bee6036205
This commit introduces the 'Training' activity and 'TrainingRepository' for handling ML model training operations within the Model Manager system. - Added model_manager/activities/training.py for the Training activity, which extends BaseActivity and integrates with Temporal workflows. - Added model_manager/utils/repository/training_repository.py for the TrainingRepository, which encapsulates the core training logic. - Updated model_manager/activities/activities.py to include the Training activity in the main activities orchestrator. - Updated README.md to document the new 'Training' component. - Added unit tests for the new activity and repository.
289 lines
9.9 KiB
Python
289 lines
9.9 KiB
Python
"""Unit tests for Training activity."""
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from io import BytesIO
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from unittest.mock import MagicMock, patch
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from pytest import mark
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from model_manager.activities.training import Training
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from model_manager.utils.models.train_model_result import TrainModelResult
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@mark.asyncio
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@patch('model_manager.activities.training.TrainingRepository')
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async def test_train_model_success(mock_training_repository_class):
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"""Test successful model training."""
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# Create mock repository instance
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mock_repository = MagicMock()
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mock_training_repository_class.return_value = mock_repository
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# Create mock train result
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mock_train_result = MagicMock(spec=TrainModelResult)
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mock_train_result.mse_val = 0.5
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mock_train_result.mae_val = 0.3
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mock_train_result.r2_val = 0.95
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mock_final_result = MagicMock(spec=TrainModelResult)
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mock_final_result.mse_val = 0.5
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mock_final_result.mae_val = 0.3
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mock_final_result.r2_val = 0.95
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# Setup repository mocks
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mock_repository.train.return_value = mock_train_result
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mock_repository.after_train_calculation.return_value = mock_final_result
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# Create Training instance
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logger = MagicMock()
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notification_handler = MagicMock()
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training = Training(logger=logger, notification_handler=notification_handler)
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# Mock inherited methods
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training.info = MagicMock()
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# Test data
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uploaded_file = BytesIO(b'test,data\n1,2\n3,4')
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train_params_dict = {
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'experiment_run_id': 123,
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'target_variable': 'price',
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'variable_columns': ['feature1', 'feature2'],
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'train_size': 80,
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'shuffle': True,
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'use_scaler': True,
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'include_ar': False,
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'bucket_name': 'test-bucket',
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'file_name': 'test.csv',
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'line_separator': '\n',
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'decimal_separator': '.',
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'lag_train': 1,
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'lag_val': 1,
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'rem_static_win': False,
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'low_lim': {'feature1': 0.0, 'feature2': 0.0},
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'upp_lim': {'feature1': 100.0, 'feature2': 100.0},
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'window': 10,
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'experiment_name': 'test_experiment',
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'experiment_description': 'Test experiment',
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'removed_intervals': [],
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}
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input_data = {
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'metadata': {'workflow_id': 'test-123'},
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'uploaded_file': uploaded_file,
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'train_params': train_params_dict,
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}
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# Execute
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result = await training.train_model(input_data)
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# Assertions
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assert result['success'] is True
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assert result['result'] == mock_final_result
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assert result['error_message'] is None
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# Verify repository calls
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mock_repository.train.assert_called_once()
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mock_repository.after_train_calculation.assert_called_once()
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@mark.asyncio
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@patch('model_manager.activities.training.TrainingRepository')
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async def test_train_model_invalid_file_type(mock_training_repository_class):
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"""Test training with invalid file type."""
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mock_repository = MagicMock()
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mock_training_repository_class.return_value = mock_repository
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logger = MagicMock()
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notification_handler = MagicMock()
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training = Training(logger=logger, notification_handler=notification_handler)
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# Invalid file type (string instead of BytesIO)
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input_data = {
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'metadata': {},
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'uploaded_file': 'not_a_bytesio',
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'train_params': {
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'experiment_run_id': 123,
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'target_variable': 'price',
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'variable_columns': ['feature1'],
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'train_size': 80,
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'shuffle': True,
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'use_scaler': False,
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'include_ar': False,
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'bucket_name': 'test',
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'file_name': 'test.csv',
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'line_separator': '\n',
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'decimal_separator': '.',
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'lag_train': 1,
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'lag_val': 1,
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'rem_static_win': False,
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'low_lim': {'feature1': 0.0},
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'upp_lim': {'feature1': 100.0},
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'window': 10,
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'experiment_name': 'test_experiment',
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'experiment_description': 'Test experiment',
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'removed_intervals': [],
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},
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}
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result = await training.train_model(input_data)
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assert result['success'] is False
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assert result['result'] is None
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assert 'uploaded_file must be BytesIO' in result['error_message']
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# Verify notification was sent (via BaseActivity)
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notification_handler.send_notification.assert_called_once()
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@mark.asyncio
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@patch('model_manager.activities.training.TrainingRepository')
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async def test_train_model_training_error(mock_training_repository_class):
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"""Test training failure during model training."""
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mock_repository = MagicMock()
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mock_training_repository_class.return_value = mock_repository
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# Setup repository to raise error
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mock_repository.train.side_effect = ValueError('Training data is empty')
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logger = MagicMock()
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notification_handler = MagicMock()
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training = Training(logger=logger, notification_handler=notification_handler)
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uploaded_file = BytesIO(b'test,data\n')
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input_data = {
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'metadata': {'workflow_id': 'test-456'},
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'uploaded_file': uploaded_file,
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'train_params': {
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'experiment_run_id': 456,
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'target_variable': 'price',
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'variable_columns': ['feature1'],
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'train_size': 80,
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'shuffle': True,
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'use_scaler': False,
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'include_ar': False,
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'bucket_name': 'test',
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'file_name': 'test.csv',
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'line_separator': '\n',
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'decimal_separator': '.',
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'lag_train': 1,
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'lag_val': 1,
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'rem_static_win': False,
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'low_lim': {'feature1': 0.0},
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'upp_lim': {'feature1': 100.0},
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'window': 10,
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'experiment_name': 'test_experiment',
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'experiment_description': 'Test experiment',
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'removed_intervals': [],
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},
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}
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result = await training.train_model(input_data)
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assert result['success'] is False
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assert result['result'] is None
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assert 'Training data is empty' in result['error_message']
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# Verify notification was sent (via BaseActivity)
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notification_handler.send_notification.assert_called_once()
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@mark.asyncio
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@patch('model_manager.activities.training.TrainingRepository')
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async def test_train_model_sends_notification_on_error(mock_training_repository_class):
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"""Test that notification is sent when training fails."""
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mock_repository = MagicMock()
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mock_training_repository_class.return_value = mock_repository
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mock_repository.train.side_effect = Exception('Database connection failed')
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logger = MagicMock()
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notification_handler = MagicMock()
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training = Training(logger=logger, notification_handler=notification_handler)
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uploaded_file = BytesIO(b'test,data\n1,2')
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input_data = {
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'metadata': {'workflow_id': 'test-789', 'experiment_run_id': 789},
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'uploaded_file': uploaded_file,
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'train_params': {
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'experiment_run_id': 789,
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'target_variable': 'price',
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'variable_columns': ['feature1'],
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'train_size': 80,
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'shuffle': True,
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'use_scaler': False,
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'include_ar': False,
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'bucket_name': 'test',
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'file_name': 'test.csv',
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'line_separator': '\n',
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'decimal_separator': '.',
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'lag_train': 1,
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'lag_val': 1,
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'rem_static_win': False,
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'low_lim': {'feature1': 0.0},
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'upp_lim': {'feature1': 100.0},
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'window': 10,
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'experiment_name': 'test_experiment',
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'experiment_description': 'Test experiment',
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'removed_intervals': [],
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},
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}
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result = await training.train_model(input_data)
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# Verify notification was sent (via BaseActivity)
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notification_handler.send_notification.assert_called_once()
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# Verify result - the important part is that error was caught and returned
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assert result['success'] is False
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assert result['result'] is None
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assert 'Database connection failed' in result['error_message']
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@mark.asyncio
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@patch('model_manager.activities.training.TrainingRepository')
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async def test_train_model_after_calculation_error(mock_training_repository_class):
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"""Test training failure during post-training calculations."""
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mock_repository = MagicMock()
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mock_training_repository_class.return_value = mock_repository
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# Train succeeds but after_calculation fails
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mock_train_result = MagicMock(spec=TrainModelResult)
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mock_repository.train.return_value = mock_train_result
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mock_repository.after_train_calculation.side_effect = Exception('Metric calculation failed')
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logger = MagicMock()
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notification_handler = MagicMock()
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training = Training(logger=logger, notification_handler=notification_handler)
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uploaded_file = BytesIO(b'test,data\n1,2\n3,4')
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input_data = {
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'metadata': {},
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'uploaded_file': uploaded_file,
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'train_params': {
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'experiment_run_id': 999,
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'target_variable': 'price',
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'variable_columns': ['feature1'],
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'train_size': 80,
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'shuffle': True,
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'use_scaler': False,
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'include_ar': False,
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'bucket_name': 'test',
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'file_name': 'test.csv',
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'line_separator': '\n',
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'decimal_separator': '.',
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'lag_train': 1,
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'lag_val': 1,
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'rem_static_win': False,
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'low_lim': {'feature1': 0.0},
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'upp_lim': {'feature1': 100.0},
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'window': 10,
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'experiment_name': 'test_experiment',
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'experiment_description': 'Test experiment',
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'removed_intervals': [],
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},
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}
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result = await training.train_model(input_data)
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assert result['success'] is False
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assert result['result'] is None
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assert 'Metric calculation failed' in result['error_message']
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# Verify notification was sent (via BaseActivity)
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notification_handler.send_notification.assert_called_once()
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