │
│ trial_6d110    TERMINATED              304             6                    29                   16   sqrt                       1508        1            39.2529          0.5949   │
│ trial_6d110    TERMINATED              386             5                    40                   14   sqrt                       5394        1            50.37            0.564381 │
│ trial_6d110    TERMINATED              392             6                    53                   27   0.3                        9960        1            59.2874          0.583612 │
│ trial_6d110    TERMINATED              431             5                    40                   15   0.3                        6650        1            59.308           0.570652 │
│ trial_6d110    TERMINATED              371             7                    33                   27   sqrt                       4330        1            51.142           0.606187 │
│ trial_6d110    TERMINATED              233             6                    43                   20   sqrt                       3961        1            28.6242          0.589465 │
│ trial_6d110    TERMINATED              365             6                    27                   21   0.3                         806        1            59.0634          0.585702 │
│ trial_6d110    TERMINATED              498             6                    20                   25   0.3                        6990        1            62.4656          0.58194  │
│ trial_6d110    TERMINATED              246             7                    20                   20   0.3                        7867        1            53.8191          0.591555 │
│ trial_6d110    TERMINATED              418             5                    38                   18   0.3                        5968        1            60.2095          0.56689  │
│ trial_6d110    TERMINATED              303             7                    24                   19   sqrt                       8779        1            41.9711          0.603261 │
│ trial_6d110    TERMINATED              461             5                    27                   10   sqrt                       5549        1            50.5957          0.574415 │
│ trial_6d110    TERMINATED              425             6                    37                   12   0.3                        3847        1            61.607           0.584448 │
│ trial_6d110    TERMINATED              260             7                    46                   16   0.3                        1306        1            55.9321          0.583612 │
│ trial_6d110    TERMINATED              403             6                    20                   22   0.3                          25        1            58.4651          0.589047 │
│ trial_6d110    TERMINATED              412             6                    30                   14   sqrt                       2871        1            46.3019          0.597826 │
│ trial_6d110    TERMINATED              261             5                    50                   10   sqrt                        229        1            35.2212          0.567308 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'n_estimators': 371, 'max_depth': 7, 'min_samples_split': 33, 'min_samples_leaf': 27, 'max_features': 'sqrt', 'random_state': 4330}
Saved model to disk
[36m(train_brf_ray_tune pid=1285135)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 8x across cluster][0m
[36m(train_brf_ray_tune pid=1285135)[0m   _log_deprecation_warning([32m [repeated 8x across cluster][0m
[36m(train_brf_ray_tune pid=1285135)[0m [Parallel(n_jobs=20)]: Done 498 out of 498 | elapsed:    0.1s finished[32m [repeated 20x across cluster][0m
[36m(train_brf_ray_tune pid=1285135)[0m [Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.[32m [repeated 7x across cluster][0m
[36m(train_brf_ray_tune pid=1285135)[0m [Parallel(n_jobs=20)]: Done 410 tasks      | elapsed:    0.1s[32m [repeated 22x across cluster][0m
2025-11-06 13:03:35.993564: 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 13:03:36.005415: 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 00:00:1762430616.019532 1291038 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762430616.023885 1291038 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762430616.034247 1291038 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430616.034270 1291038 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430616.034273 1291038 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430616.034274 1291038 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:03:36.037558: 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 371 out of 371 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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 13:04:01.041138: 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 13:04:01.052925: 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 00:00:1762430641.067106 1293021 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762430641.071418 1293021 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762430641.081710 1293021 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430641.081735 1293021 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430641.081738 1293021 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430641.081740 1293021 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:04:01.084953: 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:    1.4s
[Parallel(n_jobs=-1)]: Done 371 out of 371 | elapsed:    2.9s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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 13:04:26.702651: 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 13:04:26.714211: 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 00:00:1762430666.728357 1294473 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762430666.732772 1294473 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762430666.743300 1294473 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430666.743321 1294473 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430666.743324 1294473 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430666.743326 1294473 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:04:26.746478: 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 371 out of 371 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.41 [%]
Global accuracy score (test) = 59.95 [%]
Global F1 score (train) = 64.04 [%]
Global F1 score (test) = 58.66 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.16      0.20       161
 CAMINAR CON MÓVIL O LIBRO       0.37      0.50      0.42       161
       CAMINAR USUAL SPEED       0.35      0.32      0.33       161
            CAMINAR ZIGZAG       0.42      0.29      0.35       161
          DE PIE BARRIENDO       0.61      0.84      0.70       161
   DE PIE DOBLANDO TOALLAS       0.49      0.43      0.46       161
    DE PIE MOVIENDO LIBROS       0.53      0.46      0.49       161
          DE PIE USANDO PC       0.92      0.88      0.90       161
        FASE REPOSO CON K5       0.71      0.95      0.81       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.54      0.48      0.51       161
         SENTADO USANDO PC       0.57      0.63      0.60       161
      SENTADO VIENDO LA TV       0.54      0.33      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.94      0.86      0.89       138

                  accuracy                           0.60      2392
                 macro avg       0.59      0.60      0.59      2392
              weighted avg       0.59      0.60      0.58      2392


Accuracy capturado en la ejecución 1: 59.95 [%]
F1-score capturado en la ejecución 1: 58.66 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.69 [%]
Global accuracy score (test) = 60.62 [%]
Global F1 score (train) = 64.31 [%]
Global F1 score (test) = 59.45 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.30      0.19      0.23       161
 CAMINAR CON MÓVIL O LIBRO       0.38      0.47      0.42       161
       CAMINAR USUAL SPEED       0.35      0.32      0.33       161
            CAMINAR ZIGZAG       0.44      0.32      0.37       161
          DE PIE BARRIENDO       0.61      0.84      0.70       161
   DE PIE DOBLANDO TOALLAS       0.51      0.46      0.48       161
    DE PIE MOVIENDO LIBROS       0.53      0.45      0.48       161
          DE PIE USANDO PC       0.92      0.88      0.90       161
        FASE REPOSO CON K5       0.71      0.95      0.81       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.56      0.50      0.52       161
         SENTADO USANDO PC       0.57      0.65      0.60       161
      SENTADO VIENDO LA TV       0.57      0.34      0.42       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.94      0.86      0.89       138

                  accuracy                           0.61      2392
                 macro avg       0.60      0.61      0.59      2392
              weighted avg       0.60      0.61      0.59      2392


Accuracy capturado en la ejecución 2: 60.62 [%]
F1-score capturado en la ejecución 2: 59.45 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.11 [%]
Global accuracy score (test) = 59.7 [%]
Global F1 score (train) = 63.73 [%]
Global F1 score (test) = 58.52 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.28      0.17      0.21       161
 CAMINAR CON MÓVIL O LIBRO       0.38      0.48      0.43       161
       CAMINAR USUAL SPEED       0.34      0.29      0.32       161
            CAMINAR ZIGZAG       0.45      0.35      0.39       161
          DE PIE BARRIENDO       0.59      0.81      0.68       161
   DE PIE DOBLANDO TOALLAS       0.48      0.45      0.46       161
    DE PIE MOVIENDO LIBROS       0.53      0.45      0.48       161
          DE PIE USANDO PC       0.92      0.88      0.90       161
        FASE REPOSO CON K5       0.71      0.95      0.81       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.52      0.46      0.49       161
         SENTADO USANDO PC       0.55      0.61      0.58       161
      SENTADO VIENDO LA TV       0.52      0.33      0.40       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.94      0.86      0.89       138

                  accuracy                           0.60      2392
                 macro avg       0.59      0.60      0.59      2392
              weighted avg       0.58      0.60      0.58      2392


Accuracy capturado en la ejecución 3: 59.7 [%]
F1-score capturado en la ejecución 3: 58.52 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
2025-11-06 13:04:52.198694: 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 13:04:52.210386: 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 00:00:1762430692.224701 1296535 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762430692.228846 1296535 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762430692.239252 1296535 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430692.239293 1296535 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430692.239295 1296535 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430692.239297 1296535 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:04:52.242530: 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 371 out of 371 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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 13:05:17.384840: 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 13:05:17.396424: 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 00:00:1762430717.410301 1298166 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762430717.414602 1298166 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762430717.424872 1298166 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430717.424894 1298166 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430717.424897 1298166 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430717.424899 1298166 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:05:17.428110: 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 371 out of 371 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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 13:05:43.030482: 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 13:05:43.042252: 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 00:00:1762430743.055649 1300029 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762430743.059900 1300029 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762430743.070034 1300029 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430743.070055 1300029 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430743.070058 1300029 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430743.070060 1300029 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:05:43.073347: 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 371 out of 371 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.26 [%]
Global accuracy score (test) = 60.03 [%]
Global F1 score (train) = 63.89 [%]
Global F1 score (test) = 58.88 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.28      0.20      0.23       161
 CAMINAR CON MÓVIL O LIBRO       0.36      0.50      0.42       161
       CAMINAR USUAL SPEED       0.38      0.29      0.33       161
            CAMINAR ZIGZAG       0.41      0.29      0.34       161
          DE PIE BARRIENDO       0.61      0.83      0.70       161
   DE PIE DOBLANDO TOALLAS       0.52      0.47      0.49       161
    DE PIE MOVIENDO LIBROS       0.54      0.48      0.51       161
          DE PIE USANDO PC       0.92      0.88      0.90       161
        FASE REPOSO CON K5       0.71      0.95      0.81       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.51      0.43      0.47       161
         SENTADO USANDO PC       0.56      0.63      0.59       161
      SENTADO VIENDO LA TV       0.51      0.33      0.40       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.96      0.86      0.90       138

                  accuracy                           0.60      2392
                 macro avg       0.59      0.60      0.59      2392
              weighted avg       0.59      0.60      0.59      2392


Accuracy capturado en la ejecución 4: 60.03 [%]
F1-score capturado en la ejecución 4: 58.88 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.28 [%]
Global accuracy score (test) = 60.24 [%]
Global F1 score (train) = 63.94 [%]
Global F1 score (test) = 59.11 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.30      0.18      0.23       161
 CAMINAR CON MÓVIL O LIBRO       0.36      0.47      0.40       161
       CAMINAR USUAL SPEED       0.40      0.39      0.40       161
            CAMINAR ZIGZAG       0.40      0.27      0.32       161
          DE PIE BARRIENDO       0.60      0.81      0.69       161
   DE PIE DOBLANDO TOALLAS       0.49      0.47      0.48       161
    DE PIE MOVIENDO LIBROS       0.53      0.44      0.48       161
          DE PIE USANDO PC       0.93      0.88      0.90       161
        FASE REPOSO CON K5       0.71      0.96      0.82       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.54      0.47      0.50       161
         SENTADO USANDO PC       0.56      0.63      0.59       161
      SENTADO VIENDO LA TV       0.54      0.34      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.97      0.86      0.91       138

                  accuracy                           0.60      2392
                 macro avg       0.60      0.60      0.59      2392
              weighted avg       0.59      0.60      0.59      2392


Accuracy capturado en la ejecución 5: 60.24 [%]
F1-score capturado en la ejecución 5: 59.11 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.42 [%]
Global accuracy score (test) = 59.82 [%]
Global F1 score (train) = 64.07 [%]
Global F1 score (test) = 58.71 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.29      0.16      0.21       161
 CAMINAR CON MÓVIL O LIBRO       0.34      0.42      0.37       161
       CAMINAR USUAL SPEED       0.34      0.34      0.34       161
            CAMINAR ZIGZAG       0.42      0.31      0.36       161
          DE PIE BARRIENDO       0.61      0.84      0.71       161
   DE PIE DOBLANDO TOALLAS       0.52      0.47      0.49       161
    DE PIE MOVIENDO LIBROS       0.53      0.46      0.49       161
          DE PIE USANDO PC       0.94      0.87      0.90       161
        FASE REPOSO CON K5       0.72      0.97      0.82       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.53      0.47      0.50       161
         SENTADO USANDO PC       0.54      0.60      0.57       161
      SENTADO VIENDO LA TV       0.53      0.34      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.97      0.86      0.91       138

                  accuracy                           0.60      2392
                 macro avg       0.59      0.60      0.59      2392
              weighted avg       0.59      0.60      0.58      2392


Accuracy capturado en la ejecución 6: 59.82 [%]
F1-score capturado en la ejecución 6: 58.71 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
2025-11-06 13:06:08.140006: 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 13:06:08.151658: 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 00:00:1762430768.165754 1301891 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762430768.170132 1301891 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762430768.180397 1301891 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430768.180420 1301891 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430768.180423 1301891 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430768.180424 1301891 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:06:08.183660: 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 371 out of 371 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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 13:06:33.420207: 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 13:06:33.431241: 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 00:00:1762430793.444674 1303371 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762430793.448740 1303371 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762430793.458753 1303371 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430793.458774 1303371 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430793.458778 1303371 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430793.458787 1303371 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:06:33.461950: 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 371 out of 371 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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 13:06:58.913178: 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 13:06:58.925005: 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 00:00:1762430818.938928 1305502 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762430818.942969 1305502 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762430818.953370 1305502 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430818.953397 1305502 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430818.953399 1305502 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430818.953402 1305502 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:06:58.956611: 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 371 out of 371 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.65 [%]
Global accuracy score (test) = 59.62 [%]
Global F1 score (train) = 64.28 [%]
Global F1 score (test) = 58.37 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.31      0.19      0.23       161
 CAMINAR CON MÓVIL O LIBRO       0.35      0.50      0.41       161
       CAMINAR USUAL SPEED       0.35      0.31      0.33       161
            CAMINAR ZIGZAG       0.37      0.23      0.28       161
          DE PIE BARRIENDO       0.59      0.81      0.68       161
   DE PIE DOBLANDO TOALLAS       0.50      0.47      0.49       161
    DE PIE MOVIENDO LIBROS       0.55      0.45      0.50       161
          DE PIE USANDO PC       0.93      0.88      0.90       161
        FASE REPOSO CON K5       0.71      0.96      0.82       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.53      0.47      0.50       161
         SENTADO USANDO PC       0.55      0.61      0.58       161
      SENTADO VIENDO LA TV       0.52      0.33      0.40       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.95      0.86      0.90       138

                  accuracy                           0.60      2392
                 macro avg       0.59      0.60      0.58      2392
              weighted avg       0.59      0.60      0.58      2392


Accuracy capturado en la ejecución 7: 59.62 [%]
F1-score capturado en la ejecución 7: 58.37 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.05 [%]
Global accuracy score (test) = 59.99 [%]
Global F1 score (train) = 63.73 [%]
Global F1 score (test) = 58.78 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.16      0.20       161
 CAMINAR CON MÓVIL O LIBRO       0.37      0.47      0.42       161
       CAMINAR USUAL SPEED       0.36      0.35      0.35       161
            CAMINAR ZIGZAG       0.41      0.28      0.33       161
          DE PIE BARRIENDO       0.61      0.82      0.70       161
   DE PIE DOBLANDO TOALLAS       0.50      0.47      0.48       161
    DE PIE MOVIENDO LIBROS       0.53      0.45      0.49       161
          DE PIE USANDO PC       0.92      0.88      0.90       161
        FASE REPOSO CON K5       0.71      0.95      0.81       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.55      0.47      0.51       161
         SENTADO USANDO PC       0.56      0.65      0.60       161
      SENTADO VIENDO LA TV       0.53      0.32      0.40       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.96      0.86      0.90       138

                  accuracy                           0.60      2392
                 macro avg       0.59      0.60      0.59      2392
              weighted avg       0.59      0.60      0.58      2392


Accuracy capturado en la ejecución 8: 59.99 [%]
F1-score capturado en la ejecución 8: 58.78 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.39 [%]
Global accuracy score (test) = 59.49 [%]
Global F1 score (train) = 64.02 [%]
Global F1 score (test) = 58.05 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.10      0.13       161
 CAMINAR CON MÓVIL O LIBRO       0.36      0.49      0.42       161
       CAMINAR USUAL SPEED       0.36      0.37      0.36       161
            CAMINAR ZIGZAG       0.38      0.24      0.30       161
          DE PIE BARRIENDO       0.62      0.83      0.71       161
   DE PIE DOBLANDO TOALLAS       0.50      0.45      0.48       161
    DE PIE MOVIENDO LIBROS       0.51      0.46      0.48       161
          DE PIE USANDO PC       0.92      0.86      0.89       161
        FASE REPOSO CON K5       0.71      0.95      0.81       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.54      0.48      0.51       161
         SENTADO USANDO PC       0.56      0.63      0.59       161
      SENTADO VIENDO LA TV       0.55      0.33      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.94      0.86      0.89       138

                  accuracy                           0.59      2392
                 macro avg       0.58      0.60      0.58      2392
              weighted avg       0.58      0.59      0.58      2392


Accuracy capturado en la ejecución 9: 59.49 [%]
F1-score capturado en la ejecución 9: 58.05 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
2025-11-06 13:07:24.148950: 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 13:07:24.160695: 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 00:00:1762430844.174647 1306967 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762430844.178957 1306967 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762430844.189250 1306967 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430844.189274 1306967 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430844.189277 1306967 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430844.189278 1306967 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:07:24.192537: 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 371 out of 371 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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 13:07:49.577080: 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 13:07:49.588596: 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 00:00:1762430869.602586 1308955 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762430869.606923 1308955 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762430869.617067 1308955 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430869.617094 1308955 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430869.617104 1308955 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430869.617106 1308955 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:07:49.620335: 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 371 out of 371 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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 13:08:14.801868: 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 13:08:14.813661: 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 00:00:1762430894.828395 1310756 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762430894.832818 1310756 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762430894.843842 1310756 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430894.843887 1310756 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430894.843890 1310756 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430894.843893 1310756 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:08:14.847281: 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 371 out of 371 | elapsed:    2.7s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.36 [%]
Global accuracy score (test) = 60.2 [%]
Global F1 score (train) = 63.95 [%]
Global F1 score (test) = 59.13 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.32      0.20      0.25       161
 CAMINAR CON MÓVIL O LIBRO       0.37      0.43      0.40       161
       CAMINAR USUAL SPEED       0.36      0.35      0.36       161
            CAMINAR ZIGZAG       0.42      0.32      0.36       161
          DE PIE BARRIENDO       0.59      0.83      0.69       161
   DE PIE DOBLANDO TOALLAS       0.52      0.47      0.50       161
    DE PIE MOVIENDO LIBROS       0.55      0.47      0.51       161
          DE PIE USANDO PC       0.92      0.85      0.88       161
        FASE REPOSO CON K5       0.71      0.95      0.81       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.53      0.48      0.51       161
         SENTADO USANDO PC       0.54      0.62      0.58       161
      SENTADO VIENDO LA TV       0.54      0.31      0.40       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.95      0.86      0.90       138

                  accuracy                           0.60      2392
                 macro avg       0.60      0.60      0.59      2392
              weighted avg       0.59      0.60      0.59      2392


Accuracy capturado en la ejecución 10: 60.2 [%]
F1-score capturado en la ejecución 10: 59.13 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.51 [%]
Global accuracy score (test) = 60.37 [%]
Global F1 score (train) = 64.2 [%]
Global F1 score (test) = 59.12 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.14      0.18       161
 CAMINAR CON MÓVIL O LIBRO       0.37      0.48      0.42       161
       CAMINAR USUAL SPEED       0.38      0.35      0.36       161
            CAMINAR ZIGZAG       0.44      0.35      0.39       161
          DE PIE BARRIENDO       0.63      0.81      0.71       161
   DE PIE DOBLANDO TOALLAS       0.51      0.51      0.51       161
    DE PIE MOVIENDO LIBROS       0.51      0.43      0.47       161
          DE PIE USANDO PC       0.93      0.88      0.90       161
        FASE REPOSO CON K5       0.71      0.96      0.82       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.53      0.44      0.48       161
         SENTADO USANDO PC       0.55      0.65      0.59       161
      SENTADO VIENDO LA TV       0.53      0.32      0.40       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.95      0.86      0.90       138

                  accuracy                           0.60      2392
                 macro avg       0.59      0.61      0.59      2392
              weighted avg       0.59      0.60      0.59      2392


Accuracy capturado en la ejecución 11: 60.37 [%]
F1-score capturado en la ejecución 11: 59.12 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.69 [%]
Global accuracy score (test) = 60.08 [%]
Global F1 score (train) = 64.33 [%]
Global F1 score (test) = 58.82 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.34      0.19      0.25       161
 CAMINAR CON MÓVIL O LIBRO       0.37      0.50      0.42       161
       CAMINAR USUAL SPEED       0.36      0.35      0.36       161
            CAMINAR ZIGZAG       0.39      0.25      0.31       161
          DE PIE BARRIENDO       0.61      0.83      0.70       161
   DE PIE DOBLANDO TOALLAS       0.51      0.49      0.50       161
    DE PIE MOVIENDO LIBROS       0.53      0.43      0.48       161
          DE PIE USANDO PC       0.93      0.87      0.90       161
        FASE REPOSO CON K5       0.71      0.96      0.82       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.53      0.49      0.51       161
         SENTADO USANDO PC       0.54      0.61      0.58       161
      SENTADO VIENDO LA TV       0.51      0.29      0.37       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.96      0.86      0.90       138

                  accuracy                           0.60      2392
                 macro avg       0.59      0.60      0.59      2392
              weighted avg       0.59      0.60      0.59      2392


Accuracy capturado en la ejecución 12: 60.08 [%]
F1-score capturado en la ejecución 12: 58.82 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
2025-11-06 13:08:39.945722: 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 13:08:39.957092: 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 00:00:1762430919.970223 1312234 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762430919.974454 1312234 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762430919.984236 1312234 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430919.984254 1312234 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430919.984256 1312234 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430919.984258 1312234 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:08:39.987466: 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 371 out of 371 | elapsed:    2.5s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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 13:09:03.931157: 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 13:09:03.942211: 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 00:00:1762430943.955423 1312681 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762430943.959560 1312681 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762430943.969597 1312681 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430943.969615 1312681 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430943.969618 1312681 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430943.969619 1312681 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:09:03.972801: 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 371 out of 371 | elapsed:    2.5s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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 13:09:27.758624: 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 13:09:27.769867: 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 00:00:1762430967.782955 1313168 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762430967.787060 1313168 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762430967.797015 1313168 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430967.797037 1313168 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430967.797040 1313168 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430967.797041 1313168 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:09:27.800046: 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 371 out of 371 | elapsed:    2.6s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.66 [%]
Global accuracy score (test) = 60.83 [%]
Global F1 score (train) = 64.33 [%]
Global F1 score (test) = 59.72 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.33      0.20      0.25       161
 CAMINAR CON MÓVIL O LIBRO       0.38      0.51      0.43       161
       CAMINAR USUAL SPEED       0.40      0.33      0.36       161
            CAMINAR ZIGZAG       0.43      0.31      0.36       161
          DE PIE BARRIENDO       0.60      0.81      0.69       161
   DE PIE DOBLANDO TOALLAS       0.51      0.47      0.49       161
    DE PIE MOVIENDO LIBROS       0.53      0.46      0.49       161
          DE PIE USANDO PC       0.92      0.88      0.90       161
        FASE REPOSO CON K5       0.71      0.94      0.81       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.55      0.49      0.52       161
         SENTADO USANDO PC       0.57      0.65      0.60       161
      SENTADO VIENDO LA TV       0.57      0.34      0.42       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.95      0.86      0.90       138

                  accuracy                           0.61      2392
                 macro avg       0.60      0.61      0.60      2392
              weighted avg       0.60      0.61      0.59      2392


Accuracy capturado en la ejecución 13: 60.83 [%]
F1-score capturado en la ejecución 13: 59.72 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.67 [%]
Global accuracy score (test) = 60.41 [%]
Global F1 score (train) = 64.3 [%]
Global F1 score (test) = 59.23 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.15      0.19       161
 CAMINAR CON MÓVIL O LIBRO       0.36      0.46      0.40       161
       CAMINAR USUAL SPEED       0.35      0.35      0.35       161
            CAMINAR ZIGZAG       0.48      0.34      0.40       161
          DE PIE BARRIENDO       0.61      0.81      0.70       161
   DE PIE DOBLANDO TOALLAS       0.51      0.47      0.49       161
    DE PIE MOVIENDO LIBROS       0.54      0.47      0.50       161
          DE PIE USANDO PC       0.91      0.88      0.89       161
        FASE REPOSO CON K5       0.71      0.95      0.81       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.54      0.50      0.52       161
         SENTADO USANDO PC       0.57      0.63      0.60       161
      SENTADO VIENDO LA TV       0.56      0.33      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.94      0.86      0.89       138

                  accuracy                           0.60      2392
                 macro avg       0.60      0.61      0.59      2392
              weighted avg       0.59      0.60      0.59      2392


Accuracy capturado en la ejecución 14: 60.41 [%]
F1-score capturado en la ejecución 14: 59.23 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.51 [%]
Global accuracy score (test) = 59.74 [%]
Global F1 score (train) = 64.18 [%]
Global F1 score (test) = 58.49 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.26      0.14      0.19       161
 CAMINAR CON MÓVIL O LIBRO       0.36      0.43      0.39       161
       CAMINAR USUAL SPEED       0.39      0.40      0.40       161
            CAMINAR ZIGZAG       0.40      0.29      0.34       161
          DE PIE BARRIENDO       0.60      0.83      0.70       161
   DE PIE DOBLANDO TOALLAS       0.50      0.44      0.47       161
    DE PIE MOVIENDO LIBROS       0.52      0.46      0.49       161
          DE PIE USANDO PC       0.88      0.88      0.88       161
        FASE REPOSO CON K5       0.70      0.90      0.79       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.55      0.48      0.51       161
         SENTADO USANDO PC       0.56      0.63      0.60       161
      SENTADO VIENDO LA TV       0.53      0.34      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.94      0.86      0.89       138

                  accuracy                           0.60      2392
                 macro avg       0.59      0.60      0.58      2392
              weighted avg       0.58      0.60      0.58      2392


Accuracy capturado en la ejecución 15: 59.74 [%]
F1-score capturado en la ejecución 15: 58.49 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
2025-11-06 13:09:51.619777: 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 13:09:51.631010: 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 00:00:1762430991.644108 1313634 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762430991.648084 1313634 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762430991.658034 1313634 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430991.658054 1313634 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430991.658056 1313634 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762430991.658058 1313634 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:09:51.661037: 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 371 out of 371 | elapsed:    2.4s 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 371 out of 371 | 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 13:10:15.592953: 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 13:10:15.604332: 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 00:00:1762431015.617715 1314109 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762431015.621984 1314109 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762431015.631851 1314109 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431015.631869 1314109 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431015.631871 1314109 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431015.631873 1314109 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:10:15.635152: 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 371 out of 371 | elapsed:    2.5s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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 13:10:39.501613: 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 13:10:39.512944: 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 00:00:1762431039.526315 1314556 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762431039.530454 1314556 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762431039.540291 1314556 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431039.540310 1314556 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431039.540313 1314556 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431039.540315 1314556 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:10:39.543484: 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 371 out of 371 | elapsed:    2.5s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.85 [%]
Global accuracy score (test) = 60.74 [%]
Global F1 score (train) = 64.54 [%]
Global F1 score (test) = 59.45 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.29      0.14      0.19       161
 CAMINAR CON MÓVIL O LIBRO       0.37      0.52      0.43       161
       CAMINAR USUAL SPEED       0.37      0.37      0.37       161
            CAMINAR ZIGZAG       0.44      0.28      0.34       161
          DE PIE BARRIENDO       0.61      0.83      0.70       161
   DE PIE DOBLANDO TOALLAS       0.53      0.51      0.52       161
    DE PIE MOVIENDO LIBROS       0.53      0.44      0.48       161
          DE PIE USANDO PC       0.92      0.86      0.89       161
        FASE REPOSO CON K5       0.71      0.95      0.81       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.57      0.52      0.54       161
         SENTADO USANDO PC       0.57      0.63      0.60       161
      SENTADO VIENDO LA TV       0.55      0.33      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.95      0.86      0.90       138

                  accuracy                           0.61      2392
                 macro avg       0.60      0.61      0.59      2392
              weighted avg       0.60      0.61      0.59      2392


Accuracy capturado en la ejecución 16: 60.74 [%]
F1-score capturado en la ejecución 16: 59.45 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.94 [%]
Global accuracy score (test) = 60.16 [%]
Global F1 score (train) = 64.62 [%]
Global F1 score (test) = 58.81 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.24      0.13      0.17       161
 CAMINAR CON MÓVIL O LIBRO       0.37      0.52      0.43       161
       CAMINAR USUAL SPEED       0.39      0.34      0.36       161
            CAMINAR ZIGZAG       0.41      0.28      0.33       161
          DE PIE BARRIENDO       0.61      0.83      0.70       161
   DE PIE DOBLANDO TOALLAS       0.50      0.47      0.48       161
    DE PIE MOVIENDO LIBROS       0.52      0.45      0.48       161
          DE PIE USANDO PC       0.93      0.88      0.90       161
        FASE REPOSO CON K5       0.71      0.96      0.82       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.53      0.48      0.51       161
         SENTADO USANDO PC       0.55      0.63      0.59       161
      SENTADO VIENDO LA TV       0.57      0.32      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.95      0.86      0.90       138

                  accuracy                           0.60      2392
                 macro avg       0.59      0.60      0.59      2392
              weighted avg       0.59      0.60      0.59      2392


Accuracy capturado en la ejecución 17: 60.16 [%]
F1-score capturado en la ejecución 17: 58.81 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.8 [%]
Global accuracy score (test) = 60.66 [%]
Global F1 score (train) = 64.48 [%]
Global F1 score (test) = 59.58 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.31      0.23      0.26       161
 CAMINAR CON MÓVIL O LIBRO       0.37      0.48      0.42       161
       CAMINAR USUAL SPEED       0.40      0.35      0.37       161
            CAMINAR ZIGZAG       0.40      0.25      0.31       161
          DE PIE BARRIENDO       0.61      0.83      0.70       161
   DE PIE DOBLANDO TOALLAS       0.50      0.47      0.48       161
    DE PIE MOVIENDO LIBROS       0.52      0.44      0.48       161
          DE PIE USANDO PC       0.95      0.88      0.91       161
        FASE REPOSO CON K5       0.72      0.98      0.83       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.56      0.47      0.51       161
         SENTADO USANDO PC       0.56      0.65      0.60       161
      SENTADO VIENDO LA TV       0.55      0.35      0.43       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.97      0.86      0.91       138

                  accuracy                           0.61      2392
                 macro avg       0.60      0.61      0.60      2392
              weighted avg       0.60      0.61      0.59      2392


Accuracy capturado en la ejecución 18: 60.66 [%]
F1-score capturado en la ejecución 18: 59.58 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
2025-11-06 13:11:03.393135: 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 13:11:03.404734: 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 00:00:1762431063.418227 1315025 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762431063.422675 1315025 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762431063.432865 1315025 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431063.432885 1315025 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431063.432887 1315025 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431063.432889 1315025 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:11:03.436095: 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 371 out of 371 | elapsed:    2.5s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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 13:11:27.289730: 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 13:11:27.301062: 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 00:00:1762431087.314568 1315489 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762431087.318692 1315489 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762431087.328537 1315489 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431087.328556 1315489 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431087.328558 1315489 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431087.328560 1315489 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:11:27.331711: 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:    1.1s
[Parallel(n_jobs=-1)]: Done 371 out of 371 | elapsed:    2.5s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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 13:11:51.253630: 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 13:11:51.265130: 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 00:00:1762431111.278724 1315935 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762431111.282762 1315935 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762431111.292667 1315935 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431111.292686 1315935 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431111.292688 1315935 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431111.292690 1315935 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:11:51.295871: 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 371 out of 371 | elapsed:    2.5s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.69 [%]
Global accuracy score (test) = 60.62 [%]
Global F1 score (train) = 64.36 [%]
Global F1 score (test) = 59.49 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.32      0.19      0.24       161
 CAMINAR CON MÓVIL O LIBRO       0.37      0.49      0.42       161
       CAMINAR USUAL SPEED       0.38      0.36      0.37       161
            CAMINAR ZIGZAG       0.41      0.27      0.32       161
          DE PIE BARRIENDO       0.60      0.81      0.69       161
   DE PIE DOBLANDO TOALLAS       0.51      0.46      0.49       161
    DE PIE MOVIENDO LIBROS       0.54      0.49      0.52       161
          DE PIE USANDO PC       0.92      0.88      0.90       161
        FASE REPOSO CON K5       0.71      0.95      0.81       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.54      0.50      0.52       161
         SENTADO USANDO PC       0.57      0.63      0.60       161
      SENTADO VIENDO LA TV       0.55      0.33      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.96      0.86      0.90       138

                  accuracy                           0.61      2392
                 macro avg       0.60      0.61      0.59      2392
              weighted avg       0.60      0.61      0.59      2392


Accuracy capturado en la ejecución 19: 60.62 [%]
F1-score capturado en la ejecución 19: 59.49 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 65.01 [%]
Global accuracy score (test) = 59.7 [%]
Global F1 score (train) = 64.7 [%]
Global F1 score (test) = 58.47 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.32      0.19      0.24       161
 CAMINAR CON MÓVIL O LIBRO       0.36      0.47      0.41       161
       CAMINAR USUAL SPEED       0.36      0.30      0.33       161
            CAMINAR ZIGZAG       0.40      0.31      0.35       161
          DE PIE BARRIENDO       0.62      0.84      0.71       161
   DE PIE DOBLANDO TOALLAS       0.52      0.47      0.50       161
    DE PIE MOVIENDO LIBROS       0.53      0.46      0.49       161
          DE PIE USANDO PC       0.93      0.88      0.90       161
        FASE REPOSO CON K5       0.71      0.96      0.82       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.50      0.40      0.45       161
         SENTADO USANDO PC       0.53      0.62      0.57       161
      SENTADO VIENDO LA TV       0.49      0.32      0.39       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.94      0.86      0.89       138

                  accuracy                           0.60      2392
                 macro avg       0.59      0.60      0.58      2392
              weighted avg       0.58      0.60      0.58      2392


Accuracy capturado en la ejecución 20: 59.7 [%]
F1-score capturado en la ejecución 20: 58.47 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.62 [%]
Global accuracy score (test) = 60.49 [%]
Global F1 score (train) = 64.26 [%]
Global F1 score (test) = 59.41 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.29      0.21      0.24       161
 CAMINAR CON MÓVIL O LIBRO       0.37      0.50      0.43       161
       CAMINAR USUAL SPEED       0.37      0.27      0.31       161
            CAMINAR ZIGZAG       0.43      0.30      0.36       161
          DE PIE BARRIENDO       0.62      0.84      0.71       161
   DE PIE DOBLANDO TOALLAS       0.52      0.48      0.50       161
    DE PIE MOVIENDO LIBROS       0.51      0.44      0.47       161
          DE PIE USANDO PC       0.92      0.86      0.89       161
        FASE REPOSO CON K5       0.71      0.95      0.81       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.55      0.52      0.53       161
         SENTADO USANDO PC       0.56      0.61      0.59       161
      SENTADO VIENDO LA TV       0.58      0.34      0.43       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.96      0.86      0.90       138

                  accuracy                           0.60      2392
                 macro avg       0.60      0.61      0.59      2392
              weighted avg       0.60      0.60      0.59      2392


Accuracy capturado en la ejecución 21: 60.49 [%]
F1-score capturado en la ejecución 21: 59.41 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
2025-11-06 13:12:15.300381: 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 13:12:15.311854: 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 00:00:1762431135.325529 1316404 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762431135.329690 1316404 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762431135.340297 1316404 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431135.340320 1316404 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431135.340322 1316404 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431135.340323 1316404 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:12:15.343709: 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 371 out of 371 | elapsed:    2.5s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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 13:12:39.184277: 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 13:12:39.195534: 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 00:00:1762431159.208529 1316871 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762431159.212515 1316871 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762431159.222286 1316871 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431159.222306 1316871 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431159.222308 1316871 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431159.222310 1316871 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:12:39.225327: 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 371 out of 371 | elapsed:    2.5s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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 13:13:03.097990: 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 13:13:03.109784: 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 00:00:1762431183.123126 1317336 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762431183.127262 1317336 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762431183.137209 1317336 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431183.137229 1317336 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431183.137238 1317336 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431183.137240 1317336 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:13:03.140454: 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 371 out of 371 | elapsed:    2.5s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.56 [%]
Global accuracy score (test) = 60.49 [%]
Global F1 score (train) = 64.24 [%]
Global F1 score (test) = 59.34 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.34      0.21      0.26       161
 CAMINAR CON MÓVIL O LIBRO       0.38      0.50      0.43       161
       CAMINAR USUAL SPEED       0.39      0.35      0.37       161
            CAMINAR ZIGZAG       0.40      0.27      0.32       161
          DE PIE BARRIENDO       0.61      0.81      0.70       161
   DE PIE DOBLANDO TOALLAS       0.51      0.47      0.49       161
    DE PIE MOVIENDO LIBROS       0.53      0.47      0.50       161
          DE PIE USANDO PC       0.92      0.88      0.90       161
        FASE REPOSO CON K5       0.71      0.94      0.81       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.53      0.45      0.49       161
         SENTADO USANDO PC       0.57      0.65      0.60       161
      SENTADO VIENDO LA TV       0.53      0.34      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.94      0.86      0.89       138

                  accuracy                           0.60      2392
                 macro avg       0.60      0.61      0.59      2392
              weighted avg       0.59      0.60      0.59      2392


Accuracy capturado en la ejecución 22: 60.49 [%]
F1-score capturado en la ejecución 22: 59.34 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.53 [%]
Global accuracy score (test) = 60.08 [%]
Global F1 score (train) = 64.16 [%]
Global F1 score (test) = 58.81 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.38      0.21      0.27       161
 CAMINAR CON MÓVIL O LIBRO       0.37      0.53      0.44       161
       CAMINAR USUAL SPEED       0.35      0.29      0.31       161
            CAMINAR ZIGZAG       0.42      0.30      0.35       161
          DE PIE BARRIENDO       0.61      0.84      0.70       161
   DE PIE DOBLANDO TOALLAS       0.50      0.45      0.47       161
    DE PIE MOVIENDO LIBROS       0.52      0.44      0.48       161
          DE PIE USANDO PC       0.93      0.88      0.90       161
        FASE REPOSO CON K5       0.71      0.96      0.82       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.50      0.42      0.46       161
         SENTADO USANDO PC       0.56      0.63      0.59       161
      SENTADO VIENDO LA TV       0.51      0.34      0.40       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.94      0.86      0.89       138

                  accuracy                           0.60      2392
                 macro avg       0.59      0.60      0.59      2392
              weighted avg       0.59      0.60      0.59      2392


Accuracy capturado en la ejecución 23: 60.08 [%]
F1-score capturado en la ejecución 23: 58.81 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.82 [%]
Global accuracy score (test) = 59.95 [%]
Global F1 score (train) = 64.5 [%]
Global F1 score (test) = 58.89 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.30      0.20      0.24       161
 CAMINAR CON MÓVIL O LIBRO       0.35      0.47      0.40       161
       CAMINAR USUAL SPEED       0.37      0.30      0.33       161
            CAMINAR ZIGZAG       0.40      0.29      0.34       161
          DE PIE BARRIENDO       0.61      0.81      0.70       161
   DE PIE DOBLANDO TOALLAS       0.50      0.47      0.48       161
    DE PIE MOVIENDO LIBROS       0.51      0.45      0.48       161
          DE PIE USANDO PC       0.92      0.87      0.89       161
        FASE REPOSO CON K5       0.71      0.95      0.81       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.54      0.47      0.50       161
         SENTADO USANDO PC       0.56      0.63      0.59       161
      SENTADO VIENDO LA TV       0.56      0.34      0.42       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.97      0.86      0.91       138

                  accuracy                           0.60      2392
                 macro avg       0.59      0.60      0.59      2392
              weighted avg       0.59      0.60      0.59      2392


Accuracy capturado en la ejecución 24: 59.95 [%]
F1-score capturado en la ejecución 24: 58.89 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
2025-11-06 13:13:27.164569: 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 13:13:27.175926: 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 00:00:1762431207.189116 1317783 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762431207.193342 1317783 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762431207.203432 1317783 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431207.203451 1317783 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431207.203453 1317783 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431207.203455 1317783 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:13:27.206766: 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 371 out of 371 | elapsed:    2.5s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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 13:13:51.124109: 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 13:13:51.135480: 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 00:00:1762431231.148763 1318253 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762431231.153054 1318253 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762431231.163090 1318253 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431231.163112 1318253 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431231.163114 1318253 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431231.163116 1318253 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:13:51.166461: 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 371 out of 371 | elapsed:    2.5s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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 13:14:15.180933: 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 13:14:15.192176: 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 00:00:1762431255.205581 1318724 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762431255.209831 1318724 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762431255.219800 1318724 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431255.219822 1318724 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431255.219824 1318724 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431255.219825 1318724 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:14:15.223079: 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 371 out of 371 | elapsed:    2.5s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.64 [%]
Global accuracy score (test) = 60.2 [%]
Global F1 score (train) = 64.29 [%]
Global F1 score (test) = 59.1 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.28      0.21      0.24       161
 CAMINAR CON MÓVIL O LIBRO       0.35      0.49      0.41       161
       CAMINAR USUAL SPEED       0.44      0.27      0.34       161
            CAMINAR ZIGZAG       0.43      0.32      0.37       161
          DE PIE BARRIENDO       0.62      0.83      0.71       161
   DE PIE DOBLANDO TOALLAS       0.53      0.51      0.52       161
    DE PIE MOVIENDO LIBROS       0.54      0.45      0.49       161
          DE PIE USANDO PC       0.93      0.87      0.90       161
        FASE REPOSO CON K5       0.71      0.96      0.82       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.51      0.42      0.46       161
         SENTADO USANDO PC       0.56      0.62      0.59       161
      SENTADO VIENDO LA TV       0.49      0.33      0.39       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.95      0.86      0.90       138

                  accuracy                           0.60      2392
                 macro avg       0.60      0.60      0.59      2392
              weighted avg       0.59      0.60      0.59      2392


Accuracy capturado en la ejecución 25: 60.2 [%]
F1-score capturado en la ejecución 25: 59.1 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.44 [%]
Global accuracy score (test) = 59.91 [%]
Global F1 score (train) = 64.15 [%]
Global F1 score (test) = 58.73 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.17      0.21       161
 CAMINAR CON MÓVIL O LIBRO       0.35      0.43      0.39       161
       CAMINAR USUAL SPEED       0.33      0.34      0.33       161
            CAMINAR ZIGZAG       0.42      0.27      0.33       161
          DE PIE BARRIENDO       0.62      0.83      0.71       161
   DE PIE DOBLANDO TOALLAS       0.51      0.47      0.49       161
    DE PIE MOVIENDO LIBROS       0.54      0.46      0.49       161
          DE PIE USANDO PC       0.94      0.88      0.91       161
        FASE REPOSO CON K5       0.72      0.97      0.82       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.54      0.47      0.50       161
         SENTADO USANDO PC       0.55      0.63      0.59       161
      SENTADO VIENDO LA TV       0.56      0.34      0.42       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.94      0.86      0.89       138

                  accuracy                           0.60      2392
                 macro avg       0.59      0.60      0.59      2392
              weighted avg       0.59      0.60      0.58      2392


Accuracy capturado en la ejecución 26: 59.91 [%]
F1-score capturado en la ejecución 26: 58.73 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.51 [%]
Global accuracy score (test) = 60.08 [%]
Global F1 score (train) = 64.15 [%]
Global F1 score (test) = 58.86 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.29      0.17      0.22       161
 CAMINAR CON MÓVIL O LIBRO       0.35      0.43      0.39       161
       CAMINAR USUAL SPEED       0.33      0.33      0.33       161
            CAMINAR ZIGZAG       0.38      0.27      0.31       161
          DE PIE BARRIENDO       0.60      0.84      0.70       161
   DE PIE DOBLANDO TOALLAS       0.53      0.43      0.48       161
    DE PIE MOVIENDO LIBROS       0.54      0.50      0.52       161
          DE PIE USANDO PC       0.93      0.88      0.90       161
        FASE REPOSO CON K5       0.71      0.96      0.82       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.55      0.50      0.52       161
         SENTADO USANDO PC       0.56      0.63      0.59       161
      SENTADO VIENDO LA TV       0.58      0.34      0.43       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.94      0.86      0.89       138

                  accuracy                           0.60      2392
                 macro avg       0.59      0.60      0.59      2392
              weighted avg       0.59      0.60      0.59      2392


Accuracy capturado en la ejecución 27: 60.08 [%]
F1-score capturado en la ejecución 27: 58.86 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
2025-11-06 13:14:39.213024: 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 13:14:39.224144: 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 00:00:1762431279.237060 1319206 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762431279.241019 1319206 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762431279.250724 1319206 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431279.250744 1319206 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431279.250746 1319206 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431279.250747 1319206 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:14:39.253753: 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 371 out of 371 | elapsed:    2.5s finished
[Parallel(n_jobs=20)]: Using backend ThreadingBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    0.0s
[Parallel(n_jobs=20)]: Done 371 out of 371 | 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 13:15:03.185472: 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 13:15:03.197137: 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 00:00:1762431303.210685 1319663 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762431303.215095 1319663 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762431303.225427 1319663 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431303.225449 1319663 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431303.225452 1319663 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762431303.225453 1319663 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-06 13:15:03.228824: 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 371 out of 371 | elapsed:    2.4s 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 371 out of 371 | 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.35 [%]
Global accuracy score (test) = 60.2 [%]
Global F1 score (train) = 64.02 [%]
Global F1 score (test) = 59.04 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.29      0.17      0.22       161
 CAMINAR CON MÓVIL O LIBRO       0.36      0.47      0.41       161
       CAMINAR USUAL SPEED       0.35      0.34      0.35       161
            CAMINAR ZIGZAG       0.44      0.27      0.34       161
          DE PIE BARRIENDO       0.61      0.84      0.70       161
   DE PIE DOBLANDO TOALLAS       0.53      0.48      0.50       161
    DE PIE MOVIENDO LIBROS       0.56      0.46      0.50       161
          DE PIE USANDO PC       0.92      0.88      0.90       161
        FASE REPOSO CON K5       0.71      0.94      0.81       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.53      0.46      0.49       161
         SENTADO USANDO PC       0.56      0.63      0.59       161
      SENTADO VIENDO LA TV       0.54      0.34      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.97      0.86      0.91       138

                  accuracy                           0.60      2392
                 macro avg       0.60      0.60      0.59      2392
              weighted avg       0.59      0.60      0.59      2392


Accuracy capturado en la ejecución 28: 60.2 [%]
F1-score capturado en la ejecución 28: 59.04 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.11 [%]
Global accuracy score (test) = 59.91 [%]
Global F1 score (train) = 63.73 [%]
Global F1 score (test) = 58.61 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.26      0.14      0.18       161
 CAMINAR CON MÓVIL O LIBRO       0.35      0.48      0.40       161
       CAMINAR USUAL SPEED       0.34      0.35      0.35       161
            CAMINAR ZIGZAG       0.43      0.26      0.33       161
          DE PIE BARRIENDO       0.60      0.83      0.70       161
   DE PIE DOBLANDO TOALLAS       0.52      0.49      0.50       161
    DE PIE MOVIENDO LIBROS       0.55      0.45      0.50       161
          DE PIE USANDO PC       0.93      0.87      0.90       161
        FASE REPOSO CON K5       0.71      0.96      0.82       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.54      0.47      0.50       161
         SENTADO USANDO PC       0.56      0.63      0.59       161
      SENTADO VIENDO LA TV       0.55      0.34      0.42       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.96      0.75       161
                    TROTAR       0.94      0.86      0.89       138

                  accuracy                           0.60      2392
                 macro avg       0.59      0.60      0.59      2392
              weighted avg       0.59      0.60      0.58      2392


Accuracy capturado en la ejecución 29: 59.91 [%]
F1-score capturado en la ejecución 29: 58.61 [%]

=== 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
(2392, 42)
(22195, 42)
[[5.]
 [5.]
 [5.]
 ...
 [9.]
 [9.]
 [9.]]
(2392, 1)
[ 5.  6.  6. ...  9.  9. 11.]
(2392,)
-------------------------------------------------

Global accuracy score (train) = 64.38 [%]
Global accuracy score (test) = 60.08 [%]
Global F1 score (train) = 64.04 [%]
Global F1 score (test) = 58.9 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.26      0.16      0.19       161
 CAMINAR CON MÓVIL O LIBRO       0.37      0.45      0.41       161
       CAMINAR USUAL SPEED       0.34      0.32      0.33       161
            CAMINAR ZIGZAG       0.45      0.34      0.38       161
          DE PIE BARRIENDO       0.62      0.81      0.70       161
   DE PIE DOBLANDO TOALLAS       0.50      0.48      0.49       161
    DE PIE MOVIENDO LIBROS       0.52      0.45      0.49       161
          DE PIE USANDO PC       0.94      0.88      0.91       161
        FASE REPOSO CON K5       0.72      0.97      0.82       161
INCREMENTAL CICLOERGOMETRO       0.99      0.96      0.97       161
           SENTADO LEYENDO       0.53      0.47      0.50       161
         SENTADO USANDO PC       0.56      0.61      0.58       161
      SENTADO VIENDO LA TV       0.53      0.33      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.97      0.76       161
                    TROTAR       0.94      0.86      0.89       138

                  accuracy                           0.60      2392
                 macro avg       0.59      0.60      0.59      2392
              weighted avg       0.59      0.60      0.59      2392


Accuracy capturado en la ejecución 30: 60.08 [%]
F1-score capturado en la ejecución 30: 58.9 [%]

=== RESUMEN FINAL ===
Accuracies: [59.95, 60.62, 59.7, 60.03, 60.24, 59.82, 59.62, 59.99, 59.49, 60.2, 60.37, 60.08, 60.83, 60.41, 59.74, 60.74, 60.16, 60.66, 60.62, 59.7, 60.49, 60.49, 60.08, 59.95, 60.2, 59.91, 60.08, 60.2, 59.91, 60.08]
F1-scores: [58.66, 59.45, 58.52, 58.88, 59.11, 58.71, 58.37, 58.78, 58.05, 59.13, 59.12, 58.82, 59.72, 59.23, 58.49, 59.45, 58.81, 59.58, 59.49, 58.47, 59.41, 59.34, 58.81, 58.89, 59.1, 58.73, 58.86, 59.04, 58.61, 58.9]
Accuracy mean: 60.1453 | std: 0.3429
F1 mean: 58.9510 | std: 0.3883

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_PI/case_PI_BRF_acc_17_classes/metrics_test.npz
