reformat with black

uber · May 20, 2024 · 3bbce11 · 3bbce11
1 parent 9d8bba3
commit 3bbce11
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Showing 3 changed files with 24 additions and 4 deletions.
diff --git a/causalml/inference/tree/causal/_tree.py b/causalml/inference/tree/causal/_tree.py
@@ -42,7 +42,13 @@ def _support_missing_values(self, X) -> bool:
         return False
 
     def fit(
-        self, X, y, treatment, sample_weight=None, check_input=True, X_idx_sorted="deprecated"
+        self,
+        X,
+        y,
+        treatment,
+        sample_weight=None,
+        check_input=True,
+        X_idx_sorted="deprecated",
     ):
         random_state = check_random_state(self.random_state)
 

diff --git a/causalml/inference/tree/causal/causalforest.py b/causalml/inference/tree/causal/causalforest.py
@@ -205,7 +205,13 @@ def __init__(
         self.alpha = alpha
         self.groups_cnt = groups_cnt
 
-    def _fit(self, X: np.ndarray, treatment: np.ndarray, y: np.ndarray, sample_weight: np.ndarray = None):
+    def _fit(
+        self,
+        X: np.ndarray,
+        treatment: np.ndarray,
+        y: np.ndarray,
+        sample_weight: np.ndarray = None,
+    ):
         """
         Build a forest of trees from the training set (X, y).
         With modified _parallel_build_trees for Causal Trees used in BaseForest.fit()
@@ -377,7 +383,13 @@ def _fit(self, X: np.ndarray, treatment: np.ndarray, y: np.ndarray, sample_weigh
 
         return self
 
-    def fit(self, X: np.ndarray, treatment: np.ndarray, y: np.ndarray, sample_weight: np.ndarray = None):
+    def fit(
+        self,
+        X: np.ndarray,
+        treatment: np.ndarray,
+        y: np.ndarray,
+        sample_weight: np.ndarray = None,
+    ):
         """
         Fit Causal RandomForest
         Args:

diff --git a/causalml/inference/tree/causal/causaltree.py b/causalml/inference/tree/causal/causaltree.py
@@ -179,7 +179,9 @@ def fit(
         X, y, w = self._prepare_data(X=X, y=y, treatment=treatment)
         self.treatment_groups = np.unique(w)
 
-        super().fit(X=X, treatment=w, y=y, sample_weight=sample_weight, check_input=check_input)
+        super().fit(
+            X=X, treatment=w, y=y, sample_weight=sample_weight, check_input=check_input
+        )
 
         if self.groups_cnt:
             self._groups_cnt = self._count_groups_distribution(X=X, treatment=w)