linter

Signed-off-by: 1andrin <[email protected]>

causaltune/erupt.py

@@ -73,12 +73,12 @@ def score(
         return (w * outcome).mean()
 
     def weights(
-        self,
-        df: pd.DataFrame, 
-        policy: Union[Callable, np.ndarray, pd.Series]
+        self, df: pd.DataFrame, policy: Union[Callable, np.ndarray, pd.Series]
     ) -> pd.Series:
         W = df[self.treatment_name].astype(int)
-        assert all([x >= 0 for x in W.unique()]), "Treatment values must be non-negative integers"
+        assert all(
+            [x >= 0 for x in W.unique()]
+        ), "Treatment values must be non-negative integers"
 
         # Handle policy input
         if callable(policy):
@@ -87,7 +87,9 @@ def weights(
             policy = policy.values
         policy = np.array(policy)
         d = pd.Series(index=df.index, data=policy)
-        assert all([x >= 0 for x in d.unique()]), "Policy values must be non-negative integers"
+        assert all(
+            [x >= 0 for x in d.unique()]
+        ), "Policy values must be non-negative integers"
 
         # Get propensity scores with better handling of edge cases
         if isinstance(self.propensity_model, DummyPropensity):
@@ -98,25 +100,25 @@ def weights(
             except Exception:
                 # Fallback to safe defaults if prediction fails
                 p = np.full((len(df), 2), 0.5)
-        
+
         # Clip propensity scores to avoid division by zero or extreme weights
         min_clip = max(1e-6, self.clip)  # Ensure minimum clip is not too small
         p = np.clip(p, min_clip, 1 - min_clip)
 
-        # Initialize weights 
+        # Initialize weights
         weight = np.zeros(len(df))
-        
+
         try:
             # Calculate weights with safer operations
             for i in W.unique():
-                mask = (W == i)
+                mask = W == i
                 p_i = p[:, i][mask]
                 # Add small constant to denominator to prevent division by zero
                 weight[mask] = 1 / (p_i + 1e-10)
         except Exception:
             # If something goes wrong, return safe weights
             weight = np.ones(len(df))
-        
+
         # Zero out weights where policy disagrees with actual treatment
         weight[d != W] = 0.0
 
@@ -133,12 +135,12 @@ def weights(
         else:
             # If all weights are zero, use uniform weights
             weight = np.ones(len(df)) / len(df)
-            
+
         # Final check for NaNs
         if np.any(np.isnan(weight)):
             # Replace any remaining NaNs with uniform weights
             weight = np.ones(len(df)) / len(df)
-            
+
         return pd.Series(index=df.index, data=weight)
 
     def probabilistic_erupt_score(