Reformat file for ruff check

tests/python_package_test/test_engine.py

@@ -3458,15 +3458,20 @@ def test_interaction_constraints():
         num_boost_round=10,
     )
 
-@pytest.mark.skipif(getenv('TASK', '') == 'cuda', reason='Interaction constraints are not yet supported on the CUDA version')
+
+@pytest.mark.skipif(
+    getenv("TASK", "") == "cuda", reason="Interaction constraints are not yet supported on the CUDA version"
+)
 def test_tree_interaction_constraints():
     def check_consistency(est, tree_interaction_constraints):
         feat_to_index = {feat: i for i, feat in enumerate(est.feature_name())}
         tree_df = est.trees_to_dataframe()
         inter_found = set()
         for tree_index in tree_df["tree_index"].unique():
             tree_df_per_index = tree_df[tree_df["tree_index"] == tree_index]
-            feat_used = [feat_to_index[feat] for feat in tree_df_per_index["split_feature"].unique() if feat is not None]
+            feat_used = [
+                feat_to_index[feat] for feat in tree_df_per_index["split_feature"].unique() if feat is not None
+            ]
             inter_found.add(tuple(sorted(feat_used)))
         for feats_found in inter_found:
             found = False
@@ -3475,16 +3480,17 @@ def check_consistency(est, tree_interaction_constraints):
                     found = True
                     break
             assert found is True
+
     X, y = make_synthetic_regression(n_samples=400, n_features=30)
     num_features = X.shape[1]
     train_data = lgb.Dataset(X, label=y)
     # check that tree constraint containing all features is equivalent to no constraint
-    params = {'verbose': -1,
-              'seed': 0}
+    params = {"verbose": -1, "seed": 0}
     est = lgb.train(params, train_data, num_boost_round=10)
     pred1 = est.predict(X)
-    est = lgb.train(dict(params, tree_interaction_constraints=[list(range(num_features))]), train_data,
-                    num_boost_round=10)
+    est = lgb.train(
+        dict(params, tree_interaction_constraints=[list(range(num_features))]), train_data, num_boost_round=10
+    )
     pred2 = est.predict(X)
     np.testing.assert_allclose(pred1, pred2)
 
@@ -3507,24 +3513,26 @@ def check_consistency(est, tree_interaction_constraints):
     check_consistency(est, tree_interaction_constraints)
 
 
-
-@pytest.mark.skipif(getenv('TASK', '') == 'cuda', reason='Interaction constraints are not yet supported on the CUDA version')
+@pytest.mark.skipif(
+    getenv("TASK", "") == "cuda", reason="Interaction constraints are not yet supported on the CUDA version"
+)
 def test_max_tree_interactions():
     def check_n_interactions(est):
         feat_to_index = {feat: i for i, feat in enumerate(est.feature_name())}
         tree_df = est.trees_to_dataframe()
         max_n_interactions = 0
         for tree_index in tree_df["tree_index"].unique():
             tree_df_per_index = tree_df[tree_df["tree_index"] == tree_index]
-            feat_used = [feat_to_index[feat] for feat in tree_df_per_index["split_feature"].unique() if feat is not None]
+            feat_used = [
+                feat_to_index[feat] for feat in tree_df_per_index["split_feature"].unique() if feat is not None
+            ]
             max_n_interactions = max(max_n_interactions, len(feat_used))
         assert max_n_interactions <= est.params["max_tree_interactions"]
 
     X, y = make_synthetic_regression(n_samples=400, n_features=30)
     train_data = lgb.Dataset(X, label=y)
     # check that limiting the number of interaction to the number of features is equivalent to no constraint
-    params = {'verbose': -1,
-              'seed': 0}
+    params = {"verbose": -1, "seed": 0}
     est = lgb.train(params, train_data, num_boost_round=100)
     pred1 = est.predict(X)
     est = lgb.train(dict(params, max_tree_interactions=400), train_data, num_boost_round=100)
@@ -3545,18 +3553,23 @@ def check_n_interactions(est):
     est = lgb.train(new_params, train_data, num_boost_round=100)
     check_n_interactions(est)
 
-@pytest.mark.skipif(getenv('TASK', '') == 'cuda', reason='Interaction constraints are not yet supported on the CUDA version')
+
+@pytest.mark.skipif(
+    getenv("TASK", "") == "cuda", reason="Interaction constraints are not yet supported on the CUDA version"
+)
 def test_max_interactions():
     def check_interactions(est, max_interactions):
         feat_to_index = {feat: i for i, feat in enumerate(est.feature_name())}
         tree_df = est.trees_to_dataframe()
         inter_found = set()
         for tree_index in tree_df["tree_index"].unique():
             tree_df_per_index = tree_df[tree_df["tree_index"] == tree_index]
-            feat_used = [feat_to_index[feat] for feat in tree_df_per_index["split_feature"].unique() if feat is not None]
+            feat_used = [
+                feat_to_index[feat] for feat in tree_df_per_index["split_feature"].unique() if feat is not None
+            ]
             add_inter = True
             for inter in inter_found:
-                if set(feat_used) <= set(inter): # the interaction found is a subset of another interaction
+                if set(feat_used) <= set(inter):  # the interaction found is a subset of another interaction
                     add_inter = False
             if add_inter:
                 inter_found.add(tuple(sorted(feat_used)))
@@ -3565,8 +3578,7 @@ def check_interactions(est, max_interactions):
     X, y = make_synthetic_regression(n_samples=400, n_features=30)
     train_data = lgb.Dataset(X, label=y)
     # check that limiting the number of distinct interactions to the number of trees is equivalent to no constraint
-    params = {'verbose': -1,
-              'seed': 0}
+    params = {"verbose": -1, "seed": 0}
     est = lgb.train(params, train_data, num_boost_round=100)
     pred1 = est.predict(X)
 
@@ -3590,7 +3602,6 @@ def check_interactions(est, max_interactions):
     check_interactions(est, max_interactions)
 
 
-
 def test_linear_trees_num_threads():
     # check that number of threads does not affect result
     np.random.seed(0)