From 6d650ddb7694d0dfafc06f5c247bc525e84cb0cf Mon Sep 17 00:00:00 2001
From: Blaz Mramor <bmramor@outbrain.com>
Date: Sun, 31 Mar 2024 21:40:21 +0200
Subject: [PATCH] remove logger from function

---
 outrank/core_ranking.py      | 5 ++---
 tests/ranking_module_test.py | 4 ++--
 2 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/outrank/core_ranking.py b/outrank/core_ranking.py
index 5633ada..30f892c 100644
--- a/outrank/core_ranking.py
+++ b/outrank/core_ranking.py
@@ -186,7 +186,6 @@ def enrich_with_transformations(
 
 def compute_combined_features(
     input_dataframe: pd.DataFrame,
-    logger: Any,
     args: Any,
     pbar: Any,
     is_3mr: bool = False,
@@ -547,7 +546,7 @@ def compute_batch_ranking(
     if args.interaction_order > 1 or args.reference_model_JSON:
         pbar.set_description('Constructing new features')
         input_dataframe = compute_combined_features(
-            input_dataframe, logger, args, pbar,
+            input_dataframe, args, pbar,
         )
 
     # in case of 3mr we compute the score of combinations against the target
@@ -556,7 +555,7 @@ def compute_batch_ranking(
             'Constructing features for computing relations in 3mr',
         )
         input_dataframe = compute_combined_features(
-            input_dataframe, logger, args, pbar, True,
+            input_dataframe, args, pbar, True,
         )
 
     if args.include_noise_baseline_features == 'True' and args.heuristic != 'Constant':
diff --git a/tests/ranking_module_test.py b/tests/ranking_module_test.py
index e49880c..fd99092 100644
--- a/tests/ranking_module_test.py
+++ b/tests/ranking_module_test.py
@@ -82,7 +82,7 @@ def test_compute_combinations(self):
         random_df.columns = ['F1', 'F2', 'F3']
         local_pbar = tqdm.tqdm(total=100, position=0)
         transformed_df = compute_combined_features(
-            random_df, None, args, local_pbar,
+            random_df, args, local_pbar,
         )
         self.assertEqual(transformed_df.shape[1], 4)
 
@@ -91,7 +91,7 @@ def test_compute_combinations(self):
         random_df = pd.DataFrame(random_matrix)
         random_df.columns = ['F1', 'F2', 'F3']
         transformed_df = compute_combined_features(
-            random_df, None, args, local_pbar,
+            random_df, args, local_pbar,
         )
         self.assertEqual(transformed_df.shape[1], 6)