From 3d9d49bd0521c3b2fc7b8e092a422297109c1c9c Mon Sep 17 00:00:00 2001
From: Roland Stevenson <rolandrmgservices@gmail.com>
Date: Wed, 23 Aug 2023 10:33:07 +0000
Subject: [PATCH] linted with black

---
 tests/test_uplift_trees.py | 28 ++++++++++++++++++----------
 1 file changed, 18 insertions(+), 10 deletions(-)

diff --git a/tests/test_uplift_trees.py b/tests/test_uplift_trees.py
index c5372903..8da4eda4 100644
--- a/tests/test_uplift_trees.py
+++ b/tests/test_uplift_trees.py
@@ -250,32 +250,40 @@ def getNonleafCount(node):
     # shouldn't be larger than the number of non-leaf node
     assert num_non_zero_imp_features <= num_non_leaf_nodes
 
-def test_uplift_tree_visualization():
 
+def test_uplift_tree_visualization():
     # Data generation
     df, x_names = make_uplift_classification()
 
     # Rename features for easy interpretation of visualization
-    x_names_new = ['feature_%s'%(i) for i in range(len(x_names))]
-    rename_dict = {x_names[i]:x_names_new[i] for i in range(len(x_names))}
+    x_names_new = ["feature_%s" % (i) for i in range(len(x_names))]
+    rename_dict = {x_names[i]: x_names_new[i] for i in range(len(x_names))}
     df = df.rename(columns=rename_dict)
     x_names = x_names_new
 
     df.head()
 
-    df = df[df['treatment_group_key'].isin(['control','treatment1'])]
+    df = df[df["treatment_group_key"].isin(["control", "treatment1"])]
 
     # Split data to training and testing samples for model validation (next section)
     df_train, df_test = train_test_split(df, test_size=0.2, random_state=111)
 
     # Train uplift tree
-    uplift_model = UpliftTreeClassifier(max_depth = 4, min_samples_leaf = 200, min_samples_treatment = 50, n_reg = 100, evaluationFunction='KL', control_name='control')
+    uplift_model = UpliftTreeClassifier(
+        max_depth=4,
+        min_samples_leaf=200,
+        min_samples_treatment=50,
+        n_reg=100,
+        evaluationFunction="KL",
+        control_name="control",
+    )
 
-    uplift_model.fit(df_train[x_names].values,
-                     treatment=df_train['treatment_group_key'].values,
-                     y=df_train['conversion'].values)
+    uplift_model.fit(
+        df_train[x_names].values,
+        treatment=df_train["treatment_group_key"].values,
+        y=df_train["conversion"].values,
+    )
 
     # Plot uplift tree
-    graph = uplift_tree_plot(uplift_model.fitted_uplift_tree,x_names)
+    graph = uplift_tree_plot(uplift_model.fitted_uplift_tree, x_names)
     graph.create_png()
-