fix lint errors

uber · Aug 1, 2024 · f8aa150 · f8aa150
1 parent 5b8c063
commit f8aa150
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Showing 3 changed files with 11 additions and 4 deletions.
diff --git a/causalml/inference/meta/rlearner.py b/causalml/inference/meta/rlearner.py
@@ -667,14 +667,17 @@ def fit(self, X, treatment, y, p=None, sample_weight=None, verbose=True):
                 self.models_tau[group].fit(
                     X=X_train_filt,
                     y=(y_train_filt - yhat_train_filt) / (w_train - p_train_filt),
-                    sample_weight=sample_weight_train_filt * ((w_train - p_train_filt) ** 2),
+                    sample_weight=sample_weight_train_filt
+                    * ((w_train - p_train_filt) ** 2),
                     eval_set=[
                         (
                             X_test_filt,
                             (y_test_filt - yhat_test_filt) / (w_test - p_test_filt),
                         )
                     ],
-                    sample_weight_eval_set=[sample_weight_test_filt * ((w_test - p_test_filt) ** 2)],
+                    sample_weight_eval_set=[
+                        sample_weight_test_filt * ((w_test - p_test_filt) ** 2)
+                    ],
                     verbose=verbose,
                 )
 

diff --git a/causalml/metrics/visualize.py b/causalml/metrics/visualize.py
@@ -640,7 +640,9 @@ def plot_qini(
 def plot_tmlegain(
     df,
     inference_col,
-    learner=LGBMRegressor(num_leaves=64, learning_rate=0.05, n_estimators=300, verbose=-1),
+    learner=LGBMRegressor(
+        num_leaves=64, learning_rate=0.05, n_estimators=300, verbose=-1
+    ),
     outcome_col="y",
     treatment_col="w",
     p_col="tau",

diff --git a/causalml/propensity.py b/causalml/propensity.py
@@ -115,7 +115,9 @@ class GradientBoostedPropensityModel(PropensityModel):
     def __init__(self, early_stop=False, clip_bounds=(1e-3, 1 - 1e-3), **model_kwargs):
         self.early_stop = early_stop
 
-        super(GradientBoostedPropensityModel, self).__init__(clip_bounds, **model_kwargs)
+        super(GradientBoostedPropensityModel, self).__init__(
+            clip_bounds, **model_kwargs
+        )
 
     @property
     def _model(self):