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b/dev/_images/sphx_glr_plot_benchmark_preprocessing_thumb.png index 1bf7adc9..7a6037b9 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 1f05d51b..82927a10 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 0x7f1b519a1450>
+
<braindecode.datasets.base.BaseConcatDataset object at 0x7f6961eb2500>
 

 

 
In time series targets setup, targets variables are stored in mne.Raw object as channels
@@ -842,14 +842,14 @@ 
Training
 
  epoch    r2_train    r2_valid    train_loss    valid_loss      lr     dur
 -------  ----------  ----------  ------------  ------------  ------  ------
-      1    -23.7826     -4.6087        1.8225       11.7419  0.0006  0.5175
-      2     -1.1990     -0.1716        1.5134        2.6475  0.0006  0.4728
-      3     -0.3654     -0.4985        1.2625        3.4645  0.0005  0.4460
-      4     -0.4383     -0.2731        1.2058        2.9438  0.0004  0.4744
-      5     -0.5982     -0.1512        1.1027        2.6529  0.0002  0.4460
-      6     -0.6090     -0.1255        1.1121        2.5886  0.0001  0.4455
-      7     -0.4455     -0.1445        0.9618        2.6339  0.0000  0.4626
-      8     -0.2790     -0.1755        1.0927        2.7063  0.0000  0.4457
+      1    -23.7826     -4.6087        1.8225       11.7419  0.0006  0.5271
+      2     -1.1990     -0.1716        1.5134        2.6475  0.0006  0.4678
+      3     -0.3654     -0.4985        1.2625        3.4645  0.0005  0.4534
+      4     -0.4383     -0.2731        1.2058        2.9438  0.0004  0.4724
+      5     -0.5982     -0.1512        1.1027        2.6529  0.0002  0.4513
+      6     -0.6090     -0.1255        1.1121        2.5886  0.0001  0.4624
+      7     -0.4455     -0.1445        0.9618        2.6339  0.0000  0.4509
+      8     -0.2790     -0.1755        1.0927        2.7063  0.0000  0.4514

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

@@ -970,8 +970,8 @@

Plot Resultsplt.tight_layout()

-plot bcic iv 4 ecog cropped

Total running time of the script: (1 minutes 3.138 seconds)

-

Estimated memory usage: 1466 MB

+plot bcic iv 4 ecog cropped

Total running time of the script: (2 minutes 53.498 seconds)

+

Estimated memory usage: 1381 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  1.7435
-      2            0.3299        1.3119       0.3194            0.3194        1.3948  0.0005  1.6014
-      3            0.4757        1.1941       0.2986            0.2986        1.3259  0.0002  1.6057
-      4            0.5625        1.1671       0.3333            0.3333        1.3025  0.0000  1.6054
+      1            0.2639        1.4655       0.2639            0.2639        1.5266  0.0006  1.7984
+      2            0.3299        1.3119       0.3194            0.3194        1.3948  0.0005  1.6008
+      3            0.4757        1.1941       0.2986            0.2986        1.3259  0.0002  1.6195
+      4            0.5625        1.1671       0.3333            0.3333        1.3025  0.0000  1.6158
 
 <class 'braindecode.classifier.EEGClassifier'>[initialized](
   module_=============================================================================================================================================
@@ -850,8 +850,8 @@ 
Setting the data aug
 
train_set.transform = composed_transforms
 

 

-
Total running time of the script: (0 minutes 17.541 seconds)

-
Estimated memory usage:  967 MB

+
Total running time of the script: (0 minutes 18.402 seconds)

+
Estimated memory usage:  1129 MB
-
/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:244: UserWarning: Applying preprocessors [<braindecode.preprocessing.preprocess.Preprocessor object at 0x7f1c150fbc40>] 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 0x7f694c7e1e70>] 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 0x7f1c150fbc40>] 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 0x7f694c7e1e70>] 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 0x7f1c150fbc40>] 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 0x7f694c7e1e70>] to the mne.io.Raw of an EEGWindowsDataset.
   warn(
 
-<braindecode.datasets.base.BaseConcatDataset object at 0x7f1c150f98a0>
+<braindecode.datasets.base.BaseConcatDataset object at 0x7f694c6d5cf0>
 

 

 
@@ -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     +  1.0788
-      2       0.5938        0.7149       0.4880        0.8358        0.8068
-      3       0.4922        1.0040       0.6440        0.6172     +  0.7990
-      4       0.5234        0.7031       0.6120        0.5990     +  0.8103
-      5       0.5391        0.6751       0.5920        0.6213        0.8144
-      6       0.6719        0.6227       0.5920        0.6263        0.8038
-      7       0.6562        0.6309       0.6240        0.6117        0.7917
-      8       0.6641        0.6272       0.6480        0.5950     +  0.8118
-      9       0.6328        0.6238       0.6680        0.5797     +  0.8104
-     10       0.6406        0.6177       0.6800        0.5746     +  0.8028
-     11       0.6250        0.6323       0.7040        0.5787        0.7864
-     12       0.6094        0.6281       0.6760        0.5772        0.7984
-     13       0.6328        0.6422       0.6880        0.5790        0.8117
-     14       0.6406        0.5920       0.6840        0.5765        0.7915
-     15       0.6562        0.6170       0.6920        0.5730     +  0.7830
-     16       0.7578        0.5608       0.6960        0.5676     +  0.7846
-     17       0.6875        0.5936       0.7120        0.5612     +  0.7944
-     18       0.7734        0.5472       0.7080        0.5500     +  0.7865
-     19       0.7656        0.5245       0.7120        0.5400     +  0.8003
-     20       0.6641        0.5641       0.7160        0.5333     +  0.7922
-     21       0.7422        0.5307       0.7200        0.5272     +  0.7993
-     22       0.7109        0.5499       0.7360        0.5211     +  0.8033
-     23       0.6250        0.6259       0.7400        0.5164     +  0.7959
-     24       0.7031        0.5712       0.7400        0.5120     +  0.7848
-     25       0.7109        0.5030       0.7280        0.5120        0.8068
+      1       0.5234        0.7013       0.6680        0.6320     +  1.1019
+      2       0.5938        0.7149       0.4880        0.8358        0.8375
+      3       0.4922        1.0040       0.6440        0.6172     +  0.8318
+      4       0.5234        0.7031       0.6120        0.5990     +  0.8193
+      5       0.5391        0.6751       0.5920        0.6213        0.8141
+      6       0.6719        0.6227       0.5920        0.6263        0.8127
+      7       0.6562        0.6309       0.6240        0.6117        0.8231
+      8       0.6641        0.6272       0.6480        0.5950     +  0.8536
+      9       0.6328        0.6238       0.6680        0.5797     +  0.8490
+     10       0.6406        0.6177       0.6800        0.5746     +  0.8047
+     11       0.6250        0.6323       0.7040        0.5787        0.8115
+     12       0.6094        0.6281       0.6760        0.5772        0.8142
+     13       0.6328        0.6422       0.6880        0.5790        0.8075
+     14       0.6406        0.5920       0.6840        0.5765        0.8064
+     15       0.6562        0.6170       0.6920        0.5730     +  0.8072
+     16       0.7578        0.5608       0.6960        0.5676     +  0.8270
+     17       0.6875        0.5936       0.7120        0.5612     +  0.8575
+     18       0.7734        0.5472       0.7080        0.5500     +  0.8184
+     19       0.7656        0.5245       0.7120        0.5400     +  0.8047
+     20       0.6641        0.5641       0.7160        0.5333     +  0.8226
+     21       0.7422        0.5307       0.7200        0.5272     +  0.8214
+     22       0.7109        0.5499       0.7360        0.5211     +  0.8287
+     23       0.6250        0.6259       0.7400        0.5164     +  0.8225
+     24       0.7031        0.5712       0.7400        0.5120     +  0.8157
+     25       0.7109        0.5030       0.7280        0.5120        0.8217
 /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 0x7f1c1504bbe0>
+plot relative positioning
<matplotlib.legend.Legend object at 0x7f69620cc6a0>
 

 

 
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 54.736 seconds)

-
Estimated memory usage:  777 MB

+
Total running time of the script: (2 minutes 4.775 seconds)

+
Estimated memory usage:  822 MB