add scaling as an option

idconn/nbs.py

@@ -233,7 +233,7 @@ def pynbs(matrices, outcome, num_node=None, diagonal=False, alpha=0.05, predict=
 
 
 def kfold_nbs(
-    matrices, outcome, confounds=None, alpha=0.05, groups=None, num_node=None, diagonal=False, n_splits=10, n_iterations=10
+    matrices, outcome, confounds=None, alpha=0.05, groups=None, num_node=None, diagonal=False, scale_x=False, scale_y=False, n_splits=10, n_iterations=10
 ):
     """Calculates the Network Based Statistic (Zalesky et al., 20##) on connectivity matrices provided
     of shape ((subject x session)x node x node)
@@ -333,8 +333,7 @@ def kfold_nbs(
     manager = enlighten.get_manager()
     ticks = manager.counter(total=n_splits * n_iterations, desc="Progress", unit="folds")
     for train_idx, test_idx in cv.split(edges, split_y):
-        x_scaler = Normalizer()
-        y_scaler = Normalizer()
+
         cv_results.at[i, "split"] = (train_idx, test_idx)
 
         # assert len(train_a_idx) == len(train_b_idx)
@@ -382,15 +381,21 @@ def kfold_nbs(
                 test_y, test_edges = residualize(X=test_edges, y=test_y, confounds=test_confounds)
         else:
             pass
+        if scale_x:
+            x_scaler = Normalizer()
+            train_edges = x_scaler.fit_transform(train_edges)
+            test_edges = x_scaler.transform(test_edges)
+        if scale_y:
+            if np.unique(outcome).shape[0] == 2:
+                pass
+            else:
+                y_scaler = Normalizer()
+                train_y = y_scaler.fit_transform(train_y.reshape(-1, 1))
+                test_y = y_scaler.transform(test_y.reshape(-1, 1))
+
+
 
-        train_edges = x_scaler.fit_transform(train_edges)
-        test_edges = x_scaler.transform(test_edges)
-
-        if np.unique(outcome).shape[0] == 2:
-            pass
-        else:
-            train_y = y_scaler.fit_transform(train_y.reshape(-1, 1))
-            test_y = y_scaler.transform(test_y.reshape(-1, 1))
+
 
         # perform NBS wooooooooo
         # note: output is a dataframe :)