[ci] resolve warning in tests (#6154)

tests/python_package_test/test_engine.py

@@ -755,7 +755,7 @@ def test_ranking_prediction_early_stopping():
 # (in our example it is simply the ordering by some feature correlated with relevance, e.g., 34)
 # and clicks on that document (new_label=1) with some probability 'pclick' depending on its true relevance;
 # at each position the user may stop the traversal with some probability pstop. For the non-clicked documents,
-# new_label=0. Thus the generated new labels are biased towards the baseline ranker. 
+# new_label=0. Thus the generated new labels are biased towards the baseline ranker.
 # The positions of the documents in the ranked lists produced by the baseline, are returned.
 def simulate_position_bias(file_dataset_in, file_query_in, file_dataset_out, baseline_feature):
     # a mapping of a document's true relevance (defined on a 5-grade scale) into the probability of clicking it
@@ -772,35 +772,35 @@ def get_pclick(label):
             return 0.9
     # an instantiation of a cascade model where the user stops with probability 0.2 after observing each document
     pstop = 0.2
- 
+
     f_dataset_in = open(file_dataset_in, 'r')
     f_dataset_out = open(file_dataset_out, 'w')
     random.seed(10)
     positions_all = []
     for line in open(file_query_in):
         docs_num = int (line)
         lines = []
-        index_values = []    
+        index_values = []
         positions = [0] * docs_num
         for index in range(docs_num):
             features = f_dataset_in.readline().split()
             lines.append(features)
             val = 0.0
             for feature_val in features:
-                feature_val_split = feature_val.split(":")           
+                feature_val_split = feature_val.split(":")
                 if int(feature_val_split[0]) == baseline_feature:
                     val = float(feature_val_split[1])
             index_values.append([index, val])
         index_values.sort(key=lambda x: -x[1])
-        stop = False 
+        stop = False
         for pos in range(docs_num):
             index = index_values[pos][0]
             new_label = 0
             if not stop:
                 label = int(lines[index][0])
                 pclick = get_pclick(label)
                 if random.random() < pclick:
-                    new_label = 1       
+                    new_label = 1
                 stop = random.random() < pstop
             lines[index][0] = str(new_label)
             positions[index] = pos
@@ -843,7 +843,7 @@ def test_ranking_with_position_information_with_file(tmp_path):
     lgb_train = lgb.Dataset(str(tmp_path / 'rank.train'), params=params)
     lgb_valid = [lgb_train.create_valid(str(tmp_path / 'rank.test'))]
     gbm_unbiased_with_file = lgb.train(params, lgb_train, valid_sets = lgb_valid, num_boost_round=50)
-    
+
     # the performance of the unbiased LambdaMART should outperform the plain LambdaMART on the dataset with position bias
     assert gbm_baseline.best_score['valid_0']['ndcg@3'] + 0.03 <= gbm_unbiased_with_file.best_score['valid_0']['ndcg@3']
 
@@ -853,7 +853,7 @@ def test_ranking_with_position_information_with_file(tmp_path):
         file.close()
     lgb_train = lgb.Dataset(str(tmp_path / 'rank.train'), params=params)
     lgb_valid = [lgb_train.create_valid(str(tmp_path / 'rank.test'))]
-    with pytest.raises(lgb.basic.LightGBMError, match="Positions size \(3006\) doesn't match data size"):
+    with pytest.raises(lgb.basic.LightGBMError, match=r"Positions size \(3006\) doesn't match data size"):
         lgb.train(params, lgb_train, valid_sets = lgb_valid, num_boost_round=50)