update comment

Author: xingzhongyu

tests/test_get_result_web.py

@@ -11,78 +11,78 @@
 from examples.atlas.get_result_web import check_exist, check_identical_strings, spilt_web, write_ans
 
 
-# 测试 check_identical_strings 函数
+# Test check_identical_strings function
 def test_check_identical_strings():
-    # 测试相同字符串的情况
+    # Test case for identical strings
     assert check_identical_strings(["test", "test", "test"]) == "test"
 
-    # 测试空列表
+    # Test case for empty list
     with pytest.raises(ValueError, match="The list is empty"):
         check_identical_strings([])
 
-    # 测试不同字符串
+    # Test case for different strings
     with pytest.raises(ValueError, match="Different strings found"):
         check_identical_strings(["test1", "test2"])
 
 
-# 测试 spilt_web 函数
+# Test spilt_web function
 def test_spilt_web():
-    # 测试有效的URL
+    # Test valid URL
     url = "https://wandb.ai/user123/project456/sweeps/abc789"
     result = spilt_web(url)
     assert result == ("user123", "project456", "abc789")
 
-    # 测试无效的URL
+    # Test invalid URL
     invalid_url = "https://invalid-url.com"
     assert spilt_web(invalid_url) is None
 
 
-# 测试 check_exist 函数
+# Test check_exist function
 def test_check_exist(tmp_path):
-    # 创建临时测试目录
+    # Create temporary test directory
     results_dir = tmp_path / "results" / "params"
     results_dir.mkdir(parents=True)
 
-    # 测试空目录
+    # Test empty directory
     assert check_exist(str(tmp_path)) is False
 
-    # 创建测试文件
+    # Create test files
     (results_dir / "file1.txt").touch()
     (results_dir / "file2.txt").touch()
 
-    # 测试有多个文件的情况
+    # Test case with multiple files
     assert check_exist(str(tmp_path)) is True
 
 
-# 创建测试固定装置
+# Create test fixed data
 @pytest.fixture
 def sample_df():
     return pd.DataFrame({"id": ["run1", "run2", "run3"], "metric": [0.8, 0.9, 0.7]})
 
 
-# 如果需要模拟wandb API，可以使用mock
+#  use mock to simulate wandb API
 @pytest.fixture
 def mock_wandb(mocker):
     mock_api = mocker.patch("wandb.Api")
-    # 这里可以设置mock的返回值
+    # set mock return values
     return mock_api
 
 
-# 添加一个mock fixture来模拟ATLASDIR
+# Add a mock fixture to simulate ATLASDIR
 @pytest.fixture(autouse=True)
 def mock_settings(tmp_path, monkeypatch):
     """Mock ATLASDIR and METADIR settings for tests."""
-    # 创建临时目录
+    # Create temporary directory
     mock_atlas_dir = tmp_path / "atlas"
     mock_meta_dir = tmp_path / "meta"
     mock_atlas_dir.mkdir(parents=True)
     mock_meta_dir.mkdir(parents=True)
 
-    # 设置环境变量
+    # Set environment variables
     monkeypatch.setenv("ATLAS_DIR", str(mock_atlas_dir))
     monkeypatch.setenv("META_DIR", str(mock_meta_dir))
 
-    # 如果直接从dance.settings导入，也替换这些值
+    # If import directly from dance.settings, also replace these values
     monkeypatch.setattr("examples.atlas.get_result_web.ATLASDIR", mock_atlas_dir)
     monkeypatch.setattr("examples.atlas.get_result_web.METADIR", mock_meta_dir)
 
@@ -94,7 +94,7 @@ def test_write_ans(mock_settings):
     sweep_results_dir.mkdir(parents=True)
     output_file = sweep_results_dir / "heart_ans.csv"
 
-    # 创建初始数据
+    # Create initial data
     existing_data = pd.DataFrame({
         'Dataset_id': ['dataset1', 'dataset2'],
         'cta_actinn': ['url1', 'url2'],
@@ -103,35 +103,35 @@ def test_write_ans(mock_settings):
     })
     existing_data.to_csv(output_file)
 
-    # 测试数据：包含较低分数和较高分数的情况
+    # Test data: include lower score and higher score
     new_data = pd.DataFrame({
         'Dataset_id': ['dataset1', 'dataset2'],
         'cta_actinn': ['url1_new', 'url2_new'],
         'cta_actinn_best_yaml': ['yaml1_new', 'yaml2_new'],
-        'cta_actinn_best_res': [0.9, 0.6]  # dataset1更高分数，dataset2更低分数
+        'cta_actinn_best_res': [0.9, 0.6]  # dataset1 higher score, dataset2 lower score
     })
 
     write_ans("heart", new_data, output_file)
 
-    # 验证结果
+    # Verify results
     result_df = pd.read_csv(output_file)
 
-    # 验证高分数更新成功
+    # Verify high score update success
     dataset1_row = result_df[result_df['Dataset_id'] == 'dataset1'].iloc[0]
     assert dataset1_row['cta_actinn_best_res'] == 0.9
     assert dataset1_row['cta_actinn'] == 'url1_new'
     assert dataset1_row['cta_actinn_best_yaml'] == 'yaml1_new'
 
-    # 验证低分数保持不变
+    # Verify low score remains unchanged
     dataset2_row = result_df[result_df['Dataset_id'] == 'dataset2'].iloc[0]
     assert dataset2_row['cta_actinn_best_res'] == 0.7
     assert dataset2_row['cta_actinn'] == 'url2'
     assert dataset2_row['cta_actinn_best_yaml'] == 'yaml2'
 
 
-# 测试完全新的数据写入（文件不存在的情况）
+# Test completely new data write (file does not exist)
 def test_write_ans_new_file(mock_settings):
-    # 使用mock_settings而不是创建新的临时目录
+    # Use mock_settings instead of creating new temporary directory
     sweep_results_dir = mock_settings / "sweep_results"
     sweep_results_dir.mkdir(parents=True)
     output_file = sweep_results_dir / "new_heart_ans.csv"
@@ -143,9 +143,9 @@ def test_write_ans_new_file(mock_settings):
         'cta_actinn_best_res': [0.8, 0.9]
     })
 
-    # 测试写入新文件
+    # Test write to new file
 
-    # 验证文件被创建并包含正确的数据
+    # Verify file is created and contains correct data
     write_ans("heart", new_data, output_file)
     assert output_file.exists()