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SIENTIAPDE-1241: Suppress PytestUnraisableExceptionWarning and update test data in training repository tests.

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This commit is contained in:
Bruno Domingues
2025-10-30 15:22:59 -03:00
parent bb25a19d8c
commit 5b14f1016b
3 changed files with 21 additions and 11 deletions
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5
tests/activities/test_activities.py
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@@ -298,5 +298,10 @@ def test_activities_del_with_engine_exception_caught(
def __del__(self): def __del__(self):
raise RuntimeError('Test error') raise RuntimeError('Test error')
# Suppress the PytestUnraisableExceptionWarning for this specific test
import warnings
warnings.filterwarnings('ignore', category=pytest.PytestUnraisableExceptionWarning)
with patch('builtins.super', return_value=MockSuperWithError()): with patch('builtins.super', return_value=MockSuperWithError()):
activities.__del__() activities.__del__()

5
tests/activities/test_experiment_tracking.py
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@@ -143,6 +143,11 @@ def test_experiment_tracking_del_with_engine_exception(
def __del__(self): def __del__(self):
raise RuntimeError('Test error') raise RuntimeError('Test error')
# Suppress the PytestUnraisableExceptionWarning for this specific test
import warnings
warnings.filterwarnings('ignore', category=pytest.PytestUnraisableExceptionWarning)
with patch('builtins.super', return_value=MockSuperWithError()): with patch('builtins.super', return_value=MockSuperWithError()):
et.__del__() et.__del__()

22
tests/utils/repository/test_training_repository.py
View File

@@ -304,12 +304,12 @@ class TestAfterTrainCalculation:
x_train = pd.DataFrame( x_train = pd.DataFrame(
{'var1': [1, 2, 3], 'var2': [4, 5, 6], 'var3': [7, 8, 9]}, index=[0, 1, 2] {'var1': [1, 2, 3], 'var2': [4, 5, 6], 'var3': [7, 8, 9]}, index=[0, 1, 2]
) )
x_test = pd.DataFrame({'var1': [10], 'var2': [11], 'var3': [12]}, index=[3]) x_test = pd.DataFrame({'var1': [10, 11], 'var2': [11, 12], 'var3': [12, 13]}, index=[3, 4])
y_train = pd.Series([100, 200, 300], index=[0, 1, 2], name='target') y_train = pd.Series([100, 200, 300], index=[0, 1, 2], name='target')
y_test = pd.Series([400], index=[3], name='target') y_test = pd.Series([400, 500], index=[3, 4], name='target')
# Mock predict to return a simple array # Mock predict to return a simple array
sample_linear_model.predict = MagicMock(return_value=np.array([450.0])) sample_linear_model.predict = MagicMock(return_value=np.array([450.0, 550.0]))
return TrainModelResult( return TrainModelResult(
params=sample_params, params=sample_params,
@@ -388,17 +388,17 @@ class TestAfterTrainCalculation:
x_train = pd.DataFrame( x_train = pd.DataFrame(
{'var1': [1, 2, 3], 'var2': [4, 5, 6], 'var3': [7, 8, 9]}, index=[0, 1, 2] {'var1': [1, 2, 3], 'var2': [4, 5, 6], 'var3': [7, 8, 9]}, index=[0, 1, 2]
) )
x_test = pd.DataFrame({'var1': [10], 'var2': [11], 'var3': [12]}, index=[3]) x_test = pd.DataFrame({'var1': [10, 11], 'var2': [11, 12], 'var3': [12, 13]}, index=[3, 4])
y_train = pd.Series([100, 200, 300], index=[0, 1, 2], name='target') y_train = pd.Series([100, 200, 300], index=[0, 1, 2], name='target')
y_test = pd.Series([400], index=[3], name='target') y_test = pd.Series([400, 500], index=[3, 4], name='target')
# Mock predict # Mock predict to return a simple array
sample_linear_model.predict = MagicMock(return_value=np.array([450.0])) sample_linear_model.predict = MagicMock(return_value=np.array([450.0, 550.0]))
# Create mock scaler with denormalize methods # Create mock scaler with denormalize methods
mock_scaler = MagicMock() mock_scaler = MagicMock()
mock_scaler.denormalize_single_input = MagicMock(side_effect=lambda x, col: x * 2) mock_scaler.denormalize_single_input = MagicMock(side_effect=lambda x, col: x * 2)
mock_scaler.denormalize_predictions = MagicMock(return_value=np.array([900.0])) mock_scaler.denormalize_predictions = MagicMock(return_value=np.array([900.0, 1100.0]))
# Create mock preprocessor # Create mock preprocessor
mock_process_data = MagicMock() mock_process_data = MagicMock()
@@ -432,12 +432,12 @@ class TestAfterTrainCalculation:
x_train = pd.DataFrame( x_train = pd.DataFrame(
{'var1': [1, 2, 3], 'var2': [4, 5, 6], 'var3': [7, 8, 9]}, index=[0, 1, 2] {'var1': [1, 2, 3], 'var2': [4, 5, 6], 'var3': [7, 8, 9]}, index=[0, 1, 2]
) )
x_test = pd.DataFrame({'var1': [10], 'var2': [11], 'var3': [12]}, index=[3]) x_test = pd.DataFrame({'var1': [10, 11], 'var2': [11, 12], 'var3': [12, 13]}, index=[3, 4])
y_train = pd.Series([100, 200, 300], index=[0, 1, 2], name='target') y_train = pd.Series([100, 200, 300], index=[0, 1, 2], name='target')
y_test = pd.Series([400], index=[3], name='target') y_test = pd.Series([400, 500], index=[3, 4], name='target')
# Mock predict # Mock predict
sample_linear_model.predict = MagicMock(return_value=np.array([450.0])) sample_linear_model.predict = MagicMock(return_value=np.array([450.0, 550.0]))
# Create mock sklearn scaler (without denormalize methods) # Create mock sklearn scaler (without denormalize methods)
mock_scaler = MagicMock() mock_scaler = MagicMock()