Merge pull request #118 from abess-team/create-pull-request/patch

Fixes by format action

skscope/utilities.py

@@ -31,7 +31,11 @@ def check_y_survival(y_or_event, *args, allow_all_censored=False):
     if len(args) == 0:
         y = y_or_event
 
-        if not isinstance(y, np.ndarray) or y.dtype.fields is None or len(y.dtype.fields) != 2:
+        if (
+            not isinstance(y, np.ndarray)
+            or y.dtype.fields is None
+            or len(y.dtype.fields) != 2
+        ):
             raise ValueError(
                 "y must be a structured array with the first field"
                 " being a binary class event indicator and the second field"
@@ -47,7 +51,9 @@ def check_y_survival(y_or_event, *args, allow_all_censored=False):
 
     event = check_array(y_event, ensure_2d=False)
     if not np.issubdtype(event.dtype, np.bool_):
-        raise ValueError(f"elements of event indicator must be boolean, but found {event.dtype}")
+        raise ValueError(
+            f"elements of event indicator must be boolean, but found {event.dtype}"
+        )
 
     if not (allow_all_censored or np.any(event)):
         raise ValueError("all samples are censored")
@@ -60,7 +66,9 @@ def check_y_survival(y_or_event, *args, allow_all_censored=False):
 
         yt = check_array(yt, ensure_2d=False)
         if not np.issubdtype(yt.dtype, np.number):
-            raise ValueError(f"time must be numeric, but found {yt.dtype} for argument {i + 2}")
+            raise ValueError(
+                f"time must be numeric, but found {yt.dtype} for argument {i + 2}"
+            )
 
         return_val.append(yt)
 
@@ -195,7 +203,9 @@ def logistic_loss(params):
     The GIC is calculated using the formula:
     GIC = 2 * objective_value + effective_params_num * np.log(np.log(train_size)) * np.log(dimensionality)
     """
-    return 2 * objective_value + effective_params_num * np.log(np.log(train_size)) * np.log(dimensionality)
+    return 2 * objective_value + effective_params_num * np.log(
+        np.log(train_size)
+    ) * np.log(dimensionality)
 
 
 def EBIC(
@@ -230,7 +240,9 @@ def logistic_loss(params):
     The E is calculated using the formula:
     EBIC = 2 * objective_value + effective_params_num * (np.log(train_size) + 2 * np.log(dimensionality))
     """
-    return 2 * objective_value + effective_params_num * (np.log(train_size) + 2 * np.log(dimensionality))
+    return 2 * objective_value + effective_params_num * (
+        np.log(train_size) + 2 * np.log(dimensionality)
+    )
 
 
 def LinearSIC(
@@ -267,6 +279,6 @@ def linear_loss(params):
     The LinearSIC is calculated using the formula:
     LinearSIC = train_size * np.log(objective_value) + 2 * effective_params_num * np.log(np.log(train_size)) * np.log(dimensionality)
     """
-    return train_size * np.log(objective_value) + 2 * effective_params_num * np.log(np.log(train_size)) * np.log(
-        dimensionality
-    )
+    return train_size * np.log(objective_value) + 2 * effective_params_num * np.log(
+        np.log(train_size)
+    ) * np.log(dimensionality)