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7px; + padding: 5px 0; } ul.search li a { diff --git a/dev/_static/doctools.js b/dev/_static/doctools.js index 4d67807d..0398ebb9 100644 --- a/dev/_static/doctools.js +++ b/dev/_static/doctools.js @@ -1,12 +1,5 @@ /* - * doctools.js - * ~~~~~~~~~~~ - * * Base JavaScript utilities for all Sphinx HTML documentation. - * - * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * */ "use strict"; diff --git a/dev/_static/language_data.js b/dev/_static/language_data.js index 367b8ed8..c7fe6c6f 100644 --- a/dev/_static/language_data.js +++ b/dev/_static/language_data.js @@ -1,13 +1,6 @@ /* - * language_data.js - * ~~~~~~~~~~~~~~~~ - * * This script contains the language-specific data used by searchtools.js, * namely the list of stopwords, stemmer, scorer and splitter. - * - * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * */ var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; diff --git a/dev/_static/searchtools.js b/dev/_static/searchtools.js index b08d58c9..2c774d17 100644 --- a/dev/_static/searchtools.js +++ b/dev/_static/searchtools.js @@ -1,12 +1,5 @@ /* - * searchtools.js - * ~~~~~~~~~~~~~~~~ - * * Sphinx JavaScript utilities for the full-text search. - * - * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * */ "use strict"; @@ -20,7 +13,7 @@ if (typeof Scorer === "undefined") { // and returns the new score. /* score: result => { - const [docname, title, anchor, descr, score, filename] = result + const [docname, title, anchor, descr, score, filename, kind] = result return score }, */ @@ -47,6 +40,14 @@ if (typeof Scorer === "undefined") { }; } +// Global search result kind enum, used by themes to style search results. +class SearchResultKind { + static get index() { return "index"; } + static get object() { return "object"; } + static get text() { return "text"; } + static get title() { return "title"; } +} + const _removeChildren = (element) => { while (element && element.lastChild) element.removeChild(element.lastChild); }; @@ -64,9 +65,13 @@ const _displayItem = (item, searchTerms, highlightTerms) => { const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; const contentRoot = document.documentElement.dataset.content_root; - const [docName, title, anchor, descr, score, _filename] = item; + const [docName, title, anchor, descr, score, _filename, kind] = item; let listItem = document.createElement("li"); + // Add a class representing the item's type: + // can be used by a theme's CSS selector for styling + // See SearchResultKind for the class names. + listItem.classList.add(`kind-${kind}`); let requestUrl; let linkUrl; if (docBuilder === "dirhtml") { @@ -115,8 +120,10 @@ const _finishSearch = (resultCount) => { "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." ); else - Search.status.innerText = _( - "Search finished, found ${resultCount} page(s) matching the search query." + Search.status.innerText = Documentation.ngettext( + "Search finished, found one page matching the search query.", + "Search finished, found ${resultCount} pages matching the search query.", + resultCount, ).replace('${resultCount}', resultCount); }; const _displayNextItem = ( @@ -138,7 +145,7 @@ const _displayNextItem = ( else _finishSearch(resultCount); }; // Helper function used by query() to order search results. -// Each input is an array of [docname, title, anchor, descr, score, filename]. +// Each input is an array of [docname, title, anchor, descr, score, filename, kind]. // Order the results by score (in opposite order of appearance, since the // `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. const _orderResultsByScoreThenName = (a, b) => { @@ -248,6 +255,7 @@ const Search = { searchSummary.classList.add("search-summary"); searchSummary.innerText = ""; const searchList = document.createElement("ul"); + searchList.setAttribute("role", "list"); searchList.classList.add("search"); const out = document.getElementById("search-results"); @@ -318,7 +326,7 @@ const Search = { const indexEntries = Search._index.indexentries; // Collect multiple result groups to be sorted separately and then ordered. - // Each is an array of [docname, title, anchor, descr, score, filename]. + // Each is an array of [docname, title, anchor, descr, score, filename, kind]. const normalResults = []; const nonMainIndexResults = []; @@ -337,6 +345,7 @@ const Search = { null, score + boost, filenames[file], + SearchResultKind.title, ]); } } @@ -354,6 +363,7 @@ const Search = { null, score, filenames[file], + SearchResultKind.index, ]; if (isMain) { normalResults.push(result); @@ -475,6 +485,7 @@ const Search = { descr, score, filenames[match[0]], + SearchResultKind.object, ]); }; Object.keys(objects).forEach((prefix) => @@ -585,6 +596,7 @@ const Search = { null, score, filenames[file], + SearchResultKind.text, ]); } return results; diff --git a/dev/api.html b/dev/api.html index 82d88845..8cc4c534 100644 --- a/dev/api.html +++ b/dev/api.html @@ -42,7 +42,7 @@ - + diff --git a/dev/auto_examples/advanced_training/index.html b/dev/auto_examples/advanced_training/index.html index 04641920..eef0b9e6 100644 --- a/dev/auto_examples/advanced_training/index.html +++ b/dev/auto_examples/advanced_training/index.html @@ -42,7 +42,7 @@ - + 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 f1f8761f..f5dc792f 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 @@ -42,7 +42,7 @@ - + @@ -605,7 +605,7 @@

Preprocessingpreprocess(test_set, [Preprocessor("crop", tmin=0, tmax=24)], n_jobs=-1) -
<braindecode.datasets.base.BaseConcatDataset object at 0x7fc924635060>
+
<braindecode.datasets.base.BaseConcatDataset object at 0x7f9de52995a0>
 

 

 
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  0.5480
-      2     -1.1990     -0.1716        1.5134        2.6475  0.0006  0.4601
-      3     -0.3654     -0.4985        1.2625        3.4645  0.0005  0.4696
-      4     -0.4383     -0.2731        1.2058        2.9438  0.0004  0.4527
-      5     -0.5982     -0.1512        1.1027        2.6529  0.0002  0.4511
-      6     -0.6090     -0.1255        1.1121        2.5886  0.0001  0.4694
-      7     -0.4455     -0.1445        0.9618        2.6339  0.0000  0.4500
-      8     -0.2790     -0.1755        1.0927        2.7063  0.0000  0.4804
+      1    -23.7826     -4.6087        1.8225       11.7419  0.0006  0.5052
+      2     -1.1990     -0.1716        1.5134        2.6475  0.0006  0.4639
+      3     -0.3654     -0.4985        1.2625        3.4645  0.0005  0.4692
+      4     -0.4383     -0.2731        1.2058        2.9438  0.0004  0.4563
+      5     -0.5982     -0.1512        1.1027        2.6529  0.0002  0.4551
+      6     -0.6090     -0.1255        1.1121        2.5886  0.0001  0.4492
+      7     -0.4455     -0.1445        0.9618        2.6339  0.0000  0.4615
+      8     -0.2790     -0.1755        1.0927        2.7063  0.0000  0.4491

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: (2 minutes 17.928 seconds)

-

Estimated memory usage: 1446 MB

+plot bcic iv 4 ecog cropped

Total running time of the script: (1 minutes 25.993 seconds)

+

Estimated memory usage: 1600 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  1.8168
-      2            0.3299        1.3119       0.3194            0.3194        1.3948  0.0005  1.6408
-      3            0.4757        1.1941       0.2986            0.2986        1.3259  0.0002  1.6393
-      4            0.5625        1.1671       0.3333            0.3333        1.3025  0.0000  1.6244
+      1            0.2639        1.4655       0.2639            0.2639        1.5266  0.0006  1.7808
+      2            0.3299        1.3119       0.3194            0.3194        1.3948  0.0005  1.6071
+      3            0.4757        1.1941       0.2986            0.2986        1.3259  0.0002  1.5875
+      4            0.5625        1.1671       0.3333            0.3333        1.3025  0.0000  1.5972
 
 <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 18.648 seconds)

-
Estimated memory usage:  680 MB

+
Total running time of the script: (0 minutes 17.206 seconds)

+
Estimated memory usage:  1328 MB
-
/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:244: UserWarning: Applying preprocessors [<braindecode.preprocessing.preprocess.Preprocessor object at 0x7fc9e5173ac0>] 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 0x7f9de4f855d0>] 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 0x7fc9e5173ac0>] 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 0x7f9de4f855d0>] 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 0x7fc9e5173ac0>] 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 0x7f9de4f855d0>] to the mne.io.Raw of an EEGWindowsDataset.
   warn(
 
-<braindecode.datasets.base.BaseConcatDataset object at 0x7fc9e6a5a500>
+<braindecode.datasets.base.BaseConcatDataset object at 0x7f9de4adb1c0>
 

 

 
@@ -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     +  1.0838
-      2       0.5938        0.7149       0.4880        0.8358        0.8444
-      3       0.4922        1.0040       0.6440        0.6172     +  0.8465
-      4       0.5234        0.7031       0.6120        0.5990     +  0.8492
-      5       0.5391        0.6751       0.5920        0.6213        0.8361
-      6       0.6719        0.6227       0.5920        0.6263        0.8349
-      7       0.6562        0.6309       0.6240        0.6117        0.8214
-      8       0.6641        0.6272       0.6480        0.5950     +  0.8325
-      9       0.6328        0.6238       0.6680        0.5797     +  0.8385
-     10       0.6406        0.6177       0.6800        0.5746     +  0.8335
-     11       0.6250        0.6323       0.7040        0.5787        0.8255
-     12       0.6094        0.6281       0.6760        0.5772        0.8294
-     13       0.6328        0.6422       0.6880        0.5790        0.8333
-     14       0.6406        0.5920       0.6840        0.5765        0.8364
-     15       0.6562        0.6170       0.6920        0.5730     +  0.8340
-     16       0.7578        0.5608       0.6960        0.5676     +  0.8146
-     17       0.6875        0.5936       0.7120        0.5612     +  0.8391
-     18       0.7734        0.5472       0.7080        0.5500     +  0.8368
-     19       0.7656        0.5245       0.7120        0.5400     +  0.8692
-     20       0.6641        0.5641       0.7160        0.5333     +  0.8372
-     21       0.7422        0.5307       0.7200        0.5272     +  0.8377
-     22       0.7109        0.5499       0.7360        0.5211     +  0.8430
-     23       0.6250        0.6259       0.7400        0.5164     +  0.8440
-     24       0.7031        0.5712       0.7400        0.5120     +  0.8529
-     25       0.7109        0.5030       0.7280        0.5120        0.8420
+      1       0.5234        0.7013       0.6680        0.6320     +  0.9723
+      2       0.5938        0.7149       0.4880        0.8358        0.8442
+      3       0.4922        1.0040       0.6440        0.6172     +  0.8541
+      4       0.5234        0.7031       0.6120        0.5990     +  0.8623
+      5       0.5391        0.6751       0.5920        0.6213        0.8614
+      6       0.6719        0.6227       0.5920        0.6263        0.8541
+      7       0.6562        0.6309       0.6240        0.6117        0.8575
+      8       0.6641        0.6272       0.6480        0.5950     +  0.8401
+      9       0.6328        0.6238       0.6680        0.5797     +  0.8538
+     10       0.6406        0.6177       0.6800        0.5746     +  0.8623
+     11       0.6250        0.6323       0.7040        0.5787        0.8557
+     12       0.6094        0.6281       0.6760        0.5772        0.8501
+     13       0.6328        0.6422       0.6880        0.5790        0.8371
+     14       0.6406        0.5920       0.6840        0.5765        0.8376
+     15       0.6562        0.6170       0.6920        0.5730     +  0.8528
+     16       0.7578        0.5608       0.6960        0.5676     +  0.8549
+     17       0.6875        0.5936       0.7120        0.5612     +  0.8510
+     18       0.7734        0.5472       0.7080        0.5500     +  0.8569
+     19       0.7656        0.5245       0.7120        0.5400     +  0.8370
+     20       0.6641        0.5641       0.7160        0.5333     +  0.8474
+     21       0.7422        0.5307       0.7200        0.5272     +  0.8595
+     22       0.7109        0.5499       0.7360        0.5211     +  0.8488
+     23       0.6250        0.6259       0.7400        0.5164     +  0.8480
+     24       0.7031        0.5712       0.7400        0.5120     +  0.8391
+     25       0.7109        0.5030       0.7280        0.5120        0.8608
 /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 0x7fc920a29690>
+plot relative positioning
<matplotlib.legend.Legend object at 0x7f9de4d24af0>
 

 

 
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 48.543 seconds)

-
Estimated memory usage:  781 MB

+
Total running time of the script: (2 minutes 13.137 seconds)

+
Estimated memory usage:  1014 MB