Updating to MOABB 1.0.0 (#553)

* fix: switch setup/requirements.txt to pyproject.toml * enh: add entry for PR 553 * fix: add docstring_inheritance dep * fix, changing for underscore because of setup lib * fix adding tool.setuptools * fix updating the test * fix setup tool on python 3.11 * fix updating tutorial * adding support for python 3.11 * flake8 * fixing the tutorial * fixing the tutorial --------- Co-authored-by: Sylvain Chevallier <sylain.chevallier@universite-paris-saclay.fr> Co-authored-by: bruAristimunha <a.bruno@aluno.ufabc.edu.br>
braindecode · Oct 27, 2023 · 7263a4c · 7263a4c
1 parent 91bfb4c
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diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
@@ -13,13 +13,13 @@ jobs:
     steps:
       ## Install Braindecode
       - name: Checking Out Repository
-        uses: actions/checkout@v2
+        uses: actions/checkout@v4
       # Cache MNE Data
       # The cache key here is fixed except for os
       # so if you download a new mne dataset in the code, best to manually increment the key below
       - name: Create/Restore MNE Data Cache
         id: cache-mne_data
-        uses: actions/cache@v2
+        uses: actions/cache@v3
         with:
           path: ~/mne_data
           key: ${{ runner.os }}-v3
@@ -38,7 +38,7 @@ jobs:
       - run: python -c "import braindecode; print(braindecode.__version__)"
 
       - name: Checking Out Repository
-        uses: actions/checkout@v2
+        uses: actions/checkout@v4
       - name: Create Docs
         run: |
           cd docs

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
@@ -15,17 +15,17 @@ jobs:
       fail-fast: false
       matrix:
         os: [ "ubuntu-latest", "macos-latest", "windows-latest" ]
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
     steps:
     ## Install Braindecode
     - name: Checking Out Repository
-      uses: actions/checkout@v2
+      uses: actions/checkout@v4
     # Cache MNE Data
     # The cache key here is fixed except for os
     # so if you download a new mne dataset in the code, best to manually increment the key below
     - name: Create/Restore MNE Data Cache
       id: cache-mne_data
-      uses: actions/cache@v2
+      uses: actions/cache@v3
       with:
         path: ~/mne_data
         key: ${{ runner.os }}-v3

diff --git a/docs/whats_new.rst b/docs/whats_new.rst
@@ -57,6 +57,7 @@ Enhancements
 - Add basic training example with MNE epochs (:gh:`539` by `Pierre Guetschel`_)
 - Log validation accuracy in :class:`braindecode.EEGClassifier` (:gh:`541` by `Pierre Guetschel`_)
 - Better type hints in :mod:`braindecode.augmentation.base` (:gh:`551` by `Valentin Iovene`_)
+- Support for MOABB 1.0.0 and switch to pyproject.toml (:gh:`553` by `Sylvain Chevallier`_)
 
 Bugs
 ~~~~

diff --git a/examples/advanced_training/plot_data_augmentation.py b/examples/advanced_training/plot_data_augmentation.py
@@ -89,9 +89,8 @@
 #
 
 splitted = windows_dataset.split('session')
-train_set = splitted['session_T']
-valid_set = splitted['session_E']
-
+train_set = splitted['0train']  # Session train
+valid_set = splitted['1test']  # Session evaluation
 ######################################################################
 # Defining a Transform
 # --------------------

diff --git a/examples/advanced_training/plot_data_augmentation_search.py b/examples/advanced_training/plot_data_augmentation_search.py
@@ -123,8 +123,8 @@
 
 
 splitted = windows_dataset.split('session')
-train_set = splitted['session_T']
-eval_set = splitted['session_E']
+train_set = splitted['0train']  # Session train
+eval_set = splitted['1test']  # Session evaluation
 
 ######################################################################
 # Defining a list of transforms

diff --git a/examples/datasets_io/plot_split_dataset.py b/examples/datasets_io/plot_split_dataset.py
@@ -48,7 +48,7 @@
 
 splits = dataset.split("run")
 print(splits)
-splits["run_4"].description
+splits["4"].description
 
 ###############################################################################
 # By row index

diff --git a/examples/model_building/plot_bcic_iv_2a_moabb_cropped.py b/examples/model_building/plot_bcic_iv_2a_moabb_cropped.py
@@ -88,7 +88,7 @@
 from braindecode.datasets import MOABBDataset
 
 subject_id = 3
-dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[subject_id])
+dataset = MOABBDataset(dataset_name="BNCI2014_001", subject_ids=[subject_id])
 
 from numpy import multiply
 
@@ -107,17 +107,19 @@
 factor = 1e6
 
 preprocessors = [
-    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors
+    Preprocessor('pick_types', eeg=True, meg=False, stim=False),
+    # Keep EEG sensors
     Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV
-    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter
-    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization
+    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),
+    # Bandpass filter
+    Preprocessor(exponential_moving_standardize,
+                 # Exponential moving standardization
                  factor_new=factor_new, init_block_size=init_block_size)
 ]
 
 # Transform the data
 preprocess(dataset, preprocessors, n_jobs=-1)
 
-
 ######################################################################
 # Create model and compute windowing parameters
 # ---------------------------------------------
@@ -135,7 +137,6 @@
 
 input_window_samples = 1000
 
-
 ######################################################################
 # Now we create the model. To enable it to be used in cropped decoding
 # efficiently, we manually set the length of the final convolution layer
@@ -181,15 +182,13 @@
 if cuda:
     _ = model.cuda()
 
-
 ######################################################################
 # And now we transform model with strides to a model that outputs dense
 # prediction, so we can use it to obtain predictions for all
 # crops.
 #
 model.to_dense_prediction_model()
 
-
 ######################################################################
 # To know the models’ output shape without the last layer, we calculate the
 # shape of model output for a dummy input.
@@ -227,7 +226,6 @@
     preload=True
 )
 
-
 ######################################################################
 # Split the dataset
 # -----------------
@@ -236,9 +234,8 @@
 #
 
 splitted = windows_dataset.split('session')
-train_set = splitted['session_T']
-valid_set = splitted['session_E']
-
+train_set = splitted['0train']  # Session train
+valid_set = splitted['1test']  # Session evaluation
 
 ######################################################################
 # Training
@@ -285,7 +282,8 @@
     iterator_train__shuffle=True,
     batch_size=batch_size,
     callbacks=[
-        "accuracy", ("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
+        "accuracy",
+        ("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
     ],
     device=device,
     classes=classes,
@@ -294,7 +292,6 @@
 # in the dataset.
 _ = clf.fit(train_set, y=None, epochs=n_epochs)
 
-
 ######################################################################
 # Plot Results
 # ----------------
@@ -309,7 +306,8 @@
 from matplotlib.lines import Line2D
 
 # Extract loss and accuracy values for plotting from history object
-results_columns = ['train_loss', 'valid_loss', 'train_accuracy', 'valid_accuracy']
+results_columns = ['train_loss', 'valid_loss', 'train_accuracy',
+                   'valid_accuracy']
 df = pd.DataFrame(clf.history[:, results_columns], columns=results_columns,
                   index=clf.history[:, 'epoch'])
 
@@ -319,7 +317,8 @@
 
 fig, ax1 = plt.subplots(figsize=(8, 3))
 df.loc[:, ['train_loss', 'valid_loss']].plot(
-    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False, fontsize=14)
+    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False,
+    fontsize=14)
 
 ax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)
 ax1.set_ylabel("Loss", color='tab:blue', fontsize=14)
@@ -335,12 +334,13 @@
 
 # where some data has already been plotted to ax
 handles = []
-handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle='-', label='Train'))
-handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle=':', label='Valid'))
+handles.append(
+    Line2D([0], [0], color='black', linewidth=1, linestyle='-', label='Train'))
+handles.append(
+    Line2D([0], [0], color='black', linewidth=1, linestyle=':', label='Valid'))
 plt.legend(handles, [h.get_label() for h in handles], fontsize=14)
 plt.tight_layout()
 
-
 ######################################################################
 # Plot Confusion Matrix
 # ---------------------

diff --git a/examples/model_building/plot_bcic_iv_2a_moabb_trial.py b/examples/model_building/plot_bcic_iv_2a_moabb_trial.py
@@ -39,7 +39,7 @@
 from braindecode.datasets import MOABBDataset
 
 subject_id = 3
-dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[subject_id])
+dataset = MOABBDataset(dataset_name="BNCI2014_001", subject_ids=[subject_id])
 
 
 ######################################################################
@@ -131,12 +131,12 @@
 ######################################################################
 # We can easily split the dataset using additional info stored in the
 # description attribute, in this case ``session`` column. We select
-# ``session_T`` for training and ``session_E`` for validation.
+# ``T`` for training and ``test`` for validation.
 #
 
 splitted = windows_dataset.split('session')
-train_set = splitted['session_T']
-valid_set = splitted['session_E']
+train_set = splitted['0train']  # Session train
+valid_set = splitted['1test']  # Session evaluation
 
 
 ######################################################################

diff --git a/examples/model_building/plot_how_train_test_and_tune.py b/examples/model_building/plot_how_train_test_and_tune.py
@@ -181,7 +181,7 @@
 ######################################################################
 # We can easily split the dataset BCIC IV 2a dataset using additional
 # info stored in the description attribute, in this case the ``session``
-# column. We select ``session_T`` for training and ``session_E`` for testing.
+# column. We select ``0train`` for training and ``0test`` for testing.
 # For other datasets, you might have to choose another column and/or column.
 #
 # .. note::
@@ -192,8 +192,8 @@
 #
 
 splitted = windows_dataset.split("session")
-train_set = splitted["session_T"]
-test_set = splitted["session_E"]
+train_set = splitted['0train']  # Session train
+test_set = splitted['1test']  # Session evaluation
 
 
 ######################################################################
@@ -412,7 +412,7 @@ def plot_simple_train_test(ax, all_dataset, train_set, test_set):
 def plot_train_valid_test(ax, all_dataset, train_subset, val_subset, test_set):
     """Create a sample plot for training, validation, testing."""
 
-    bd_cmap = ["#3A6190", "#683E00", "#2196F3", "#DDF2FF",]
+    bd_cmap = ["#3A6190", "#683E00", "#2196F3", "#DDF2FF", ]
 
     n_train, n_val, n_test = len(train_subset), len(val_subset), len(test_set)
     ax.barh("Original\ndataset", len(all_dataset), left=0, height=0.5, color=bd_cmap[0])
@@ -508,7 +508,7 @@ def plot_train_valid_test(ax, all_dataset, train_subset, val_subset, test_set):
 def plot_k_fold(ax, cv, all_dataset, X_train, y_train, test_set):
     """Create a sample plot for training, validation, testing."""
 
-    bd_cmap = ["#3A6190", "#683E00", "#2196F3", "#DDF2FF",]
+    bd_cmap = ["#3A6190", "#683E00", "#2196F3", "#DDF2FF", ]
 
     ax.barh("Original\nDataset", len(all_dataset), left=0, height=0.5, color=bd_cmap[0])
 

diff --git a/examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.py b/examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.py
@@ -172,12 +172,12 @@
 ######################################################################
 # We can easily split the dataset using additional info stored in the
 # description attribute, in this case ``session`` column. We select
-# ``session_T`` for training and ``session_E`` for evaluation.
+# ``0train`` for training and ``1test`` for evaluation.
 #
 
 splitted = windows_dataset.split('session')
-train_set = splitted['session_T']
-eval_set = splitted['session_E']
+train_set = splitted['0train']  # Session train
+eval_set = splitted['1test']  # Session evaluation
 
 ######################################################################
 # Create model

diff --git a/examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.py b/examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.py
@@ -73,7 +73,7 @@
 from braindecode.datasets import MOABBDataset
 
 subject_id = 3
-dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[subject_id])
+dataset = MOABBDataset(dataset_name="BNCI2014_001", subject_ids=[subject_id])
 
 ######################################################################
 # Preprocessing, the offline transformation of the raw dataset
@@ -185,7 +185,7 @@
 ######################################################################
 # We can easily split the dataset using additional info stored in the
 # description attribute, in this case the ``session`` column. We
-# select ``session_T`` for training and ``session_E`` for testing.
+# select ``Train`` for training and ``test`` for testing.
 # For other datasets, you might have to choose another column.
 #
 # .. note::
@@ -196,8 +196,8 @@
 #
 
 splitted = windows_dataset.split("session")
-train_set = splitted["session_T"]
-test_set = splitted["session_E"]
+train_set = splitted['0train']  # Session train
+test_set = splitted['1test']  # Session evaluation
 
 ######################################################################
 # Option 1: Pure PyTorch training loop