diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py
index aea7e421..fb344fa0 100644
--- a/deepcave/evaluators/mo_fanova.py
+++ b/deepcave/evaluators/mo_fanova.py
@@ -92,7 +92,6 @@ def calculate(
             objectives, budget, specific=True, include_combined_cost=True
         )
 
-
         # normalize objectives
         objectives_normed = list()
         for obj in objectives:
diff --git a/deepcave/evaluators/mo_lpi.py b/deepcave/evaluators/mo_lpi.py
index 1decbcec..8166f70b 100644
--- a/deepcave/evaluators/mo_lpi.py
+++ b/deepcave/evaluators/mo_lpi.py
@@ -121,7 +121,6 @@ def calculate(
             objectives=objectives, budget=budget, specific=True, include_combined_cost=True
         )
 
-
         # normalize objectives
         assert isinstance(objectives, list)
         objectives_normed = list()
diff --git a/deepcave/utils/multi_objective_importance.py b/deepcave/utils/multi_objective_importance.py
index e557f82f..eb658392 100644
--- a/deepcave/utils/multi_objective_importance.py
+++ b/deepcave/utils/multi_objective_importance.py
@@ -21,8 +21,8 @@
 
 from typing import List
 
-import pandas as pd
 import numpy as np
+import pandas as pd
 
 
 def is_pareto_efficient(costs):
@@ -42,7 +42,7 @@ def is_pareto_efficient(costs):
     is_efficient = np.ones(costs.shape[0], dtype=bool)
     for i, c in enumerate(costs):
         is_efficient[i] = np.all(np.any(costs[:i] > c, axis=1)) and np.all(
-            np.any(costs[i + 1:] > c, axis=1)
+            np.any(costs[i + 1 :] > c, axis=1)
         )
     return is_efficient
 
@@ -65,7 +65,5 @@ def get_weightings(objectives_normed: List[str], df: pd.DataFrame) -> np.ndarray
     """
     optimized = is_pareto_efficient(df[objectives_normed].to_numpy())
     return (
-        df[optimized][objectives_normed]
-        .T.apply(lambda values: values / values.sum())
-        .T.to_numpy()
-    )
\ No newline at end of file
+        df[optimized][objectives_normed].T.apply(lambda values: values / values.sum()).T.to_numpy()
+    )