linting scripts

download_data.py

@@ -24,7 +24,8 @@
 
 os.makedirs(PATH_DATA, exist_ok=True)
 
-URL_DATA = 'ftp://ftp.cea.fr/pub/unati/people/educhesnay/data/brain_anatomy_schizophrenia_data/sz_public_202211.zip' # PUBLIC DATASET
+URL_DATA = 'ftp://ftp.cea.fr/pub/unati/people/educhesnay/data/\
+brain_anatomy_schizophrenia_data/sz_public_202211.zip'  # PUBLIC DATASET
 
 
 def fetch_data(urls, dst, verbose=1):

problem.py

@@ -9,13 +9,15 @@
 
 
 N_FOLDS = 5
-problem_title = 'Predict schizophrenia from brain grey matter (classification)'
+problem_title = 'Predict schizophrenia from \
+    brain grey matter (classification)'
 
 _target_column_name = 'diagnosis'
 _prediction_label_names = ['control', 'schizophrenia']
 
 # A type (class) which will be used to create wrapper objects for y_pred
-Predictions = rw.prediction_types.make_multiclass(label_names=_prediction_label_names)
+Predictions = rw.prediction_types.make_multiclass(
+    label_names=_prediction_label_names)
 # An object implementing the workflow
 workflow = rw.workflows.Estimator()
 
@@ -24,10 +26,12 @@
     rw.score_types.Accuracy(name='acc')
 ]
 
+
 def get_cv(X, y):
     cv_train = StratifiedKFold(n_splits=N_FOLDS, shuffle=True, random_state=0)
     return cv_train.split(X, y)
 
+
 def _read_data(path, dataset, datatype=['rois', 'vbm']):
     """ Read data.
 
@@ -65,7 +69,8 @@ def _read_data(path, dataset, datatype=['rois', 'vbm']):
 
     # Read 3d images and mask
     if'vbm' in datatype:
-        imgs_arr_zip = np.load(os.path.join(path, 'data', "%s_vbm.npz" % dataset))
+        imgs_arr_zip = np.load(os.path.join(path, 'data',
+                                            "%s_vbm.npz" % dataset))
         x_img_arr = imgs_arr_zip['imgs_arr'].squeeze()
         mask_arr = imgs_arr_zip['mask_arr']
         x_img_arr = x_img_arr[:, mask_arr]

submissions/starting_kit/estimator.py

@@ -16,6 +16,7 @@
 
 class ROIsFeatureExtractor(BaseEstimator, TransformerMixin):
     """Select only the 284 ROIs features:"""
+
     def fit(self, X, y):
         return self
 
@@ -27,6 +28,9 @@ def get_estimator():
     """Build your estimator here."""
     estimator = make_pipeline(
         ROIsFeatureExtractor(),
-        MLPClassifier(random_state=1, hidden_layer_sizes=(200, 150, 100, 50, 25, )))
+        MLPClassifier(random_state=1,
+                      hidden_layer_sizes=(200, 150, 100, 50, 25, ),
+                      )
+    )
 
     return estimator