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b/dev/_images/sphx_glr_plot_benchmark_preprocessing_thumb.png index 289e6d31..7777289c 100644 Binary files a/dev/_images/sphx_glr_plot_benchmark_preprocessing_thumb.png and b/dev/_images/sphx_glr_plot_benchmark_preprocessing_thumb.png differ diff --git a/dev/auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html b/dev/auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html index edcd3ddf..065b82ae 100644 --- a/dev/auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html +++ b/dev/auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html @@ -605,7 +605,7 @@

Preprocessingpreprocess(test_set, [Preprocessor("crop", tmin=0, tmax=24)], n_jobs=-1) -
<braindecode.datasets.base.BaseConcatDataset object at 0x7fb2bdc553f0>
+
<braindecode.datasets.base.BaseConcatDataset object at 0x7fc6b5af56c0>
 

 

 
In time series targets setup, targets variables are stored in mne.Raw object as channels
@@ -838,14 +838,14 @@ 
Training
 
  epoch    r2_train    r2_valid    train_loss    valid_loss      lr     dur
 -------  ----------  ----------  ------------  ------------  ------  ------
-      1    -23.7826     -4.6087        1.8225       11.7419  0.0006  0.5451
-      2     -1.1990     -0.1716        1.5134        2.6475  0.0006  0.4571
-      3     -0.3654     -0.4985        1.2625        3.4645  0.0005  0.4480
-      4     -0.4383     -0.2731        1.2058        2.9438  0.0004  0.4596
-      5     -0.5982     -0.1512        1.1027        2.6529  0.0002  0.4500
-      6     -0.6090     -0.1255        1.1121        2.5886  0.0001  0.4628
-      7     -0.4455     -0.1445        0.9618        2.6339  0.0000  0.4471
-      8     -0.2790     -0.1755        1.0927        2.7063  0.0000  0.4606
+      1    -23.7826     -4.6087        1.8225       11.7419  0.0006  0.5330
+      2     -1.1990     -0.1716        1.5134        2.6475  0.0006  0.4530
+      3     -0.3654     -0.4985        1.2625        3.4645  0.0005  0.4640
+      4     -0.4383     -0.2731        1.2058        2.9438  0.0004  0.4465
+      5     -0.5982     -0.1512        1.1027        2.6529  0.0002  0.4629
+      6     -0.6090     -0.1255        1.1121        2.5886  0.0001  0.4475
+      7     -0.4455     -0.1445        0.9618        2.6339  0.0000  0.4510
+      8     -0.2790     -0.1755        1.0927        2.7063  0.0000  0.4463

Obtaining predictions and targets for the test, train, and validation dataset

@@ -966,8 +966,8 @@

Plot Resultsplt.tight_layout()

-plot bcic iv 4 ecog cropped

Total running time of the script: (2 minutes 22.781 seconds)

-

Estimated memory usage: 1513 MB

+plot bcic iv 4 ecog cropped

Total running time of the script: (2 minutes 23.506 seconds)

+

Estimated memory usage: 1294 MB

  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
 -------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
-      1            0.2639        1.4655       0.2639            0.2639        1.5266  0.0006  1.7600
-      2            0.3299        1.3119       0.3194            0.3194        1.3948  0.0005  1.5833
-      3            0.4757        1.1941       0.2986            0.2986        1.3259  0.0002  1.5917
-      4            0.5625        1.1671       0.3333            0.3333        1.3025  0.0000  1.6079
+      1            0.2639        1.4655       0.2639            0.2639        1.5266  0.0006  1.7509
+      2            0.3299        1.3119       0.3194            0.3194        1.3948  0.0005  1.5873
+      3            0.4757        1.1941       0.2986            0.2986        1.3259  0.0002  1.6130
+      4            0.5625        1.1671       0.3333            0.3333        1.3025  0.0000  1.6090
 
 <class 'braindecode.classifier.EEGClassifier'>[initialized](
   module_=============================================================================================================================================
@@ -846,8 +846,8 @@ 
Setting the data aug
 
train_set.transform = composed_transforms
 

 

-
Total running time of the script: (0 minutes 17.849 seconds)

-
Estimated memory usage:  1139 MB

+
Total running time of the script: (0 minutes 17.694 seconds)

+
Estimated memory usage:  995 MB
-
/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:244: UserWarning: Applying preprocessors [<braindecode.preprocessing.preprocess.Preprocessor object at 0x7fb2bddf87c0>] to the mne.io.Raw of an EEGWindowsDataset.
+
/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:244: UserWarning: Applying preprocessors [<braindecode.preprocessing.preprocess.Preprocessor object at 0x7fc6b5713df0>] to the mne.io.Raw of an EEGWindowsDataset.
   warn(
-/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:244: UserWarning: Applying preprocessors [<braindecode.preprocessing.preprocess.Preprocessor object at 0x7fb2bddf87c0>] to the mne.io.Raw of an EEGWindowsDataset.
+/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:244: UserWarning: Applying preprocessors [<braindecode.preprocessing.preprocess.Preprocessor object at 0x7fc6b5713df0>] to the mne.io.Raw of an EEGWindowsDataset.
   warn(
-/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:244: UserWarning: Applying preprocessors [<braindecode.preprocessing.preprocess.Preprocessor object at 0x7fb2bddf87c0>] to the mne.io.Raw of an EEGWindowsDataset.
+/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:244: UserWarning: Applying preprocessors [<braindecode.preprocessing.preprocess.Preprocessor object at 0x7fc6b5713df0>] to the mne.io.Raw of an EEGWindowsDataset.
   warn(
 
-<braindecode.datasets.base.BaseConcatDataset object at 0x7fb2bd7727d0>
+<braindecode.datasets.base.BaseConcatDataset object at 0x7fc77a01f400>
 

 

 
@@ -892,31 +892,31 @@ 
Training
 
  epoch    train_acc    train_loss    valid_acc    valid_loss    cp     dur
 -------  -----------  ------------  -----------  ------------  ----  ------
-      1       0.5234        0.7013       0.6680        0.6320     +  1.0454
-      2       0.5938        0.7149       0.4880        0.8358        0.8264
-      3       0.4922        1.0040       0.6440        0.6172     +  0.8113
-      4       0.5234        0.7031       0.6120        0.5990     +  0.8027
-      5       0.5391        0.6751       0.5920        0.6213        0.8052
-      6       0.6719        0.6227       0.5920        0.6263        0.8149
-      7       0.6562        0.6309       0.6240        0.6117        0.8124
-      8       0.6641        0.6272       0.6480        0.5950     +  0.8031
-      9       0.6328        0.6238       0.6680        0.5797     +  0.8050
-     10       0.6406        0.6177       0.6800        0.5746     +  0.7929
-     11       0.6250        0.6323       0.7040        0.5787        0.8330
-     12       0.6094        0.6281       0.6760        0.5772        0.7975
-     13       0.6328        0.6422       0.6880        0.5790        0.7951
-     14       0.6406        0.5920       0.6840        0.5765        0.7926
-     15       0.6562        0.6170       0.6920        0.5730     +  0.7991
-     16       0.7578        0.5608       0.6960        0.5676     +  0.8024
-     17       0.6875        0.5936       0.7120        0.5612     +  0.8024
-     18       0.7734        0.5472       0.7080        0.5500     +  0.7878
-     19       0.7656        0.5245       0.7120        0.5400     +  0.7977
-     20       0.6641        0.5641       0.7160        0.5333     +  0.8044
-     21       0.7422        0.5307       0.7200        0.5272     +  0.7979
-     22       0.7109        0.5499       0.7360        0.5211     +  0.7950
-     23       0.6250        0.6259       0.7400        0.5164     +  0.7956
-     24       0.7031        0.5712       0.7400        0.5120     +  0.8086
-     25       0.7109        0.5030       0.7280        0.5120        0.8139
+      1       0.5234        0.7013       0.6680        0.6320     +  1.0960
+      2       0.5938        0.7149       0.4880        0.8358        0.8065
+      3       0.4922        1.0040       0.6440        0.6172     +  0.8059
+      4       0.5234        0.7031       0.6120        0.5990     +  0.8140
+      5       0.5391        0.6751       0.5920        0.6213        0.8104
+      6       0.6719        0.6227       0.5920        0.6263        0.8044
+      7       0.6562        0.6309       0.6240        0.6117        0.7978
+      8       0.6641        0.6272       0.6480        0.5950     +  0.7990
+      9       0.6328        0.6238       0.6680        0.5797     +  0.8059
+     10       0.6406        0.6177       0.6800        0.5746     +  0.7922
+     11       0.6250        0.6323       0.7040        0.5787        0.7898
+     12       0.6094        0.6281       0.6760        0.5772        0.7900
+     13       0.6328        0.6422       0.6880        0.5790        0.8247
+     14       0.6406        0.5920       0.6840        0.5765        0.8032
+     15       0.6562        0.6170       0.6920        0.5730     +  0.7959
+     16       0.7578        0.5608       0.6960        0.5676     +  0.7900
+     17       0.6875        0.5936       0.7120        0.5612     +  0.8016
+     18       0.7734        0.5472       0.7080        0.5500     +  0.7867
+     19       0.7656        0.5245       0.7120        0.5400     +  0.7900
+     20       0.6641        0.5641       0.7160        0.5333     +  0.7964
+     21       0.7422        0.5307       0.7200        0.5272     +  0.7945
+     22       0.7109        0.5499       0.7360        0.5211     +  0.8014
+     23       0.6250        0.6259       0.7400        0.5164     +  0.8036
+     24       0.7031        0.5712       0.7400        0.5120     +  0.7995
+     25       0.7109        0.5030       0.7280        0.5120        0.8178
 /home/runner/.local/lib/python3.10/site-packages/skorch/net.py:2626: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
   return torch.load(f_name, map_location=map_location)
 

@@ -1102,7 +1102,7 @@ 
Using the learned re
 ax.legend()
-plot relative positioning
<matplotlib.legend.Legend object at 0x7fb2bd7702e0>
+plot relative positioning
<matplotlib.legend.Legend object at 0x7fc6a299f7f0>
 

 

 
We see that there is sleep stage-related structure in the embedding. A
@@ -1159,8 +1159,8 @@ 
References
 
 
-
Total running time of the script: (1 minutes 54.626 seconds)

-
Estimated memory usage:  814 MB

+
Total running time of the script: (2 minutes 4.919 seconds)

+
Estimated memory usage:  738 MB