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      Preprocessingpreprocess(test_set, [Preprocessor('crop', tmin=0, tmax=24)], n_jobs=-1) - -plot bcic iv 4 ecog cropped

      Total running time of the script: (1 minutes 0.056 seconds)

      -

      Estimated memory usage: 845 MB

      +plot bcic iv 4 ecog cropped

      Total running time of the script: (0 minutes 58.587 seconds)

      +

      Estimated memory usage: 677 MB

        epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
       -------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
      -      1            0.2500        1.6077       0.2500            0.2500        3.0178  0.0006  3.0161
      -      2            0.2569        1.3809       0.2569            0.2569        2.1896  0.0005  2.6188
      -      3            0.3021        1.2767       0.2569            0.2569        1.7959  0.0002  2.6951
      -      4            0.3229        1.1763       0.2674            0.2674        1.6416  0.0000  2.6257
      +      1            0.2500        1.6077       0.2500            0.2500        3.0179  0.0006  1.7614
      +      2            0.2569        1.3809       0.2569            0.2569        2.1898  0.0005  1.5510
      +      3            0.3021        1.2767       0.2604            0.2604        1.7960  0.0002  1.5831
      +      4            0.3229        1.1763       0.2674            0.2674        1.6417  0.0000  1.5343
       
       <class 'braindecode.classifier.EEGClassifier'>[initialized](
         module_=============================================================================================================================================
      @@ -841,8 +841,8 @@ 

      Setting the data aug -

      Total running time of the script: (0 minutes 23.931 seconds)

      -

      Estimated memory usage: 396 MB

      +

      Total running time of the script: (0 minutes 16.459 seconds)

      +

      Estimated memory usage: 506 MB

      -
      <braindecode.datasets.base.BaseConcatDataset object at 0x7fb4c38fe3e0>
      +
      <braindecode.datasets.base.BaseConcatDataset object at 0x7faa94c34d90>
       
      @@ -867,31 +867,31 @@

      Training
        epoch    train_acc    train_loss    valid_acc    valid_loss    cp     dur
       -------  -----------  ------------  -----------  ------------  ----  ------
      -      1       0.5234        0.7013       0.6680        0.6320     +  1.7328
      -      2       0.5938        0.7149       0.4880        0.8357        1.2757
      -      3       0.4922        1.0039       0.6440        0.6172     +  1.2785
      -      4       0.5234        0.7031       0.6120        0.5988     +  1.2658
      -      5       0.5391        0.6751       0.5920        0.6208        1.2662
      -      6       0.6719        0.6235       0.5840        0.6256        1.3371
      -      7       0.6641        0.6315       0.6200        0.6109        1.2458
      -      8       0.6562        0.6261       0.6480        0.5934     +  1.2601
      -      9       0.6484        0.6235       0.6720        0.5778     +  1.2768
      -     10       0.6562        0.6161       0.6920        0.5739     +  1.2443
      -     11       0.6172        0.6322       0.7000        0.5788        1.2820
      -     12       0.6172        0.6260       0.6840        0.5776        1.2430
      -     13       0.6328        0.6427       0.6880        0.5794        1.2645
      -     14       0.6562        0.5923       0.6800        0.5763        1.2628
      -     15       0.6484        0.6193       0.6760        0.5727     +  1.2472
      -     16       0.7344        0.5650       0.6760        0.5677     +  1.2822
      -     17       0.7031        0.5943       0.7000        0.5627     +  1.2413
      -     18       0.7578        0.5444       0.6960        0.5535     +  1.2836
      -     19       0.7812        0.5250       0.7120        0.5440     +  1.2707
      -     20       0.6641        0.5657       0.7160        0.5369     +  1.2543
      -     21       0.7422        0.5285       0.7280        0.5309     +  1.2728
      -     22       0.6797        0.5494       0.7200        0.5262     +  1.2488
      -     23       0.6328        0.6264       0.7240        0.5229     +  1.2567
      -     24       0.7031        0.5611       0.7120        0.5210     +  1.2580
      -     25       0.7031        0.5018       0.7280        0.5204     +  1.2485
      +      1       0.5234        0.7013       0.6680        0.6320     +  1.1546
      +      2       0.5938        0.7149       0.4880        0.8358        0.9195
      +      3       0.4922        1.0040       0.6440        0.6172     +  0.8991
      +      4       0.5234        0.7031       0.6120        0.5990     +  0.9274
      +      5       0.5391        0.6751       0.5920        0.6213        0.8894
      +      6       0.6719        0.6227       0.5920        0.6263        0.8817
      +      7       0.6562        0.6309       0.6240        0.6117        0.8883
      +      8       0.6641        0.6272       0.6480        0.5950     +  0.8862
      +      9       0.6328        0.6238       0.6680        0.5797     +  0.9222
      +     10       0.6406        0.6177       0.6800        0.5746     +  0.8751
      +     11       0.6250        0.6323       0.7040        0.5787        0.8887
      +     12       0.6094        0.6281       0.6760        0.5772        0.8835
      +     13       0.6328        0.6422       0.6880        0.5790        0.8700
      +     14       0.6406        0.5920       0.6840        0.5765        0.8871
      +     15       0.6562        0.6170       0.6920        0.5730     +  0.8821
      +     16       0.7578        0.5608       0.6960        0.5676     +  0.8916
      +     17       0.6875        0.5936       0.7120        0.5612     +  0.8869
      +     18       0.7734        0.5472       0.7080        0.5500     +  0.8865
      +     19       0.7656        0.5245       0.7120        0.5400     +  0.8835
      +     20       0.6641        0.5641       0.7160        0.5333     +  0.8742
      +     21       0.7422        0.5307       0.7200        0.5272     +  0.8891
      +     22       0.7109        0.5499       0.7360        0.5211     +  0.8911
      +     23       0.6250        0.6259       0.7400        0.5164     +  0.8893
      +     24       0.7031        0.5712       0.7400        0.5120     +  0.8823
      +     25       0.7109        0.5030       0.7280        0.5120        0.8824
       
      @@ -950,16 +950,16 @@

      Inspecting pretext t print(classification_report(y_true, y_pred))

      -

      The balanced accuracy is much higher than chance-level (i.e., 20% for our @@ -1066,7 +1066,7 @@

      Using the learned re plot relative positioning
      /home/runner/work/braindecode/braindecode/examples/advanced_training/plot_relative_positioning.py:472: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
         colors = cm.get_cmap('viridis', 5)(range(5))
       
      -<matplotlib.legend.Legend object at 0x7fb4c39c6e00>
      +<matplotlib.legend.Legend object at 0x7faa97001660>
       

      We see that there is sleep stage-related structure in the embedding. A @@ -1123,8 +1123,8 @@

      References -

      Total running time of the script: (1 minutes 35.052 seconds)

      -

      Estimated memory usage: 180 MB

      +

      Total running time of the script: (1 minutes 25.650 seconds)

      +

      Estimated memory usage: 152 MB

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