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SIENTIAPDE-1273

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Enhance data handling and export processes in Laborious workflows

- Updated `gates.py` to improve data quality validation, filtering, and formatting operations, including enhanced metrics recording.
- Refined `mlflow.py` to better manage model transformations and reference data retrieval from MLflow Model Registry.
- Enhanced `format_and_export_prediction.py` to support separate export of transformed data, improving flexibility in data handling.
- Added comprehensive test coverage for new functionalities, including transformed data formatting and retrain report generation.
- Improved documentation in `README.md` to reflect changes in activities and workflows, ensuring clarity on data processing and export paths.
This commit is contained in:
Vitor Pimentel dos Santos
2025-11-17 16:52:33 -03:00
parent a88a15c60a
commit 07dc612116
7 changed files with 531 additions and 20 deletions
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146
tests/laborious/activities/test_gates.py
View File

@@ -546,6 +546,80 @@ async def test_format_prediction_with_timestamp_invalid_policy(gates_activity):
raise AssertionError('Expected ValueError')
@mark.asyncio
async def test_format_transformed_data_single_row(gates_activity):
# Arrange
input_data = {
**metadata,
'data': {
'var1': {'2023-05-26 11:12:27': 1.0},
'var2': {'2023-05-26 11:12:27': 2.0},
},
'model_id': 'test_model',
}
# Act
result = await gates_activity.format_transformed_data(input_data)
# Assert
assert result['timestamp'] == {0: '2023-05-26 11:12:27', 1: '2023-05-26 11:12:27'}
assert result['variable'] == {0: 'var1', 1: 'var2'}
assert result['value'] == {0: 1.0, 1: 2.0}
assert result['model_id'] == {0: 'test_model', 1: 'test_model'}
gates_activity.info.assert_called()
@mark.asyncio
async def test_format_transformed_data_multiple_rows(gates_activity):
# Arrange
input_data = {
**metadata,
'data': {
'var1': {
'2023-05-26 11:12:27': 1.0,
'2023-05-26 11:12:28': 2.0,
},
'var2': {
'2023-05-26 11:12:27': 3.0,
'2023-05-26 11:12:28': 4.0,
},
},
'model_id': 'test_model',
}
# Act
result = await gates_activity.format_transformed_data(input_data)
# Assert
assert len(result['timestamp']) == 4
assert len(result['variable']) == 4
assert len(result['value']) == 4
assert len(result['model_id']) == 4
assert all(v == 'test_model' for v in result['model_id'].values())
assert set(result['variable'].values()) == {'var1', 'var2'}
gates_activity.info.assert_called()
@mark.asyncio
async def test_format_transformed_data_empty_data(gates_activity):
# Arrange
input_data = {
**metadata,
'data': {},
'model_id': 'test_model',
}
# Act
result = await gates_activity.format_transformed_data(input_data)
# Assert
assert result['timestamp'] == {}
assert result['variable'] == {}
assert result['value'] == {}
assert result['model_id'] == {}
gates_activity.info.assert_called()
@mark.asyncio
async def test_format_default_prediction(gates_activity):
# Arrange
@@ -603,6 +677,40 @@ async def test_format_retrain_report(gates_activity):
assert result['mlflow_experiment_id'] == {0: 'test_mlflow_experiment_id'}
@mark.asyncio
async def test_format_retrain_report_failure(gates_activity):
# Arrange
input_data = {
**metadata,
'experiment_response': {
'success': False,
'timestamp': '2023-05-26 11:12:27',
'message': 'failure',
},
'update_report': {
'version': '1.0.0',
'mlflow_run_id': 'test_mlflow_run_id',
'mlflow_experiment_id': 'test_mlflow_experiment_id',
},
'model_id': 'test_model',
'model_name': 'test_model',
}
# Act
result = await gates_activity.format_retrain_report(input_data)
# Assert
assert result['model_id'] == {0: 'test_model'}
assert result['model_name'] == {0: 'test_model'}
assert result['timestamp'] == {0: '2023-05-26 11:12:27'}
assert result['status'] == {0: 'failure'}
assert 'version' not in result
assert 'mlflow_run_id' not in result
assert 'mlflow_experiment_id' not in result
gates_activity.info.assert_called()
gates_activity.debug.assert_called()
@mark.asyncio
async def test_get_last_timestamp_with_data(gates_activity):
# Arrange
@@ -742,3 +850,41 @@ async def test_write_metrics(mock_metrics, gates_activity):
),
]
)
@mark.asyncio
@patch('laborious.activities.gates.metrics')
async def test_write_metrics_with_none_opc_response_time(mock_metrics, gates_activity):
"""Test write_metrics method with None response_time in opc_metrics."""
input_data = {
**metadata,
'prediction': {
'prediction': [1],
'prediction_confidence': [0.9],
'response_time': [0.1],
},
'opc_metrics': {'server1': {'tag1': 0.1, 'tag2': None}},
}
await gates_activity.write_metrics(input_data)
# Verify that metrics for tag1 are emitted
gates_activity.emit_metric.assert_any_call(
metric_object=mock_metrics.PREDICTION_OPC_WRITING_RESPONSE_TIME_MONITOR,
method='observe',
tags={
'pod_id': gates_activity.pod_id,
'model_name': metadata['metadata']['model_name'],
'workflow_name': metadata['metadata']['workflow_name'],
'opc_server_id': 'server1',
'tag': 'tag1',
},
value=0.1,
)
# Verify that metrics for tag2 (with None response_time) are NOT emitted
calls = [
c
for c in gates_activity.emit_metric.call_args_list
if len(c[1].get('tags', {})) > 0 and c[1]['tags'].get('tag') == 'tag2'
]
assert len(calls) == 0, 'Metrics should not be emitted for None response_time'

89
tests/laborious/activities/test_mlflow.py
View File

@@ -76,6 +76,11 @@ def mlflow(mock_minio_repository, mock_mlflow_repository):
mlflow.send_notification = MagicMock()
mlflow.emit_metric = AsyncMock()
mlflow.send_notification_async = AsyncMock()
mlflow.error = MagicMock()
mlflow.debug = MagicMock()
mlflow.info = MagicMock()
mlflow.warning = MagicMock()
mlflow.critical = MagicMock()
return mlflow
@@ -488,3 +493,87 @@ async def test_update_production_model_error(mlflow):
)
else:
raise AssertionError('No exception raised')
@mark.asyncio
@patch('laborious.activities.mlflow.to_datetime')
async def test_get_reference_data_success(mock_to_datetime, mlflow):
# Arrange
input_data = {
**metadata,
'model_name': 'test_model',
}
# Mock reference data DataFrame
mock_reference_data = MagicMock()
mock_reference_data.__getitem__.return_value = MagicMock()
mock_to_datetime.return_value.dt.strftime.return_value = MagicMock()
mock_reference_data.to_dict.return_value = [
{'timestamp': '2023-05-26 11:12:27', 'value': 1.0},
{'timestamp': '2023-05-26 11:12:28', 'value': 2.0},
]
mlflow.model_monitoring_repository.load_artifact_dataframe.return_value = mock_reference_data
# Act
result = await mlflow.get_reference_data(input_data)
# Assert
mlflow.model_monitoring_repository.load_artifact_dataframe.assert_called_once_with(
model_name='test_model',
artifact_path='evaluation_data.csv',
metadata=metadata['metadata'],
)
mock_to_datetime.assert_called_once_with(mock_reference_data.__getitem__.return_value)
mock_reference_data.to_dict.assert_called_once_with(orient='records')
assert result == mock_reference_data.to_dict.return_value
@mark.asyncio
async def test_get_reference_data_not_found(mlflow):
# Arrange
input_data = {
**metadata,
'model_name': 'test_model',
}
mlflow.model_monitoring_repository.load_artifact_dataframe.return_value = None
# Act
result = await mlflow.get_reference_data(input_data)
# Assert
mlflow.model_monitoring_repository.load_artifact_dataframe.assert_called_once_with(
model_name='test_model',
artifact_path='evaluation_data.csv',
metadata=metadata['metadata'],
)
mlflow.warning.assert_called_once_with(
'Reference data not found for model test_model', metadata['metadata']
)
assert result is None
@mark.asyncio
async def test_get_reference_data_exception(mlflow):
# Arrange
input_data = {
**metadata,
'model_name': 'test_model',
}
mlflow.model_monitoring_repository.load_artifact_dataframe.side_effect = Exception(
'Error loading artifact'
)
# Act & Assert
with raises(Exception) as e:
await mlflow.get_reference_data(input_data)
assert str(e.value) == 'Error loading artifact'
mlflow.model_monitoring_repository.load_artifact_dataframe.assert_called_once_with(
model_name='test_model',
artifact_path='evaluation_data.csv',
metadata=metadata['metadata'],
)