fix pylint issues

Signed-off-by: hsj576 <[email protected]>
kubeedge · Sep 21, 2023 · 72fa77e · 72fa77e
1 parent 1e4917e
commit 72fa77e
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Showing 3 changed files with 10 additions and 10 deletions.
diff --git a/core/storymanager/rank/rank.py b/core/storymanager/rank/rank.py
@@ -196,7 +196,7 @@ def _get_selected(self, test_cases, test_results) -> pd.DataFrame:
         all_df = copy.deepcopy(self.all_df)
         selected_df = pd.DataFrame(all_df, columns=header)
         selected_df = selected_df.drop_duplicates(header[:-2])
-
+        # pylint: disable=E1136
         paradigms = self.selected_dataitem.get("paradigms")
         if paradigms != ["all"]:
             selected_df = selected_df.loc[selected_df["paradigm"].isin(paradigms)]

diff --git a/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py b/core/testcasecontroller/algorithm/paradigm/lifelong_learning/lifelong_learning.py
@@ -175,12 +175,12 @@ def run(self):
                     self.cloud_task_index = self._train(self.cloud_task_index,
                                                     train_dataset_file,
                                                     r)
-                    
+
                     self.edge_task_index, tasks_detail, res = self.my_eval(
                                                     self.cloud_task_index,
                                                     eval_dataset_file,
                                                     r)
-                    
+
                 else:
                     infer_dataset_file, eval_dataset_file = dataset_files[r]
                     inference_results, unseen_task_train_samples = self._inference(
@@ -197,7 +197,7 @@ def run(self):
                     self.cloud_task_index = self._train(self.cloud_task_index,
                                                         unseen_task_train_samples,
                                                         r)
-                
+
                 tmp_dict = {}
                 for j in range(1, rounds+1):
                     _, eval_dataset_file = dataset_files[j]
@@ -232,7 +232,7 @@ def run(self):
                     scores_list.append(tmp_dict[key])
                     my_dict[key] = scores_list
                     LOGGER.info(f"{key} scores: {scores_list}")
-                
+
 
             self.edge_task_index, tasks_detail, res = self.my_eval(self.cloud_task_index,
                                                       self.dataset.test_url,

diff --git a/core/testenvmanager/dataset/dataset.py b/core/testenvmanager/dataset/dataset.py
@@ -167,7 +167,7 @@ def split_dataset(self, dataset_url, dataset_format, ratio, method="default",
                                               data_types=dataset_types,
                                               output_dir=output_dir,
                                               times=times)
-        
+
         if method == "hard-example_splitting":
             return self._hard_example_splitting(dataset_url, dataset_format, ratio,
                                               data_types=dataset_types,
@@ -341,16 +341,16 @@ def _hard_example_splitting(self, data_file, data_format, ratio,
         all_num = len(all_data)
         step = int(all_num / (times*2))
         data_files.append((
-                self._get_dataset_file(all_data[:int((all_num * ratio)/2)], output_dir,
+            self._get_dataset_file(all_data[:int((all_num * ratio)/2)], output_dir,
                                        data_types[0], 0, data_format),
-                self._get_dataset_file(all_data[int((all_num * ratio)/2):int(all_num/2)], output_dir,
+            self._get_dataset_file(all_data[int((all_num * ratio)/2):int(all_num/2)], output_dir,
                                        data_types[1], 0, data_format)))
         index = 1
         while index <= times:
             if index == times:
-                new_dataset = all_data[int(all_num/2) + step * (index - 1):]
+                new_dataset = all_data[int(all_num/2)+step*(index-1):]
             else:
-                new_dataset = all_data[int(all_num/2) + step * (index - 1): int(all_num/2) + step * index]
+                new_dataset = all_data[int(all_num/2)+step*(index-1): int(all_num/2)+step*index]
 
             new_num = len(new_dataset)