updated formatting

Signed-off-by: Amit Sharma <amit_sharma@live.com>

dowhy/causal_estimators/distance_matching_estimator.py

@@ -103,12 +103,7 @@ class DistanceMatchingEstimator(CausalEstimator):
         self.matched_indices_att = None
         self.matched_indices_atc = None
 
-    def fit(
-        self,
-        data: pd.DataFrame, 
-        effect_modifier_names: Optional[List[str]] = None,
-        exact_match_cols=None
-    ):
+    def fit(self, data: pd.DataFrame, effect_modifier_names: Optional[List[str]] = None, exact_match_cols=None):
         """
         Fits the estimator with data for effect estimation
         :param data: data frame containing the data
@@ -121,7 +116,7 @@ class DistanceMatchingEstimator(CausalEstimator):
                     methods support this currently.
         """
         self.exact_match_cols = exact_match_cols
-        self._fit_params = {'exact_match_cols': exact_match_cols}
+        self._fit_params = {"exact_match_cols": exact_match_cols}
         self.reset_encoders()  # Forget any existing encoders
         self._set_effect_modifiers(data, effect_modifier_names)
 

dowhy/causal_refuters/add_unobserved_common_cause.py

@@ -843,7 +843,7 @@ def sensitivity_simulation(
         new_estimator.fit(
             new_data,
             effect_modifier_names=estimate.estimator._effect_modifier_names,
-            **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {}
+            **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {},
         )
         new_effect = new_estimator.estimate_effect(
             new_data,
@@ -891,7 +891,7 @@ def sensitivity_simulation(
                     new_estimator.fit(
                         new_data,
                         effect_modifier_names=estimate.estimator._effect_modifier_names,
-                        **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {}
+                        **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {},
                     )
                     new_effect = new_estimator.estimate_effect(
                         new_data,
@@ -967,7 +967,7 @@ def sensitivity_simulation(
                 new_estimator.fit(
                     new_data,
                     effect_modifier_names=estimate.estimator._effect_modifier_names,
-                    **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {}
+                    **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {},
                 )
                 new_effect = new_estimator.estimate_effect(
                     new_data,
@@ -1025,7 +1025,7 @@ def sensitivity_simulation(
                 new_estimator.fit(
                     new_data,
                     effect_modifier_names=estimate.estimator._effect_modifier_names,
-                    **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {}
+                    **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {},
                 )
                 new_effect = new_estimator.estimate_effect(
                     new_data,

dowhy/causal_refuters/bootstrap_refuter.py

@@ -133,7 +133,7 @@ def _refute_once(
     new_estimator.fit(
         new_data,
         effect_modifier_names=estimate.estimator._effect_modifier_names,
-        **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {}
+        **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {},
     )
     new_effect = new_estimator.estimate_effect(
         new_data,

dowhy/causal_refuters/data_subset_refuter.py

@@ -77,7 +77,7 @@ def _refute_once(
     new_estimator.fit(
         new_data,
         effect_modifier_names=estimate.estimator._effect_modifier_names,
-        **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {}
+        **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {},
     )
     new_effect = new_estimator.estimate_effect(
         new_data,

dowhy/causal_refuters/dummy_outcome_refuter.py

@@ -494,7 +494,7 @@ def refute_dummy_outcome(
             new_estimator.fit(
                 new_data,
                 effect_modifier_names=estimate.estimator._effect_modifier_names,
-                **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {}
+                **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {},
             )
             new_effect = new_estimator.estimate_effect(
                 new_data,
@@ -574,7 +574,7 @@ def refute_dummy_outcome(
                 new_estimator.fit(
                     new_data,
                     effect_modifier_names=estimate.estimator._effect_modifier_names,
-                    **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {}
+                    **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {},
                 )
                 new_effect = new_estimator.estimate_effect(
                     new_data,

dowhy/causal_refuters/evalue_sensitivity_analyzer.py

@@ -262,7 +262,7 @@ class EValueSensitivityAnalyzer:
             new_estimator.fit(
                 self.data,
                 effect_modifier_names=self.estimate.estimator._effect_modifier_names,
-                **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {}
+                **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {},
             )
 
             # new effect estimate

dowhy/causal_refuters/placebo_treatment_refuter.py

@@ -155,7 +155,7 @@ def _refute_once(
     new_estimator.fit(
         new_data,
         effect_modifier_names=estimate.estimator._effect_modifier_names,
-        **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {}
+        **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {},
     )
     new_effect = new_estimator.estimate_effect(
         new_data,

dowhy/causal_refuters/random_common_cause.py

@@ -71,7 +71,7 @@ def _refute_once(
     new_estimator.fit(
         new_data,
         effect_modifier_names=estimate.estimator._effect_modifier_names,
-        **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {}
+        **new_estimator._fit_params if hasattr(new_estimator, "_fit_params") else {},
     )
     new_effect = new_estimator.estimate_effect(
         new_data,