Update tutorial artifacts for braindecode/plot_data_augmentation_search

README.md

@@ -0,0 +1,10 @@
+# plot_data_augmentation_search
+
+Saved search results for the Braindecode tutorial `examples/advanced_training/plot_data_augmentation_search.py`.
+
+These files are meant to be loaded by the tutorial so the docs can plot the offline augmentation search without rerunning the full GridSearchCV procedure.
+
+## Stored files
+
+- `search_results.csv`: serialized `GridSearchCV.cv_results_`
+- `metadata.json`: summary metrics for the saved search

metadata.json

@@ -0,0 +1,15 @@
+{
+  "best_augmentation": "SmoothTimeMask()",
+  "chance_level": 0.25,
+  "cv_splits": 2,
+  "display_metric_key": "eval_accuracy",
+  "display_metric_name": "accuracy",
+  "display_split_name": "held-out session",
+  "epochs_requested": 20,
+  "eval_accuracy": 0.6145833333333334,
+  "search_candidates": 6,
+  "short_run_epochs": 2,
+  "training_score": 0.7048611111111112,
+  "tutorial": "plot_data_augmentation_search",
+  "validation_score": 0.3993055555555556
+}

search_results.csv

@@ -0,0 +1,7 @@
+mean_fit_time,std_fit_time,mean_score_time,std_score_time,param_iterator_train__transforms,params,split0_test_score,split1_test_score,mean_test_score,std_test_score,rank_test_score,split0_train_score,split1_train_score,mean_train_score,std_train_score
+19.13320744037628,0.08719742298126221,0.3246210813522339,0.0034400224685668945,FTSurrogate(),{'iterator_train__transforms': FTSurrogate()},0.4166666666666667,0.375,0.39583333333333337,0.020833333333333343,2,0.7638888888888888,0.6666666666666666,0.7152777777777777,0.048611111111111105
+19.76908302307129,0.1334381103515625,0.25862085819244385,0.005473017692565918,FTSurrogate(),{'iterator_train__transforms': FTSurrogate()},0.4513888888888889,0.3333333333333333,0.3923611111111111,0.05902777777777779,3,0.6388888888888888,0.5069444444444444,0.5729166666666666,0.06597222222222221
+19.327903866767883,0.15474402904510498,0.36883795261383057,0.10311996936798096,SmoothTimeMask(),{'iterator_train__transforms': SmoothTimeMask()},0.4305555555555556,0.3680555555555556,0.3993055555555556,0.03125,1,0.7430555555555556,0.6666666666666666,0.7048611111111112,0.038194444444444475
+18.767938494682312,0.08156955242156982,0.28813016414642334,0.010505080223083496,SmoothTimeMask(),{'iterator_train__transforms': SmoothTimeMask()},0.3611111111111111,0.3819444444444444,0.3715277777777778,0.010416666666666657,5,0.5902777777777778,0.5763888888888888,0.5833333333333333,0.006944444444444475
+19.036476135253906,0.13442707061767578,0.30655086040496826,0.03131401538848877,ChannelsDropout(),{'iterator_train__transforms': ChannelsDropout()},0.4375,0.3125,0.375,0.0625,4,0.7638888888888888,0.5763888888888888,0.6701388888888888,0.09375
+18.096240043640137,0.7642838954925537,0.31714797019958496,0.014190912246704102,ChannelsDropout(),{'iterator_train__transforms': ChannelsDropout()},0.2847222222222222,0.2708333333333333,0.2777777777777778,0.0069444444444444475,6,0.4027777777777778,0.4375,0.4201388888888889,0.017361111111111105