6b1df7c3a7
- Updated `Activities` class to improve garbage collection handling. - Enhanced error messaging in `ExperimentTracking` for better clarity on update failures. - Refactored `Training` class to streamline exception handling and improve type hints. - Introduced new methods in `TrainModelParams` for better handling of experiment run IDs and model metadata. - Added functionality to extract model equations in `DataManagerRepository` for linear regression models.
34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
"""Unit tests for the CleanupFiles workflow."""
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import os
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from unittest.mock import AsyncMock, patch
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import pytest
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@pytest.mark.asyncio
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@patch('model_manager.workflows.cleanup_files.workflow')
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async def test_cleanup_files_workflow(mock_workflow_module):
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"""Test the CleanupFiles workflow."""
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from model_manager.workflows.cleanup_files import CleanupFiles
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# Mock execute_activity_method
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mock_workflow_module.execute_activity_method = AsyncMock()
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# Instantiate and run the workflow
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workflow_instance = CleanupFiles()
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with patch.dict(os.environ, {'POD_ID': 'temporal-pod'}):
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await workflow_instance.run({})
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# Verify that the activities were called with the correct parameters
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calls = mock_workflow_module.execute_activity_method.call_args_list
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assert len(calls) == 1
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# Check cleanup_temp_directories call
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local_call_args = calls[0][0][1]
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assert local_call_args['temp_path'] == 'model_manager/reports/temp'
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assert local_call_args['metadata'] == {
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'pod_id': 'temporal-pod',
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'workflow_name': 'cleanup_files',
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}
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