SIENTIAPDE-1243: Refactor and enhance model manager activities and workflows

This commit includes several changes:

- Reorganized imports and class inheritance in activities.py, gates.py and mlflow.py for better readability and maintainability.
- Improved error handling and logging in gates.py and mlflow.py.
- Added input validation and filtering in gates.py to ensure data quality.
- Enhanced prediction formatting and storage policy management in gates.py.
- Updated metrics.py to use consistent naming conventions and labels.
- Refactored connectors_config.py to use type hints and improve code clarity.
- Updated conditional and MLFlow filters for better data quality checks.
- Improved model repository logic for retraining and updating models.
- Enhanced worker.py to include SDK metrics and improved error handling.
- Refactored workflows for better modularity and error handling.
- Updated tests to reflect the changes and improve test coverage.

tests/laborious/activities/test_activities.py

@@ -1,16 +1,17 @@
+from unittest.mock import ANY, MagicMock, patch
+
 from pytest import mark
-from unittest.mock import patch, MagicMock, ANY
 from sientia_do.temporal.activities.postgres import Postgres
+
 from model_manager.activities.activities import Activities
-from model_manager.activities.mlflow import MLFlow
 from model_manager.activities.gates import Gates
+from model_manager.activities.mlflow import MLFlow
 
 
 @patch('model_manager.activities.activities.Postgres.__init__')
 @patch('model_manager.activities.activities.MLFlow.__init__')
 @patch('model_manager.activities.activities.Gates.__init__')
 def test___init__(mock_gates_init, mock_mlflow_init, mock_postgres_init):
-
     postgres_config = {
         'host': 'localhost',
         'port': 5432,
@@ -18,15 +19,10 @@ def test___init__(mock_gates_init, mock_mlflow_init, mock_postgres_init):
         'password': 'postgres',
         'dbname': 'postgres',
         'min_connections': 1,
-        'max_connections': 10
+        'max_connections': 10,
     }
 
-    mlflow_config = {
-        'host': 'localhost',
-        'port': 5000,
-        'username': 'mlflow',
-        'password': 'mlflow'
-    }
+    mlflow_config = {'host': 'localhost', 'port': 5000, 'username': 'mlflow', 'password': 'mlflow'}
 
     logger = MagicMock()
     notification_handler = MagicMock()
@@ -35,7 +31,7 @@ def test___init__(mock_gates_init, mock_mlflow_init, mock_postgres_init):
         postgres_config=postgres_config,
         mlflow_config=mlflow_config,
         logger=logger,
-        notification_handler=notification_handler
+        notification_handler=notification_handler,
     )
 
     assert isinstance(activities, Activities)
@@ -53,7 +49,7 @@ def test___init__(mock_gates_init, mock_mlflow_init, mock_postgres_init):
         min_connections=postgres_config['min_connections'],
         max_connections=postgres_config['max_connections'],
         logger=logger,
-        notification_handler=notification_handler
+        notification_handler=notification_handler,
     )
 
     mock_mlflow_init.assert_called_once_with(
@@ -63,13 +59,11 @@ def test___init__(mock_gates_init, mock_mlflow_init, mock_postgres_init):
         mlflow_username=mlflow_config['username'],
         mlflow_password=mlflow_config['password'],
         logger=logger,
-        notification_handler=notification_handler
+        notification_handler=notification_handler,
     )
 
     mock_gates_init.assert_called_once_with(
-        ANY,
-        logger=logger,
-        notification_handler=notification_handler
+        ANY, logger=logger, notification_handler=notification_handler
     )
 
 
@@ -84,15 +78,10 @@ async def test_shutdown(_mock_mlflow_init, mock_postgres_init):
         'password': 'postgres',
         'dbname': 'postgres',
         'min_connections': 1,
-        'max_connections': 10
+        'max_connections': 10,
     }
 
-    mlflow_config = {
-        'host': 'localhost',
-        'port': 5000,
-        'username': 'mlflow',
-        'password': 'mlflow'
-    }
+    mlflow_config = {'host': 'localhost', 'port': 5000, 'username': 'mlflow', 'password': 'mlflow'}
 
     logger = MagicMock()
     notification_handler = MagicMock()
@@ -101,7 +90,7 @@ async def test_shutdown(_mock_mlflow_init, mock_postgres_init):
         postgres_config=postgres_config,
         mlflow_config=mlflow_config,
         logger=logger,
-        notification_handler=notification_handler
+        notification_handler=notification_handler,
     )
 
     await activities.shutdown()

tests/laborious/activities/test_gates.py

@@ -1,6 +1,8 @@
-from unittest.mock import MagicMock, ANY, patch
+from unittest.mock import ANY, MagicMock, patch
+
 from pytest import fixture, mark
 from sientia_do.notifications.models import NotificationLevel
+
 from model_manager.activities.gates import Gates
 
 
@@ -20,11 +22,11 @@ def gates_activity():
 
 
 metadata = {
-    "metadata": {
-        "model_id": "test_model",
-        "model_name": "test_model",
-        "workflow_name": "test_workflow",
-        "schema_name": "test_schedule",
+    'metadata': {
+        'model_id': 'test_model',
+        'model_name': 'test_model',
+        'workflow_name': 'test_workflow',
+        'schema_name': 'test_schedule',
     },
 }
 
@@ -34,20 +36,18 @@ async def test_input_gate_invalid_filter(gates_activity):
     # Arrange
     input_data = {
         **metadata,
-        'filters': {
-            'INVALID_FILTER': {'POLICY': 'STOP'}
-        },
+        'filters': {'INVALID_FILTER': {'POLICY': 'STOP'}},
         'data': {'value': [1, 2, 3]},
-        'path_priority': ['STOP', 'CONTINUE', 'REPEAT']
+        'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
     }
 
     # Act
     result = await gates_activity.input_gate(input_data)
 
     # Assert
-    assert result == (None, 0, "")
+    assert result == (None, 0, '')
     gates_activity.error.assert_called_once_with(
-        "Filter INVALID_FILTER not found", metadata['metadata']
+        'Filter INVALID_FILTER not found', metadata['metadata']
     )
 
 
@@ -57,28 +57,27 @@ async def test_input_gate_filter_exception(mock_input_filter_functions, gates_ac
     # Arrange
     mock_input_filter_functions.__contains__.return_value = True
     mock_input_filter_functions.__getitem__.return_value = MagicMock(
-        side_effect=Exception("Test error"))
+        side_effect=Exception('Test error')
+    )
     input_data = {
         **metadata,
-        'filters': {
-            'EMPTY_DATA': {'policy': 'STOP', 'config': {}}
-        },
+        'filters': {'EMPTY_DATA': {'policy': 'STOP', 'config': {}}},
         'data': {'value': []},
-        'path_priority': ['STOP', 'CONTINUE', 'REPEAT']
+        'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
     }
 
     # Act
     result = await gates_activity.input_gate(input_data)
 
     # Assert
-    assert result == (None, 0, "")
+    assert result == (None, 0, '')
     gates_activity.send_notification.assert_called_once_with(
         metadata=metadata['metadata'],
-        notification_id="INTPUT_GATE_ERROR__EMPTY_DATA",
+        notification_id='INTPUT_GATE_ERROR__EMPTY_DATA',
         message="Error in filter EMPTY_DATA:{'policy': 'STOP', 'config': {}}: \n Test error",
-        block="input_gate",
+        block='input_gate',
         level=NotificationLevel.ERROR,
-        attachment_content=ANY
+        attachment_content=ANY,
     )
 
 
@@ -89,14 +88,14 @@ async def test_input_gate_no_filters(gates_activity):
         **metadata,
         'filters': {},
         'data': {'value': [1, 2, 3]},
-        'path_priority': ['CONTINUE', 'STOP', 'REPEAT']
+        'path_priority': ['CONTINUE', 'STOP', 'REPEAT'],
     }
 
     # Act
     result = await gates_activity.input_gate(input_data)
 
     # Assert
-    assert result == (None, 0, "")
+    assert result == (None, 0, '')
     gates_activity.debug.assert_called()
 
 
@@ -105,18 +104,16 @@ async def test_input_gate_with_filter(gates_activity):
     # Arrange
     input_data = {
         **metadata,
-        'filters': {
-            'EMPTY_DATA': {'policy': 'STOP', 'config': {}}
-        },
+        'filters': {'EMPTY_DATA': {'policy': 'STOP', 'config': {}}},
         'data': {'value': []},
-        'path_priority': ['STOP', 'CONTINUE', 'REPEAT']
+        'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
     }
 
     # Act
     result = await gates_activity.input_gate(input_data)
 
     # Assert
-    assert result == ('STOP', -1, "Input data with bad quality")
+    assert result == ('STOP', -1, 'Input data with bad quality')
     gates_activity.debug.assert_called()
 
 
@@ -125,51 +122,49 @@ async def test_mlflow_response_gate_invalid_filter(gates_activity):
     # Arrange
     input_data = {
         **metadata,
-        'filters': {
-            'INVALID_FILTER': {'POLICY': 'STOP'}
-        },
+        'filters': {'INVALID_FILTER': {'POLICY': 'STOP'}},
         'data': {'content': {'message': 'success'}},
         'type': 'test',
-        'path_priority': ['STOP', 'CONTINUE', 'REPEAT']
+        'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
     }
 
     # Act
     result = await gates_activity.mlflow_response_gate(input_data)
 
     # Assert
-    assert result == (None, 0, "")
+    assert result == (None, 0, '')
 
 
 @mark.asyncio
 @patch('model_manager.activities.gates.mlflow_response_filter_functions')
-async def test_mlflow_response_gate_filter_exception(mock_mlflow_response_filter_functions,
-                                                     gates_activity):
+async def test_mlflow_response_gate_filter_exception(
+    mock_mlflow_response_filter_functions, gates_activity
+):
     # Arrange
     mock_mlflow_response_filter_functions.__contains__.return_value = True
     mock_mlflow_response_filter_functions.__getitem__.return_value = MagicMock(
-        side_effect=Exception("Test error"))
+        side_effect=Exception('Test error')
+    )
     input_data = {
         **metadata,
-        'filters': {
-            'INVALID_FILTER': {'POLICY': 'STOP'}
-        },
+        'filters': {'INVALID_FILTER': {'POLICY': 'STOP'}},
         'data': {'content': {'message': 'success'}},
         'type': 'test',
-        'path_priority': ['STOP', 'CONTINUE', 'REPEAT']
+        'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
     }
 
     # Act
     result = await gates_activity.mlflow_response_gate(input_data)
 
     # Assert
-    assert result == (None, 0, "")
+    assert result == (None, 0, '')
     gates_activity.send_notification.assert_called_once_with(
         metadata=metadata['metadata'],
-        notification_id="MLFLOW_GATE_RESPONSE_FILTER__INVALID_FILTER",
+        notification_id='MLFLOW_GATE_RESPONSE_FILTER__INVALID_FILTER',
         message="Error in filter INVALID_FILTER:{'POLICY': 'STOP'}: \n Test error",
-        block="mlflow_gate",
+        block='mlflow_gate',
         level=NotificationLevel.ERROR,
-        attachment_content=ANY
+        attachment_content=ANY,
     )
 
 
@@ -181,14 +176,14 @@ async def test_mlflow_response_gate_no_filters(gates_activity):
         'filters': {},
         'data': {'content': {'message': 'success'}},
         'type': 'test',
-        'path_priority': ['CONTINUE', 'STOP', 'REPEAT']
+        'path_priority': ['CONTINUE', 'STOP', 'REPEAT'],
     }
 
     # Act
     result = await gates_activity.mlflow_response_gate(input_data)
 
     # Assert
-    assert result == (None, 0, "")
+    assert result == (None, 0, '')
     gates_activity.debug.assert_called()
 
 
@@ -197,25 +192,20 @@ async def test_mlflow_response_gate_with_filter(gates_activity):
     # Arrange
     input_data = {
         **metadata,
-        'filters': {
-            'API_ERROR': {'policy': 'STOP'}
-        },
+        'filters': {'API_ERROR': {'policy': 'STOP'}},
         'data': {
             'success': False,
-            'content': {
-                'message': 'API error occurred',
-                'traceback': 'error trace'
-            }
+            'content': {'message': 'API error occurred', 'traceback': 'error trace'},
         },
         'type': 'test',
-        'path_priority': ['STOP', 'CONTINUE', 'REPEAT']
+        'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
     }
 
     # Act
     result = await gates_activity.mlflow_response_gate(input_data)
 
     # Assert
-    assert result == ('STOP', -1, "API error occurred")
+    assert result == ('STOP', -1, 'API error occurred')
     gates_activity.debug.assert_called()
     gates_activity.send_notification.assert_called()
 
@@ -225,58 +215,53 @@ async def test_mlflow_content_gate_invalid_filter(gates_activity):
     # Arrange
     input_data = {
         **metadata,
-        'filters': {
-            'INVALID_FILTER': {'POLICY': 'STOP'}
-        },
+        'filters': {'INVALID_FILTER': {'POLICY': 'STOP'}},
         'data': {'value': [1, 2, 3]},
         'type': 'test',
-        'path_priority': ['STOP', 'CONTINUE', 'REPEAT']
+        'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
     }
 
     # Act
     result = await gates_activity.mlflow_content_gate(input_data)
 
     # Assert
-    assert result == (None, 0, "")
+    assert result == (None, 0, '')
 
 
 @mark.asyncio
 @patch('model_manager.activities.gates.mlflow_content_filter_functions')
-async def test_mlflow_content_gate_filter_exception(mock_mlflow_content_filter_functions,
-                                                    gates_activity):
+async def test_mlflow_content_gate_filter_exception(
+    mock_mlflow_content_filter_functions, gates_activity
+):
     # Arrange
     mock_mlflow_content_filter_functions.__contains__.return_value = True
     mock_mlflow_content_filter_functions.__getitem__.return_value = MagicMock(
-        side_effect=Exception("Test error"))
+        side_effect=Exception('Test error')
+    )
     input_data = {
         **metadata,
-        'filters': {
-            'API_ERROR': {'POLICY': 'STOP'}
-        },
+        'filters': {'API_ERROR': {'POLICY': 'STOP'}},
         'data': {
             'success': False,
-            'content': {
-                'message': 'API error occurred',
-                'traceback': 'error trace'
-            }
+            'content': {'message': 'API error occurred', 'traceback': 'error trace'},
         },
         'type': 'test',
-        'path_priority': ['STOP', 'CONTINUE', 'REPEAT']
+        'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
     }
 
     # Act
     result = await gates_activity.mlflow_content_gate(input_data)
 
     # Assert
-    assert result == (None, 0, "")
+    assert result == (None, 0, '')
     gates_activity.debug.assert_called()
     gates_activity.send_notification.assert_called_once_with(
         metadata=metadata['metadata'],
-        notification_id="MLFLOW_GATE_CONTENT_FILTER__API_ERROR",
+        notification_id='MLFLOW_GATE_CONTENT_FILTER__API_ERROR',
         message="Error in filter API_ERROR:{'POLICY': 'STOP'}: \n Test error",
-        block="mlflow_gate",
+        block='mlflow_gate',
         level=NotificationLevel.ERROR,
-        attachment_content=ANY
+        attachment_content=ANY,
     )
 
 
@@ -288,14 +273,14 @@ async def test_mlflow_content_gate_no_filters(gates_activity):
         'filters': {},
         'data': {'value': [1, 2, 3]},
         'type': 'test',
-        'path_priority': ['CONTINUE', 'STOP', 'REPEAT']
+        'path_priority': ['CONTINUE', 'STOP', 'REPEAT'],
     }
 
     # Act
     result = await gates_activity.mlflow_content_gate(input_data)
 
     # Assert
-    assert result == (None, 0, "")
+    assert result == (None, 0, '')
     gates_activity.debug.assert_called()
 
 
@@ -304,20 +289,17 @@ async def test_mlflow_content_gate_with_filter(gates_activity):
     # Arrange
     input_data = {
         **metadata,
-        'filters': {
-            'NAN_VALUES': {'policy': 'STOP', 'config': {}}
-        },
+        'filters': {'NAN_VALUES': {'policy': 'STOP', 'config': {}}},
         'data': {'value': [None, None, None]},
         'type': 'test',
-        'path_priority': ['STOP', 'CONTINUE', 'REPEAT']
+        'path_priority': ['STOP', 'CONTINUE', 'REPEAT'],
     }
 
     # Act
     result = await gates_activity.mlflow_content_gate(input_data)
 
     # Assert
-    assert result == (
-        'STOP', -1, "Transformed data not passed the content filter")
+    assert result == ('STOP', -1, 'Transformed data not passed the content filter')
     gates_activity.debug.assert_called()
     gates_activity.send_notification.assert_called()
 
@@ -328,7 +310,8 @@ def test_get_prediction_store_policy_invalid_policy(gates_activity):
 
     # Act
     policy_type, policy_value = gates_activity.get_prediction_store_policy(
-        prediction_store_policy, metadata)
+        prediction_store_policy, metadata
+    )
 
     # Assert
     assert policy_type == 'lts'
@@ -341,7 +324,8 @@ def test_get_prediction_store_policy_invalid_policy_value(gates_activity):
 
     # Act
     policy_type, policy_value = gates_activity.get_prediction_store_policy(
-        prediction_store_policy, metadata)
+        prediction_store_policy, metadata
+    )
 
     # Assert
     assert policy_type == 'lts'
@@ -354,7 +338,8 @@ def test_get_prediction_store_policy_valid_policy_type(gates_activity):
 
     # Act
     policy_type, policy_value = gates_activity.get_prediction_store_policy(
-        prediction_store_policy, metadata)
+        prediction_store_policy, metadata
+    )
 
     # Assert
     assert policy_type == 'lts'
@@ -367,7 +352,8 @@ def test_get_prediction_store_policy_valid_policy(gates_activity):
 
     # Act
     policy_type, policy_value = gates_activity.get_prediction_store_policy(
-        prediction_store_policy, metadata)
+        prediction_store_policy, metadata
+    )
 
     # Assert
     assert policy_type == 'erl'
@@ -380,16 +366,12 @@ async def test_format_prediction_no_timestamp(gates_activity):
     input_data = {
         **metadata,
         'data': {
-            'prediction': {
-                '2023-05-26 11:12:27': 1
-            },
-            'response_time': {
-                '2023-05-26 11:12:27': 0.1
-            }
+            'prediction': {'2023-05-26 11:12:27': 1},
+            'response_time': {'2023-05-26 11:12:27': 0.1},
         },
         'model_id': 'test_model',
         'prediction_confidence': 0.9,
-        'prediction_store_policy': 'lts:1'
+        'prediction_store_policy': 'lts:1',
     }
 
     # Act
@@ -402,7 +384,7 @@ async def test_format_prediction_no_timestamp(gates_activity):
     assert result['model_id'] == {0: 'test_model'}
     assert result['prediction_confidence'] == {0: 0.9}
     assert result['prediction_status'] == {0: 'Good'}
-    assert result['comments'] == {0: ""}
+    assert result['comments'] == {0: ''}
 
 
 @mark.asyncio
@@ -420,11 +402,11 @@ async def test_format_prediction_with_timestamp_erl(gates_activity):
                 '2023-05-26 11:12:27': 0.1,
                 '2023-05-26 11:12:28': 0.2,
                 '2023-05-26 11:12:29': 0.3,
-            }
+            },
         },
         'model_id': 'test_model',
         'prediction_confidence': 0.9,
-        'prediction_store_policy': 'erl:2'
+        'prediction_store_policy': 'erl:2',
     }
 
     # Act
@@ -433,12 +415,11 @@ async def test_format_prediction_with_timestamp_erl(gates_activity):
     # Assert
     assert result['prediction'] == {0: 2, 1: 1}
     assert result['response_time'] == {0: 0.2, 1: 0.1}
-    assert result['timestamp'] == {
-        0: '2023-05-26 11:12:28', 1: '2023-05-26 11:12:27'}
+    assert result['timestamp'] == {0: '2023-05-26 11:12:28', 1: '2023-05-26 11:12:27'}
     assert result['model_id'] == {0: 'test_model', 1: 'test_model'}
     assert result['prediction_confidence'] == {0: 0.9, 1: 0.9}
     assert result['prediction_status'] == {0: 'Good', 1: 'Good'}
-    assert result['comments'] == {0: "", 1: ""}
+    assert result['comments'] == {0: '', 1: ''}
 
 
 @mark.asyncio
@@ -456,11 +437,11 @@ async def test_format_prediction_with_timestamp_lts(gates_activity):
                 '2023-05-26 11:12:27': 0.1,
                 '2023-05-26 11:12:28': 0.2,
                 '2023-05-26 11:12:29': 0.3,
-            }
+            },
         },
         'model_id': 'test_model',
         'prediction_confidence': 0.9,
-        'prediction_store_policy': 'lts:2'
+        'prediction_store_policy': 'lts:2',
     }
 
     # Act
@@ -469,12 +450,11 @@ async def test_format_prediction_with_timestamp_lts(gates_activity):
     # Assert
     assert result['prediction'] == {0: 3, 1: 2}
     assert result['response_time'] == {0: 0.3, 1: 0.2}
-    assert result['timestamp'] == {
-        0: '2023-05-26 11:12:29', 1: '2023-05-26 11:12:28'}
+    assert result['timestamp'] == {0: '2023-05-26 11:12:29', 1: '2023-05-26 11:12:28'}
     assert result['model_id'] == {0: 'test_model', 1: 'test_model'}
     assert result['prediction_confidence'] == {0: 0.9, 1: 0.9}
     assert result['prediction_status'] == {0: 'Good', 1: 'Good'}
-    assert result['comments'] == {0: "", 1: ""}
+    assert result['comments'] == {0: '', 1: ''}
 
 
 @mark.asyncio
@@ -482,22 +462,23 @@ async def test_format_prediction_with_timestamp_invalid_policy(gates_activity):
     # Arrange
     input_data = {
         **metadata,
-        'data': {'prediction': [1, 2, 3],
-                 'response_time': [0.1, 0.2, 0.3],
-                 'timestamp': ['2023-05-26 11:12:27', '2023-05-26 11:12:28', '2023-05-26 11:12:29']},
+        'data': {
+            'prediction': [1, 2, 3],
+            'response_time': [0.1, 0.2, 0.3],
+            'timestamp': ['2023-05-26 11:12:27', '2023-05-26 11:12:28', '2023-05-26 11:12:29'],
+        },
         'model_id': 'test_model',
         'prediction_confidence': 0.9,
-        'prediction_store_policy': 'lts:2'
+        'prediction_store_policy': 'lts:2',
     }
-    gates_activity.get_prediction_store_policy = MagicMock(
-        return_value=('invalid', 1))
+    gates_activity.get_prediction_store_policy = MagicMock(return_value=('invalid', 1))
 
     try:
-        result = await gates_activity.format_prediction(input_data)
+        await gates_activity.format_prediction(input_data)
     except ValueError as e:
-        assert str(e) == "Invalid policy type: invalid"
+        assert str(e) == 'Invalid policy type: invalid'
     else:
-        assert False, "Expected ValueError"
+        raise AssertionError('Expected ValueError')
 
 
 @mark.asyncio
@@ -508,7 +489,7 @@ async def test_format_default_prediction(gates_activity):
         'timestamp': '2023-05-26 11:12:27',
         'model_id': 'test_model',
         'prediction_confidence': 0.1,
-        'comment': 'Test comment'
+        'comment': 'Test comment',
     }
 
     # Act
@@ -528,12 +509,7 @@ async def test_format_default_prediction(gates_activity):
 @mark.asyncio
 async def test_get_last_timestamp_with_data(gates_activity):
     # Arrange
-    input_data = {
-        **metadata,
-        'data': {
-            'timestamp': ['2023-05-26 11:12:27', '2023-05-26 11:12:28']
-        }
-    }
+    input_data = {**metadata, 'data': {'timestamp': ['2023-05-26 11:12:27', '2023-05-26 11:12:28']}}
 
     # Act
     result = await gates_activity.get_last_timestamp(input_data)
@@ -545,10 +521,7 @@ async def test_get_last_timestamp_with_data(gates_activity):
 @mark.asyncio
 async def test_get_last_timestamp_no_data(gates_activity):
     # Arrange
-    input_data = {
-        'data': {},
-        **metadata
-    }
+    input_data = {'data': {}, **metadata}
 
     # Act
     result = await gates_activity.get_last_timestamp(input_data)
@@ -567,30 +540,28 @@ async def test_write_metrics(mock_metrics, gates_activity):
         'prediction': {
             'prediction': [1, 2, 3],
             'prediction_confidence': [0.9, 0.8, 0.7],
-            'response_time': [0.1, 0.2, 0.3]
-        }
+            'response_time': [0.1, 0.2, 0.3],
+        },
     }
     await gates_activity.write_metrics(input_data)
     mock_metrics.PREDICTIONS_WRITTEN_COUNT.labels.assert_called_once_with(
         pod_id=gates_activity.pod_id,
         model_name=metadata['metadata']['model_name'],
-        pipeline_name=metadata['metadata']['workflow_name']
+        pipeline_name=metadata['metadata']['workflow_name'],
     )
     mock_metrics.PREDICTIONS_WRITTEN_COUNT.labels.return_value.inc.assert_called_once_with()
 
     mock_metrics.PREDICTION_CONFIDENCE_MONITOR.labels.assert_called_once_with(
         pod_id=gates_activity.pod_id,
         model_name=metadata['metadata']['model_name'],
-        pipeline_name=metadata['metadata']['workflow_name']
-    )
-    mock_metrics.PREDICTION_CONFIDENCE_MONITOR.labels.return_value.set.assert_called_once_with(
-        0.9
+        pipeline_name=metadata['metadata']['workflow_name'],
     )
+    mock_metrics.PREDICTION_CONFIDENCE_MONITOR.labels.return_value.set.assert_called_once_with(0.9)
 
     mock_metrics.PREDICTION_RESPONSE_TIME_MONITOR.labels.assert_called_once_with(
         pod_id=gates_activity.pod_id,
         model_name=metadata['metadata']['model_name'],
-        pipeline_name=metadata['metadata']['workflow_name']
+        pipeline_name=metadata['metadata']['workflow_name'],
     )
     mock_metrics.PREDICTION_RESPONSE_TIME_MONITOR.labels.return_value.observe.assert_called_once_with(
         0.1

tests/laborious/activities/test_mlflow.py

@@ -1,45 +1,42 @@
-from datetime import datetime
 from unittest.mock import ANY, MagicMock, patch
 
 import numpy as np
-from pandas import DataFrame, Timestamp
-from pytest import fixture, mark, raises
-from sientia_do.temporal.constants import DATETIME_FORMAT, DATETIME_FORMAT_WITH_TZ
-from model_manager.activities.mlflow import MLFlow
+from pytest import fixture, mark
 from sientia_do.notifications.models import NotificationLevel
+from sientia_do.temporal.constants import DATETIME_FORMAT, DATETIME_FORMAT_WITH_TZ
+
+from model_manager.activities.mlflow import MLFlow
 
 
-@patch("model_manager.activities.mlflow.MLFlowRepository")
+@patch('model_manager.activities.mlflow.MLFlowRepository')
 def test___init__(mock_mlflow_repository):
     mlflow = MLFlow(
-        mlflow_host="http://localhost",
+        mlflow_host='http://localhost',
         mlflow_port=5000,
-        mlflow_username="admin",
-        mlflow_password="admin",
+        mlflow_username='admin',
+        mlflow_password='admin',
         logger=MagicMock(),
-        notification_handler=MagicMock()
+        notification_handler=MagicMock(),
     )
 
-    assert mlflow.mlflow_host == "http://localhost"
+    assert mlflow.mlflow_host == 'http://localhost'
     assert mlflow.mlflow_port == 5000
-    assert mlflow.mlflow_username == "admin"
-    assert mlflow.mlflow_password == "admin"
+    assert mlflow.mlflow_username == 'admin'
+    assert mlflow.mlflow_password == 'admin'
 
-    mock_mlflow_repository.assert_called_once_with(
-        "http://localhost:5000", "admin", "admin", ANY
-    )
+    mock_mlflow_repository.assert_called_once_with('http://localhost:5000', 'admin', 'admin', ANY)
 
 
 @fixture
-@patch("model_manager.activities.mlflow.MLFlowRepository")
+@patch('model_manager.activities.mlflow.MLFlowRepository')
 def mlflow(mock_mlflow_repository):
     mlflow = MLFlow(
-        mlflow_host="http://localhost:5000",
+        mlflow_host='http://localhost:5000',
         mlflow_port=5000,
-        mlflow_username="admin",
-        mlflow_password="admin",
+        mlflow_username='admin',
+        mlflow_password='admin',
         logger=MagicMock(),
-        notification_handler=MagicMock()
+        notification_handler=MagicMock(),
     )
 
     mlflow.send_notification = MagicMock()
@@ -48,44 +45,67 @@ def mlflow(mock_mlflow_repository):
 
 
 metadata = {
-    "metadata": {
-        "model_id": "test_model",
-        "model_name": "test_model",
-        "workflow_name": "test_workflow",
-        "schema_name": "test_schedule",
+    'metadata': {
+        'model_id': 'test_model',
+        'model_name': 'test_model',
+        'workflow_name': 'test_workflow',
+        'schema_name': 'test_schedule',
     },
 }
 
 
 @mark.asyncio
-@patch("model_manager.activities.mlflow.DataFrame")
-@patch("model_manager.activities.mlflow.max")
+@patch('model_manager.activities.mlflow.DataFrame')
+@patch('model_manager.activities.mlflow.max')
 async def test_request_transform_success(mock_max, mock_dataframe, mlflow):
     mock_max.return_value = '2024-01-02'
     # Mock input data
     input_data = {
         **metadata,
         'data': [
-            {'timestamp': '2024-01-01', 'variable': 'var1',
-                'value': 1.0, 'created_at': '2024-01-01 12:00:00'},
-            {'timestamp': '2024-01-01', 'variable': 'var2',
-                'value': 2.0, 'created_at': '2024-01-01 12:00:00'},
-            {'timestamp': '2024-01-02', 'variable': 'var1',
-                'value': 3.0, 'created_at': '2024-01-02 12:00:00'},
-            {'timestamp': '2024-01-02', 'variable': 'var2',
-                'value': 4.0, 'created_at': '2024-01-02 12:00:00'},
-            {'timestamp': '2024-01-02', 'variable': 'var1',
-                'value': 1.0, 'created_at': '2024-01-01 12:00:00'},
-            {'timestamp': '2024-01-02', 'variable': 'var2',
-                'value': 1.0, 'created_at': '2024-01-01 12:00:00'}
+            {
+                'timestamp': '2024-01-01',
+                'variable': 'var1',
+                'value': 1.0,
+                'created_at': '2024-01-01 12:00:00',
+            },
+            {
+                'timestamp': '2024-01-01',
+                'variable': 'var2',
+                'value': 2.0,
+                'created_at': '2024-01-01 12:00:00',
+            },
+            {
+                'timestamp': '2024-01-02',
+                'variable': 'var1',
+                'value': 3.0,
+                'created_at': '2024-01-02 12:00:00',
+            },
+            {
+                'timestamp': '2024-01-02',
+                'variable': 'var2',
+                'value': 4.0,
+                'created_at': '2024-01-02 12:00:00',
+            },
+            {
+                'timestamp': '2024-01-02',
+                'variable': 'var1',
+                'value': 1.0,
+                'created_at': '2024-01-01 12:00:00',
+            },
+            {
+                'timestamp': '2024-01-02',
+                'variable': 'var2',
+                'value': 1.0,
+                'created_at': '2024-01-01 12:00:00',
+            },
         ],
         'model_name': 'test_model',
-        'model_config': {}
+        'model_config': {},
     }
 
     # Mock the transform response
-    expected_response = {'prediction': [0.5, 0.6], 'timestamp': [
-        '2024-01-01', '2024-01-02']}
+    expected_response = {'prediction': [0.5, 0.6], 'timestamp': ['2024-01-01', '2024-01-02']}
     mlflow.model_monitoring_repository.transform.return_value = expected_response
 
     mock_dataframe.return_value.sort_values.return_value = mock_dataframe.return_value
@@ -114,30 +134,25 @@ async def test_request_transform_success(mock_max, mock_dataframe, mlflow):
 
 
 @mark.asyncio
-@patch("model_manager.activities.mlflow.DataFrame")
-@patch("model_manager.activities.mlflow.to_datetime")
-@patch("model_manager.activities.mlflow.max")
+@patch('model_manager.activities.mlflow.DataFrame')
+@patch('model_manager.activities.mlflow.to_datetime')
+@patch('model_manager.activities.mlflow.max')
 async def test_request_predict(mock_max, mock_to_datetime, mock_dataframe, mlflow):
     mock_max.return_value = '2024-01-02'
     # Mock input data
     input_data = {
         **metadata,
         'data': {
-            "variable": {
-                "2024-01-01": "var1",
-                "2024-01-02": "var2",
-                "2024-01-03": "var1",
-                "2024-01-04": "var2"
+            'variable': {
+                '2024-01-01': 'var1',
+                '2024-01-02': 'var2',
+                '2024-01-03': 'var1',
+                '2024-01-04': 'var2',
             },
-            "value": {
-                "2024-01-01": 1.0,
-                "2024-01-02": 2.0,
-                "2024-01-03": 3.0,
-                "2024-01-04": 4.0
-            }
+            'value': {'2024-01-01': 1.0, '2024-01-02': 2.0, '2024-01-03': 3.0, '2024-01-04': 4.0},
         },
         'model_name': 'test_model',
-        'model_config': {}
+        'model_config': {},
     }
 
     # Mock the predict response
@@ -148,9 +163,7 @@ async def test_request_predict(mock_max, mock_to_datetime, mock_dataframe, mlflo
     response_data = await mlflow.request_predict(input_data)
 
     mock_dataframe.assert_called_once_with(input_data['data'])
-    mock_dataframe.return_value.replace.assert_called_once_with(
-        np.nan, None, inplace=True
-    )
+    mock_dataframe.return_value.replace.assert_called_once_with(np.nan, None, inplace=True)
     mock_dataframe.return_value.__setitem__.assert_any_call(
         'timestamp', mock_to_datetime.return_value.dt.strftime.return_value
     )
@@ -158,9 +171,7 @@ async def test_request_predict(mock_max, mock_to_datetime, mock_dataframe, mlflo
     mock_to_datetime.assert_called_once_with(
         mock_dataframe.return_value.__getitem__.return_value, format=DATETIME_FORMAT_WITH_TZ
     )
-    mock_to_datetime.return_value.dt.strftime.assert_called_once_with(
-        DATETIME_FORMAT
-    )
+    mock_to_datetime.return_value.dt.strftime.assert_called_once_with(DATETIME_FORMAT)
 
     # Verify the response
     assert response_data == expected_response
@@ -174,28 +185,26 @@ async def test_request_predict(mock_max, mock_to_datetime, mock_dataframe, mlflo
 @mark.asyncio
 async def test_retrain_model(mlflow):
     data = {
-        "model_id": [4, 5, 6, 7],
-        "created_at": [1, 2, 3, 4],
-        "timestamp": [1, 1, 2, 2],
-        "variable": ["var1", "var2", "var1", "var2"],
-        "value": [1, 2, 3, 4]
+        'model_id': [4, 5, 6, 7],
+        'created_at': [1, 2, 3, 4],
+        'timestamp': [1, 1, 2, 2],
+        'variable': ['var1', 'var2', 'var1', 'var2'],
+        'value': [1, 2, 3, 4],
     }
 
     mlflow.model_monitoring_repository.retrain_model.return_value = (
-        'Model retrained successfully', 'test')
+        'Model retrained successfully',
+        'test',
+    )
 
-    response = await mlflow.retrain_model({
-        **metadata,
-        'data': data,
-        'model_name': 'test_model'
-    })
+    response = await mlflow.retrain_model({**metadata, 'data': data, 'model_name': 'test_model'})
 
     mlflow.model_monitoring_repository.retrain_model.assert_called_once()
 
     assert response == {
-        "status": 'Model retrained successfully',
-        "timestamp": 2,
-        "experiment": 'test'
+        'status': 'Model retrained successfully',
+        'timestamp': 2,
+        'experiment': 'test',
     }
 
 
@@ -206,20 +215,16 @@ async def test_retrain_model_error(mlflow):
     )
 
     data = {
-        "model_id": [4, 5, 6, 7],
-        "created_at": [1, 2, 3, 4],
-        "timestamp": [1, 1, 2, 2],
-        "variable": ["var1", "var2", "var1", "var2"],
-        "value": [1, 2, 3, 4]
+        'model_id': [4, 5, 6, 7],
+        'created_at': [1, 2, 3, 4],
+        'timestamp': [1, 1, 2, 2],
+        'variable': ['var1', 'var2', 'var1', 'var2'],
+        'value': [1, 2, 3, 4],
     }
 
     try:
-        await mlflow.retrain_model({
-            **metadata,
-            'data': data,
-            'model_name': 'test_model'
-        })
-    except Exception as e:
+        await mlflow.retrain_model({**metadata, 'data': data, 'model_name': 'test_model'})
+    except Exception as e:  # noqa: BLE001
         assert str(e) == 'Error retraining model'
         mlflow.send_notification.assert_called_once_with(
             metadata=metadata['metadata'],
@@ -227,20 +232,18 @@ async def test_retrain_model_error(mlflow):
             message='Error retraining model test_model: Error retraining model',
             block='retrain_model',
             level=NotificationLevel.ERROR,
-            attachment_content=ANY
+            attachment_content=ANY,
         )
     else:
-        assert False, "No exception raised"
+        raise AssertionError('No exception raised')
 
 
 @mark.asyncio
 async def test_update_production_model(mlflow):
-    mlflow.model_monitoring_repository.update_production_model.return_value = (
-        {
-            "data1": 1,
-            "data2": 2
-        }
-    )
+    mlflow.model_monitoring_repository.update_production_model.return_value = {
+        'data1': 1,
+        'data2': 2,
+    }
 
     input_data = {
         **metadata,
@@ -248,13 +251,14 @@ async def test_update_production_model(mlflow):
         'model_id': 1,
         'experiment': 'test',
         'timestamp': 2,
-        'status': 'success'
+        'status': 'success',
     }
 
     response = await mlflow.update_production_model(input_data)
 
     mlflow.model_monitoring_repository.update_production_model.assert_called_once_with(
-        experiment='test', model_name='test_model')
+        experiment='test', model_name='test_model'
+    )
 
     assert response == {
         'data1': {0: 1},
@@ -262,7 +266,7 @@ async def test_update_production_model(mlflow):
         'model_id': {0: 1},
         'model_name': {0: 'test_model'},
         'timestamp': {0: 2},
-        'status': {0: 'success'}
+        'status': {0: 'success'},
     }
 
 
@@ -278,12 +282,12 @@ async def test_update_production_model_error(mlflow):
         'model_id': 1,
         'experiment': 'test',
         'timestamp': 2,
-        'status': 'success'
+        'status': 'success',
     }
 
     try:
         await mlflow.update_production_model(input_data)
-    except Exception as e:
+    except Exception as e:  # noqa: BLE001
         assert str(e) == 'Error updating production model'
         mlflow.send_notification.assert_called_once_with(
             metadata=metadata['metadata'],
@@ -291,7 +295,7 @@ async def test_update_production_model_error(mlflow):
             message='Error updating production model test_model: Error updating production model',
             block='update_production_model',
             level=NotificationLevel.ERROR,
-            attachment_content=ANY
+            attachment_content=ANY,
         )
     else:
-        assert False, "No exception raised"
+        raise AssertionError('No exception raised')

tests/laborious/utils/filters/test_conditional_filters.py

@@ -1,23 +1,29 @@
 from pandas import DataFrame
 
 from model_manager.utils.filters.conditional_filters import (
+    filter_empty_data,
     filter_specific_variables_null_values,
-    filter_empty_data
 )
 
 
 def test_filter_specific_variables_null_values():
-    assert filter_specific_variables_null_values(
-        DataFrame(
-            {'variable': ['variable1', 'variable2'], 'value': [1, 2]}),
-        config={'variables': ['variable2']}) is False
+    assert (
+        filter_specific_variables_null_values(
+            DataFrame({'variable': ['variable1', 'variable2'], 'value': [1, 2]}),
+            config={'variables': ['variable2']},
+        )
+        is False
+    )
 
 
 def test_filter_specific_variables_null_values_with_null_values():
-    assert filter_specific_variables_null_values(
-        DataFrame(
-            {'variable': ['variable1', 'variable2'], 'value': [1, None]}),
-        config={'variables': ['variable2']}) is True
+    assert (
+        filter_specific_variables_null_values(
+            DataFrame({'variable': ['variable1', 'variable2'], 'value': [1, None]}),
+            config={'variables': ['variable2']},
+        )
+        is True
+    )
 
 
 def test_filter_empty_data():
@@ -25,6 +31,7 @@ def test_filter_empty_data():
 
 
 def test_filter_empty_data_with_data():
-    assert filter_empty_data(
-        DataFrame({'variable': ['variable1', 'variable2'], 'value': [1, 2]}),
-        {}) is False
+    assert (
+        filter_empty_data(DataFrame({'variable': ['variable1', 'variable2'], 'value': [1, 2]}), {})
+        is False
+    )

tests/laborious/utils/filters/test_mlflow_filters.py

@@ -1,22 +1,23 @@
 from pandas import DataFrame
+
 from model_manager.utils.filters.mlflow_filters import api_error_filter, nan_values_filter
 
 
 def test_api_error_filter_invalid_response():
-    assert api_error_filter(None, {}) == True  # NOSONAR
+    assert api_error_filter(None, {})
 
 
 def test_api_error_filter_valid_response_fail():
-    assert api_error_filter({'success': False}, {}) == True
+    assert api_error_filter({'success': False}, {})
 
 
 def test_api_error_filter_valid_response_success():
-    assert api_error_filter({'success': True}, {}) == False
+    assert not api_error_filter({'success': True}, {})
 
 
 def test_nan_values_filter_all_nan_values():
-    assert nan_values_filter(DataFrame({'variable': [None, None]}), {}) == True
+    assert nan_values_filter(DataFrame({'variable': [None, None]}), {})
 
 
 def test_nan_values_filter_no_nan_values():
-    assert nan_values_filter(DataFrame({'variable': [1, 2]}), {}) == False
+    assert not nan_values_filter(DataFrame({'variable': [1, 2]}), {})

tests/laborious/utils/repository/test_model_repository.py

@@ -1,34 +1,33 @@
+from datetime import UTC, datetime
 from unittest.mock import ANY, MagicMock, call, patch
+
 import numpy as np
-from pandas import DataFrame
 import pytest
-from datetime import datetime, timezone
-from pandas import Timestamp
+from pandas import DataFrame, Timestamp
+
 from model_manager.utils.repository.model_repository import MLFlowRepository
 
 
 @pytest.fixture
 def mlflow_repository():
-    with patch('model_manager.utils.repository.model_repository.ModelServing',
-               autospec=True) as mock_model_serving:
+    with patch(
+        'model_manager.utils.repository.model_repository.ModelServing', autospec=True
+    ) as mock_model_serving:
         mock_instance = mock_model_serving.return_value
         mock_instance.get_transformed_data = MagicMock()
 
         repo = MLFlowRepository(
-            host='http://localhost:5000',
-            username='admin',
-            password='admin',
-            logger=MagicMock()
+            host='http://localhost:5000', username='admin', password='admin', logger=MagicMock()
         )
         return repo
 
 
 metadata = {
-    "metadata": {
-        "model_id": "test_model",
-        "model_name": "test_model",
-        "workflow_name": "test_workflow",
-        "schema_name": "test_schedule",
+    'metadata': {
+        'model_id': 'test_model',
+        'model_name': 'test_model',
+        'workflow_name': 'test_workflow',
+        'schema_name': 'test_schedule',
     },
 }
 
@@ -38,80 +37,56 @@ class Any:
 
 
 invalid_cases = [
-    (
-        {
-            'value': {
-                '2024-01-01 12:00:00': 1,
-                2024: 2
-            }
-        }
-    ),
-    (
-        {
-            'value': {
-                '2024-01-01': 1,
-                '2024-01-02': 2
-            }
-        }
-    ),
-    (
-        {
-            'value': {
-                Any(): 1,
-                Any(): 2
-            }
-        }
-    )
+    ({'value': {'2024-01-01 12:00:00': 1, 2024: 2}}),
+    ({'value': {'2024-01-01': 1, '2024-01-02': 2}}),
+    ({'value': {Any(): 1, Any(): 2}}),
 ]
 
 
-@pytest.mark.parametrize("data", invalid_cases)
+@pytest.mark.parametrize('data', invalid_cases)
 def test_detect_and_parse_datetime_index_error_cases(mlflow_repository, data):
-    input_data = DataFrame(
-        data
-    )
+    input_data = DataFrame(data)
 
     with pytest.raises(ValueError) as e:
-        mlflow_repository.detect_and_parse_datetime_index(
-            input_data, metadata['metadata'])
+        mlflow_repository.detect_and_parse_datetime_index(input_data, metadata['metadata'])
 
-        assert str(e) == "Index must be all timestamp like column. Valid formats are: pandas Timestamp, datetime, string in format %Y-%m-%d %H:%M:%S"
+        assert (
+            str(e)
+            == 'Index must be all timestamp like column. Valid formats are: pandas Timestamp, datetime, string in format %Y-%m-%d %H:%M:%S'
+        )
 
 
 valid_cases = [
     (
-        {
-            'value': {
-                '2024-01-01 12:00:00+0000': 1,
-                '2024-01-02 12:00:00+0000': 2
-            }
-        }, ['2024-01-01 12:00:00+0000', '2024-01-02 12:00:00+0000']
+        {'value': {'2024-01-01 12:00:00+0000': 1, '2024-01-02 12:00:00+0000': 2}},
+        ['2024-01-01 12:00:00+0000', '2024-01-02 12:00:00+0000'],
     ),
     (
         {
             'value': {
-                datetime(2025, 1, 1, 12, 0, 0, tzinfo=timezone.utc): 1,
-                datetime(2025, 1, 2, 12, 0, 0, tzinfo=timezone.utc): 2
+                datetime(2025, 1, 1, 12, 0, 0, tzinfo=UTC): 1,
+                datetime(2025, 1, 2, 12, 0, 0, tzinfo=UTC): 2,
             }
-        }, ['2025-01-01 12:00:00+0000', '2025-01-02 12:00:00+0000']
+        },
+        ['2025-01-01 12:00:00+0000', '2025-01-02 12:00:00+0000'],
     ),
     (
         {
             'value': {
-                Timestamp(2026, 1, 1, 12, 0, 0, tzinfo=timezone.utc): 1,
-                Timestamp(2026, 1, 2, 12, 0, 0, tzinfo=timezone.utc): 2
+                Timestamp(2026, 1, 1, 12, 0, 0, tzinfo=UTC): 1,
+                Timestamp(2026, 1, 2, 12, 0, 0, tzinfo=UTC): 2,
             }
-        }, ['2026-01-01 12:00:00+0000', '2026-01-02 12:00:00+0000']
+        },
+        ['2026-01-01 12:00:00+0000', '2026-01-02 12:00:00+0000'],
     ),
 ]
 
 
-@pytest.mark.parametrize("data,expected", valid_cases)
+@pytest.mark.parametrize('data,expected', valid_cases)
 def test_detect_and_parse_datetime_index_valid_format(mlflow_repository, data, expected):
     input_data = DataFrame(data)
 
-    response = mlflow_repository.detect_and_parse_datetime_index(
-        input_data, metadata['metadata'])
+    response = mlflow_repository.detect_and_parse_datetime_index(input_data, metadata['metadata'])
 
     assert response.index.tolist() == expected
 
@@ -122,18 +97,19 @@ def test_transform_success(mlflow_repository):
 
     mlflow_repository.detect_and_parse_datetime_index = MagicMock()
 
-    output = mlflow_repository.transform(
-        model_name, data, {}, metadata['metadata'])
+    output = mlflow_repository.transform(model_name, data, {}, metadata['metadata'])
 
     mlflow_repository.model_serving.get_cached_transform.assert_called_once_with(
-        model_name, data, 0, 'sklearn', False, 'model', 'predict')
+        model_name, data, 0, 'sklearn', False, 'model', 'predict'
+    )
 
     mlflow_repository.detect_and_parse_datetime_index.assert_called_once_with(
-        mlflow_repository.model_serving.get_cached_transform.return_value, metadata['metadata'])
+        mlflow_repository.model_serving.get_cached_transform.return_value, metadata['metadata']
+    )
 
     assert output == {
         'success': True,
-        'content': mlflow_repository.detect_and_parse_datetime_index.return_value.to_dict.return_value
+        'content': mlflow_repository.detect_and_parse_datetime_index.return_value.to_dict.return_value,
     }
 
 
@@ -141,80 +117,48 @@ def test_transform_error(mlflow_repository):
     data = MagicMock()
     model_name = 'model'
 
-    mlflow_repository.model_serving.get_cached_transform.side_effect = Exception(
-        'error')
+    mlflow_repository.model_serving.get_cached_transform.side_effect = Exception('error')
 
-    output = mlflow_repository.transform(
-        model_name, data, {}, metadata['metadata'])
+    output = mlflow_repository.transform(model_name, data, {}, metadata['metadata'])
 
     mlflow_repository.model_serving.get_cached_transform.assert_called_once_with(
-        model_name, data, 0, 'sklearn', False, 'model', 'predict')
+        model_name, data, 0, 'sklearn', False, 'model', 'predict'
+    )
 
-    assert output == {
-        'success': False,
-        'content': {
-            'message': 'error',
-            'traceback': ANY
-        }
-    }
+    assert output == {'success': False, 'content': {'message': 'error', 'traceback': ANY}}
 
 
 def test_predict_success(mlflow_repository):
-    data = DataFrame({
-        'feat_1': {
-            'index_1': 2,
-            'index_2': 3
-        }
-    })
+    data = DataFrame({'feat_1': {'index_1': 2, 'index_2': 3}})
     model_name = 'model'
-    mlflow_repository.model_serving.get_cached_predict.return_value = np.array(
-        [2, 3]
-    )
+    mlflow_repository.model_serving.get_cached_predict.return_value = np.array([2, 3])
 
-    output = mlflow_repository.predict(
-        model_name, data, {}, metadata['metadata'])
+    output = mlflow_repository.predict(model_name, data, {}, metadata['metadata'])
 
     mlflow_repository.model_serving.get_cached_predict.assert_called_once_with(
-        model_name, data, 0, 'pyfunc', False, 'model')
+        model_name, data, 0, 'pyfunc', False, 'model'
+    )
 
     assert output['success'] is True
     assert output['content'] == {
-        'prediction': {
-            'index_1': 2,
-            'index_2': 3
-        }, 'response_time': {
-            'index_1': ANY,
-            'index_2': ANY
-        }
+        'prediction': {'index_1': 2, 'index_2': 3},
+        'response_time': {'index_1': ANY, 'index_2': ANY},
     }
 
 
 def test_predict_error(mlflow_repository):
-    data = DataFrame({
-        'feat_1': {
-            'index_1': 2,
-            'index_2': 3
-        }
-    })
+    data = DataFrame({'feat_1': {'index_1': 2, 'index_2': 3}})
     model_name = 'model'
 
-    mlflow_repository.model_serving.get_cached_predict = MagicMock(
-        side_effect=Exception('error')
-    )
+    mlflow_repository.model_serving.get_cached_predict = MagicMock(side_effect=Exception('error'))
 
-    output = mlflow_repository.predict(
-        model_name, data, {}, metadata['metadata'])
+    output = mlflow_repository.predict(model_name, data, {}, metadata['metadata'])
 
     mlflow_repository.model_serving.get_cached_predict.assert_called_once_with(
-        model_name, data, 0, 'pyfunc', False, 'model')
+        model_name, data, 0, 'pyfunc', False, 'model'
+    )
 
-    assert output == {
-        'success': False,
-        'content': {
-            'message': 'error',
-            'traceback': ANY
-        }
-    }
+    assert output == {'success': False, 'content': {'message': 'error', 'traceback': ANY}}
 
 
 @patch('model_manager.utils.repository.model_repository.mlflow')
@@ -246,8 +190,7 @@ def test_get_next_run_name(mlflow, mlflow_repository):
 
 @patch('model_manager.utils.repository.model_repository.mlflow')
 def test_get_experiment_success(mlflow, mlflow_repository):
-    mlflow.get_experiment_by_name.return_value = MagicMock(
-        experiment_id='0')
+    mlflow.get_experiment_by_name.return_value = MagicMock(experiment_id='0')
 
     output = mlflow_repository.get_experiment('test')
 
@@ -263,23 +206,25 @@ def test_get_experiment_error(mlflow, mlflow_repository):
     except ValueError as e:
         assert str(e) == 'Experiment test not found'
     else:
-        assert False
+        raise AssertionError('Expected exception')
 
 
 @patch('model_manager.utils.repository.model_repository.mlflow')
 def test_get_experiment_last_run(mlflow, mlflow_repository):
-    mlflow.search_runs.return_value = DataFrame({
-        'params.retrain': ['True', 'False', 'True', 'False'],
-        'end_time': ['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04'],
-        'run_id': ['0', '1', '2', '3'],
-    })
+    mlflow.search_runs.return_value = DataFrame(
+        {
+            'params.retrain': ['True', 'False', 'True', 'False'],
+            'end_time': ['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04'],
+            'run_id': ['0', '1', '2', '3'],
+        }
+    )
 
     output = mlflow_repository.get_experiment_last_run(0)
 
     mlflow.search_runs.assert_called_once_with(
         experiment_ids=[0],
-        filter_string="",
-        output_format="pandas",
+        filter_string='',
+        output_format='pandas',
     )
 
     assert output == '2'
@@ -294,17 +239,14 @@ def test_get_experiment_last_run_error(mlflow, mlflow_repository):
     except ValueError as e:
         assert str(e) == 'Runs is not a pandas DataFrame'
     else:
-        assert False
+        raise AssertionError('Expected exception')
 
 
 @patch('model_manager.utils.repository.model_repository.mlflow.sklearn')
 @patch('model_manager.utils.repository.model_repository.mlflow.set_experiment')
 def test_create_model_experiment(set_experiment, sklearn, mlflow_repository):
-
-    mlflow_repository.model_serving.get_model_run_id = MagicMock(
-        return_value='0')
-    mlflow_repository.model_serving.get_model_uri = MagicMock(
-        return_value='test')
+    mlflow_repository.model_serving.get_model_run_id = MagicMock(return_value='0')
+    mlflow_repository.model_serving.get_model_uri = MagicMock(return_value='test')
     mlflow_repository.get_experiment_by_run_id = MagicMock()
 
     data_model_mock = MagicMock()
@@ -313,29 +255,30 @@ def test_create_model_experiment(set_experiment, sklearn, mlflow_repository):
     sklearn.load_model.side_effect = [data_model_mock, prediction_model_mock]
 
     data_model_mock.fit.return_value = data_model_mock
-    data_model_mock.predict.return_value = DataFrame({
-        'x': [10, 20, 30],
-    })
+    data_model_mock.predict.return_value = DataFrame(
+        {
+            'x': [10, 20, 30],
+        }
+    )
     data_model_mock.target_variable = 'y'
 
     prediction_model_mock.fit.return_value = prediction_model_mock
 
-    data = DataFrame({
-        'x': [1, 2, 3],
-        'y': [4, 5, 6]
-    })
+    data = DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
 
     output = mlflow_repository.create_model_experiment('test', data)
 
     mlflow_repository.model_serving.get_model_run_id.assert_called_once_with(
-        'test', stage='Production')
-    mlflow_repository.model_serving.get_model_uri.assert_called_once_with(
-        '0', prediction=False)
+        'test', stage='Production'
+    )
+    mlflow_repository.model_serving.get_model_uri.assert_called_once_with('0', prediction=False)
 
-    sklearn.load_model.assert_has_calls([
-        call(mlflow_repository.model_serving.get_model_uri.return_value),
-        call("models:/test/production"),
-    ])
+    sklearn.load_model.assert_has_calls(
+        [
+            call(mlflow_repository.model_serving.get_model_uri.return_value),
+            call('models:/test/production'),
+        ]
+    )
     assert sklearn.load_model.call_count == 2
 
     data_model_mock.fit.assert_called_once_with(data)
@@ -343,21 +286,23 @@ def test_create_model_experiment(set_experiment, sklearn, mlflow_repository):
 
     fit_args = prediction_model_mock.fit.call_args[0][0]
     assert fit_args.equals(
-        DataFrame({
-            'x': [10, 20, 30],
-            'y': [4, 5, 6],
-        })
+        DataFrame(
+            {
+                'x': [10, 20, 30],
+                'y': [4, 5, 6],
+            }
+        )
     )
 
     mlflow_repository.get_experiment_by_run_id.assert_called_once_with('0')
 
-    set_experiment.assert_called_once_with(
-        mlflow_repository.get_experiment_by_run_id.return_value
-    )
+    set_experiment.assert_called_once_with(mlflow_repository.get_experiment_by_run_id.return_value)
 
-    assert output == (prediction_model_mock,
-                      data_model_mock,
-                      mlflow_repository.get_experiment_by_run_id.return_value)
+    assert output == (
+        prediction_model_mock,
+        data_model_mock,
+        mlflow_repository.get_experiment_by_run_id.return_value,
+    )
 
 
 @patch('model_manager.utils.repository.model_repository.mlflow.start_run')
@@ -365,41 +310,43 @@ def test_create_model_experiment(set_experiment, sklearn, mlflow_repository):
 @patch('model_manager.utils.repository.model_repository.mlflow.sklearn.log_model')
 @patch('model_manager.utils.repository.model_repository.mlflow.log_artifact')
 def test_perform_model_retrain(log_artifact, log_model, log_param, start_run, mlflow_repository):
-
     prediction_model_mock = MagicMock()
     data_model_mock = MagicMock()
     experiment = 'test'
     model_name = 'test'
     data = MagicMock()
 
-    mlflow_repository.get_next_run_name = MagicMock(
-        return_value='test-1')
+    mlflow_repository.get_next_run_name = MagicMock(return_value='test-1')
     run = MagicMock()
     start_run.__enter__.return_value = run
 
     output = mlflow_repository.perform_model_retrain(
-        prediction_model_mock, data_model_mock, experiment, model_name, data)
+        prediction_model_mock, data_model_mock, experiment, model_name, data
+    )
 
     mlflow_repository.get_next_run_name.assert_called_once_with(experiment)
     start_run.assert_called_once_with(
-        run_name='test-1', description='Retrain model test with new data')
+        run_name='test-1', description='Retrain model test with new data'
+    )
 
-    log_model.assert_has_calls([
-        call(data_model_mock, "data_model"),
-        call(prediction_model_mock, "prediction_model"),
-    ])
+    log_model.assert_has_calls(
+        [
+            call(data_model_mock, 'data_model'),
+            call(prediction_model_mock, 'prediction_model'),
+        ]
+    )
 
-    data.to_csv.assert_called_once_with(
-        "temp/raw_data_test.csv", index=True)
+    data.to_csv.assert_called_once_with('temp/raw_data_test.csv', index=True)
 
-    log_artifact.assert_called_once_with(
-        "temp/raw_data_test.csv")
+    log_artifact.assert_called_once_with('temp/raw_data_test.csv')
 
-    log_param.assert_has_calls([
-        call("retrain", True),
-    ])
+    log_param.assert_has_calls(
+        [
+            call('retrain', True),
+        ]
+    )
 
-    assert output == ("Model retrained successfully", experiment)
+    assert output == ('Model retrained successfully', experiment)
 
 
 def test_retrain_model(mlflow_repository):
@@ -407,18 +354,18 @@ def test_retrain_model(mlflow_repository):
     model_name = 'test'
 
     mlflow_repository.create_model_experiment = MagicMock(
-        return_value=('data_model', 'prediction_model', '0'))
+        return_value=('data_model', 'prediction_model', '0')
+    )
 
-    mlflow_repository.perform_model_retrain = MagicMock(
-        return_value='Model retrained successfully')
+    mlflow_repository.perform_model_retrain = MagicMock(return_value='Model retrained successfully')
 
     output = mlflow_repository.retrain_model(data, model_name)
 
-    mlflow_repository.create_model_experiment.assert_called_once_with(
-        model_name, data)
+    mlflow_repository.create_model_experiment.assert_called_once_with(model_name, data)
 
     mlflow_repository.perform_model_retrain.assert_called_once_with(
-        'data_model', 'prediction_model', '0', model_name, data)
+        'data_model', 'prediction_model', '0', model_name, data
+    )
 
     assert output == 'Model retrained successfully'
 
@@ -438,7 +385,7 @@ def test_update_production_model_by_run_id(mlflow, mlflow_repository):
     output = mlflow_repository.update_production_model_by_run_id('0', 'test')
 
     mlflow.register_model.assert_called_once_with(
-        "runs:/0/prediction_model",
+        'runs:/0/prediction_model',
         'test',
     )
 
@@ -461,38 +408,34 @@ def test_update_production_model_by_run_id(mlflow, mlflow_repository):
 @patch('model_manager.utils.repository.model_repository.mlflow')
 def test_update_production_model_by_run_id_error(mlflow, mlflow_repository):
     mlflow.tracking.MlflowClient.return_value = MagicMock(
-        get_registered_model=MagicMock(
-            return_value=MagicMock(
-                latest_versions={}
-            )
-        )
+        get_registered_model=MagicMock(return_value=MagicMock(latest_versions={}))
     )
 
     try:
         mlflow_repository.update_production_model_by_run_id('0', 'test')
-    except Exception as e:
+    except Exception as e:  # noqa: BLE001
         assert str(e) == 'Model versions is not a list'
     else:
-        assert False
+        raise AssertionError('Expected exception')
 
 
 def test_update_production_model(mlflow_repository):
     connector = mlflow_repository
 
-    with patch.object(connector, 'get_experiment',
-                      return_value='0') as get_experiment:
-        with patch.object(connector, 'get_experiment_last_run',
-                          return_value='2') as get_experiment_last_run:
-            with patch.object(connector, 'update_production_model_by_run_id',
-                              return_value={'model_name': 'test', 'version': '3',
-                                            'mlflow_run_id': '0'}) as update_production_model_by_run_id:
-
+    with patch.object(connector, 'get_experiment', return_value='0') as get_experiment:
+        with patch.object(
+            connector, 'get_experiment_last_run', return_value='2'
+        ) as get_experiment_last_run:
+            with patch.object(
+                connector,
+                'update_production_model_by_run_id',
+                return_value={'model_name': 'test', 'version': '3', 'mlflow_run_id': '0'},
+            ) as update_production_model_by_run_id:
                 output = connector.update_production_model('0', 'test')
 
                 get_experiment.assert_called_once_with('0')
                 get_experiment_last_run.assert_called_once_with('0')
-                update_production_model_by_run_id.assert_called_once_with(
-                    '2', 'test')
+                update_production_model_by_run_id.assert_called_once_with('2', 'test')
 
                 assert output == {
                     'model_name': 'test',

tests/laborious/utils/test_connectors_config.py

@@ -1,7 +1,10 @@
 from os import environ
-from model_manager.utils.connectors_config import (build_mlflow_config,
-                                               build_postgres_config,
-                                               build_mongodb_config)
+
+from model_manager.utils.connectors_config import (
+    build_mlflow_config,
+    build_mongodb_config,
+    build_postgres_config,
+)
 
 
 def test_build_mlflow_config_with_env_vars():
@@ -95,7 +98,7 @@ def test_build_mongo_db_config_with_env_vars():
     assert build_mongodb_config() == {
         'connection_string': 'mongodb://sientia1:sientia1@localhost:27018',
         'database_name': 'test_db',
-        'ttl_index_seconds': 3600
+        'ttl_index_seconds': 3600,
     }
 
 
@@ -108,5 +111,5 @@ def test_build_mongo_db_config_with_defaults():
     assert build_mongodb_config() == {
         'connection_string': 'mongodb://root:wKZDbMNU1c@localhost:27018',
         'database_name': 'sientia',
-        'ttl_index_seconds': 3600
+        'ttl_index_seconds': 3600,
     }

tests/laborious/workflows/subworkflows/test_format_and_export_prediction.py

@@ -1,9 +1,12 @@
-from unittest.mock import call, patch, AsyncMock, ANY
-from pytest import mark, fixture
+from unittest.mock import ANY, AsyncMock, call, patch
+
+from pytest import fixture, mark
+from sientia_do.temporal.constants import DATETIME_FORMAT_WITH_TZ
 
 from model_manager.activities.activities import Activities
-from model_manager.workflows.sub_workflows.format_and_export_prediction import FormatAndExportPrediction
-from sientia_do.temporal.constants import DATETIME_FORMAT_WITH_TZ
+from model_manager.workflows.sub_workflows.format_and_export_prediction import (
+    FormatAndExportPrediction,
+)
 
 
 @fixture
@@ -12,119 +15,133 @@ def format_and_export_prediction():
 
 
 metadata = {
-    "metadata": {
-        "model_id": "test_model",
-        "model_name": "test_model",
-        "workflow_name": "test_workflow",
-        "schema_name": "test_schedule",
+    'metadata': {
+        'model_id': 'test_model',
+        'model_name': 'test_model',
+        'workflow_name': 'test_workflow',
+        'schema_name': 'test_schedule',
     },
 }
 
 
 @mark.asyncio
-@patch("model_manager.workflows.sub_workflows.format_and_export_prediction.workflow", new_callable=AsyncMock)
+@patch(
+    'model_manager.workflows.sub_workflows.format_and_export_prediction.workflow',
+    new_callable=AsyncMock,
+)
 async def test_run_none_path_flag(workflow_mock, format_and_export_prediction):
-
     input_data = {
         'metadata': metadata,
-        "path_flag": None,
-        "data": {"test": "data"},
-        "timestamp": "2021-01-01",
-        "model_id": 1,
-        "prediction_confidence": 0,
-        "schema": "test_schema",
-        "table_name": "test_table",
-        "prediction_store_policy": "erl:1"
+        'path_flag': None,
+        'data': {'test': 'data'},
+        'timestamp': '2021-01-01',
+        'model_id': 1,
+        'prediction_confidence': 0,
+        'schema': 'test_schema',
+        'table_name': 'test_table',
+        'prediction_store_policy': 'erl:1',
     }
 
     await format_and_export_prediction.run(input_data)
 
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(
-            Activities.format_prediction,
-            {
-                'data': input_data['data'],
-                'timestamp': input_data['timestamp'],
-                'model_id': input_data['model_id'],
-                'prediction_confidence': input_data['prediction_confidence'],
-                'prediction_store_policy': input_data['prediction_store_policy'],
-                **metadata
-            },
-            retry_policy=ANY,
-            start_to_close_timeout=ANY
-        )])
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.format_prediction,
+                {
+                    'data': input_data['data'],
+                    'timestamp': input_data['timestamp'],
+                    'model_id': input_data['model_id'],
+                    'prediction_confidence': input_data['prediction_confidence'],
+                    'prediction_store_policy': input_data['prediction_store_policy'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
 
-    workflow_mock.execute_activity_method.assert_has_calls([
-        call(
-            Activities.export_data_to_postgres,
-            {
-                'schema': input_data['schema'],
-                'table_name': input_data['table_name'],
-                'data': workflow_mock.execute_activity_method.return_value,
-                **metadata,
-                'timestamp_conversion': {
-                    'column': 'timestamp',
-                    'format': DATETIME_FORMAT_WITH_TZ
-                }
-            },
-            retry_policy=ANY,
-            start_to_close_timeout=ANY
-        )])
+    workflow_mock.execute_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.export_data_to_postgres,
+                {
+                    'schema': input_data['schema'],
+                    'table_name': input_data['table_name'],
+                    'data': workflow_mock.execute_activity_method.return_value,
+                    **metadata,
+                    'timestamp_conversion': {
+                        'column': 'timestamp',
+                        'format': DATETIME_FORMAT_WITH_TZ,
+                    },
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
 
     assert workflow_mock.execute_activity_method.call_count == 2
     assert workflow_mock.execute_local_activity_method.call_count == 1
 
 
 @mark.asyncio
-@patch("model_manager.workflows.sub_workflows.format_and_export_prediction.workflow", new_callable=AsyncMock)
+@patch(
+    'model_manager.workflows.sub_workflows.format_and_export_prediction.workflow',
+    new_callable=AsyncMock,
+)
 async def test_run_default_path_flag(workflow_mock, format_and_export_prediction):
-
     input_data = {
         'metadata': metadata,
-        "path_flag": "default",
-        "data": {"test": "data"},
-        "timestamp": "2021-01-01",
-        "model_id": 1,
-        "prediction_confidence": 0,
-        "schema": "test_schema",
-        "table_name": "test_table",
-        "comment": "test_comment"
+        'path_flag': 'default',
+        'data': {'test': 'data'},
+        'timestamp': '2021-01-01',
+        'model_id': 1,
+        'prediction_confidence': 0,
+        'schema': 'test_schema',
+        'table_name': 'test_table',
+        'comment': 'test_comment',
     }
 
     await format_and_export_prediction.run(input_data)
 
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(
-            Activities.format_default_prediction,
-            {
-                'timestamp': input_data['timestamp'],
-                'model_id': input_data['model_id'],
-                'prediction_confidence': input_data['prediction_confidence'],
-                'comment': input_data['comment'],
-                **metadata
-            },
-            retry_policy=ANY,
-            start_to_close_timeout=ANY
-        )
-    ])
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.format_default_prediction,
+                {
+                    'timestamp': input_data['timestamp'],
+                    'model_id': input_data['model_id'],
+                    'prediction_confidence': input_data['prediction_confidence'],
+                    'comment': input_data['comment'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
 
-    workflow_mock.execute_activity_method.assert_has_calls([
-        call(
-            Activities.export_data_to_postgres,
-            {
-                'schema': input_data['schema'],
-                'table_name': input_data['table_name'],
-                'data': workflow_mock.execute_activity_method.return_value,
-                **metadata,
-                'timestamp_conversion': {
-                    'column': 'timestamp',
-                    'format': DATETIME_FORMAT_WITH_TZ
-                }
-            },
-            retry_policy=ANY,
-            start_to_close_timeout=ANY
-        )
-    ])
+    workflow_mock.execute_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.export_data_to_postgres,
+                {
+                    'schema': input_data['schema'],
+                    'table_name': input_data['table_name'],
+                    'data': workflow_mock.execute_activity_method.return_value,
+                    **metadata,
+                    'timestamp_conversion': {
+                        'column': 'timestamp',
+                        'format': DATETIME_FORMAT_WITH_TZ,
+                    },
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
 
     assert workflow_mock.execute_activity_method.call_count == 3
     assert workflow_mock.execute_local_activity_method.call_count == 1

tests/laborious/workflows/subworkflows/test_prediction_process.py

@@ -1,5 +1,7 @@
-from unittest.mock import AsyncMock, patch, call, ANY
+from unittest.mock import ANY, AsyncMock, call, patch
+
 from pytest import fixture, mark
+
 from model_manager.activities.activities import Activities
 from model_manager.workflows.sub_workflows.prediction_process import PredictionProcess
 
@@ -10,17 +12,17 @@ def prediction_process():
 
 
 metadata = {
-    "metadata": {
-        "model_id": "test_model",
-        "model_name": "test_model",
-        "workflow_name": "test_workflow",
-        "schema_name": "test_schedule",
+    'metadata': {
+        'model_id': 'test_model',
+        'model_name': 'test_model',
+        'workflow_name': 'test_workflow',
+        'schema_name': 'test_schedule',
     },
 }
 
 
 @mark.asyncio
-@patch("model_manager.workflows.sub_workflows.prediction_process.workflow", new_callable=AsyncMock)
+@patch('model_manager.workflows.sub_workflows.prediction_process.workflow', new_callable=AsyncMock)
 async def test_run(workflow_mock, prediction_process):
     prediction_process.path_flag_handler = AsyncMock(return_value=False)
     # Arrange
@@ -34,26 +36,23 @@ async def test_run(workflow_mock, prediction_process):
         'mlflow_transform_filters': {'test': 'filter'},
         'mlflow_predict_filters': {'test': 'filter'},
         'model_name': 'test_model_name',
-        'model_config': {
-            'retention': '30'
-        },
+        'model_config': {'retention': '30'},
         'path_priority': ['continue', 'repeat', 'stop'],
-
-        'prediction_store_policy': 'lts:1'
+        'prediction_store_policy': 'lts:1',
     }
 
     # Mock the activity responses
     workflow_mock.execute_local_activity_method.side_effect = [
         '2024-01-01',  # get_last_timestamp
-        ('continue', 0.95, "Input data with bad quality"),  # input_gate
+        ('continue', 0.95, 'Input data with bad quality'),  # input_gate
         {'content': 'transformed_data', 'timestamp': '2024-01-01'},  # transform_data
         # mlflow_response_gate (transform)
-        ('continue', 0.95, "Error"),
+        ('continue', 0.95, 'Error'),
         # mlflow_content_gate (transform)
-        ('continue', 0.95, "Transformed data not passed the content filter"),
+        ('continue', 0.95, 'Transformed data not passed the content filter'),
         {'content': 'predicted_data', 'timestamp': '2024-01-01'},  # request_predict
         # mlflow_response_gate (predict)
-        ('continue', 0.95, "Error"),
+        ('continue', 0.95, 'Error'),
     ]
 
     # Act
@@ -62,57 +61,112 @@ async def test_run(workflow_mock, prediction_process):
     # Assert
     assert workflow_mock.execute_local_activity_method.call_count == 7
 
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.get_last_timestamp, {
-            **metadata,
-            'data': input_data['data'],
-        },
-            retry_policy=ANY, start_to_close_timeout=ANY)])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.input_gate, {
-            **metadata,
-            'filters': input_data['input_filters'],
-            'data': input_data['data'],
-            'path_priority': input_data['path_priority'],
-        }, retry_policy=ANY, start_to_close_timeout=ANY)])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.request_transform, {
-            **metadata,
-            'data': input_data['data'],
-            'model_name': input_data['model_name'],
-            'model_config': input_data['model_config'],
-        }, retry_policy=ANY, start_to_close_timeout=ANY)])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.mlflow_response_gate, {
-            **metadata,
-            'filters': input_data['mlflow_transform_filters'],
-            'data': {'content': 'transformed_data', 'timestamp': '2024-01-01'},
-            'type': 'transform',
-            'path_priority': input_data['path_priority'],
-        }, retry_policy=ANY, start_to_close_timeout=ANY)])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.mlflow_content_gate, {
-            **metadata,
-            'filters': input_data['mlflow_transform_filters'],
-            'data': 'transformed_data',
-            'type': 'transform',
-            'path_priority': input_data['path_priority'],
-        }, retry_policy=ANY, start_to_close_timeout=ANY)])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.request_predict, {
-            **metadata,
-            'data': 'transformed_data',
-            'model_name': input_data['model_name'],
-            'model_config': input_data['model_config'],
-        }, retry_policy=ANY, start_to_close_timeout=ANY)])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.mlflow_response_gate, {
-            **metadata,
-            'filters': input_data['mlflow_predict_filters'],
-            'data': {'content': 'predicted_data', 'timestamp': '2024-01-01'},
-            'type': 'predict',
-            'path_priority': input_data['path_priority'],
-        }, retry_policy=ANY, start_to_close_timeout=ANY)])
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.get_last_timestamp,
+                {
+                    **metadata,
+                    'data': input_data['data'],
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.input_gate,
+                {
+                    **metadata,
+                    'filters': input_data['input_filters'],
+                    'data': input_data['data'],
+                    'path_priority': input_data['path_priority'],
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.request_transform,
+                {
+                    **metadata,
+                    'data': input_data['data'],
+                    'model_name': input_data['model_name'],
+                    'model_config': input_data['model_config'],
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.mlflow_response_gate,
+                {
+                    **metadata,
+                    'filters': input_data['mlflow_transform_filters'],
+                    'data': {'content': 'transformed_data', 'timestamp': '2024-01-01'},
+                    'type': 'transform',
+                    'path_priority': input_data['path_priority'],
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.mlflow_content_gate,
+                {
+                    **metadata,
+                    'filters': input_data['mlflow_transform_filters'],
+                    'data': 'transformed_data',
+                    'type': 'transform',
+                    'path_priority': input_data['path_priority'],
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.request_predict,
+                {
+                    **metadata,
+                    'data': 'transformed_data',
+                    'model_name': input_data['model_name'],
+                    'model_config': input_data['model_config'],
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.mlflow_response_gate,
+                {
+                    **metadata,
+                    'filters': input_data['mlflow_predict_filters'],
+                    'data': {'content': 'predicted_data', 'timestamp': '2024-01-01'},
+                    'type': 'predict',
+                    'path_priority': input_data['path_priority'],
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
 
     workflow_mock.execute_child_workflow.assert_called_once_with(
         'format_and_export_prediction',
@@ -125,17 +179,16 @@ async def test_run(workflow_mock, prediction_process):
             'model_id': 1,
             'model_name': 'test_model_name',
             'model_config': input_data['model_config'],
-
             'schema': input_data['schema'],
             'table_name': input_data['table_name'],
             'comment': 'Error',
-            'prediction_store_policy': input_data['prediction_store_policy']
-        }
+            'prediction_store_policy': input_data['prediction_store_policy'],
+        },
     )
 
 
 @mark.asyncio
-@patch("model_manager.workflows.sub_workflows.prediction_process.workflow", new_callable=AsyncMock)
+@patch('model_manager.workflows.sub_workflows.prediction_process.workflow', new_callable=AsyncMock)
 async def test_run_stop_at_input_gate(workflow_mock, prediction_process):
     prediction_process.path_flag_handler = AsyncMock(return_value=True)
     # Arrange
@@ -149,17 +202,14 @@ async def test_run_stop_at_input_gate(workflow_mock, prediction_process):
         'mlflow_transform_filters': {'test': 'filter'},
         'mlflow_predict_filters': {'test': 'filter'},
         'model_name': 'test_model_name',
-        'model_config': {
-            'retention': '30'
-        },
+        'model_config': {'retention': '30'},
         'path_priority': ['continue', 'repeat', 'stop'],
-
     }
 
     # Mock the activity responses
     workflow_mock.execute_local_activity_method.side_effect = [
         '2024-01-01',  # get_last_timestamp
-        ('stop', 0.95, "Input data with bad quality"),  # input_gate
+        ('stop', 0.95, 'Input data with bad quality'),  # input_gate
     ]
 
     # Act
@@ -167,23 +217,35 @@ async def test_run_stop_at_input_gate(workflow_mock, prediction_process):
 
     # Assert
     assert workflow_mock.execute_local_activity_method.call_count == 2
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.get_last_timestamp, {
-             'data': input_data['data'],
-             **metadata,
-             }, retry_policy=ANY, start_to_close_timeout=ANY),
-        call(Activities.input_gate, {
-             'filters': input_data['input_filters'],
-             'data': input_data['data'],
-             'path_priority': input_data['path_priority'],
-             **metadata,
-             }, retry_policy=ANY, start_to_close_timeout=ANY)
-    ])
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.get_last_timestamp,
+                {
+                    'data': input_data['data'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            ),
+            call(
+                Activities.input_gate,
+                {
+                    'filters': input_data['input_filters'],
+                    'data': input_data['data'],
+                    'path_priority': input_data['path_priority'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            ),
+        ]
+    )
     workflow_mock.execute_child_workflow.assert_not_called()
 
 
 @mark.asyncio
-@patch("model_manager.workflows.sub_workflows.prediction_process.workflow", new_callable=AsyncMock)
+@patch('model_manager.workflows.sub_workflows.prediction_process.workflow', new_callable=AsyncMock)
 async def test_run_stop_at_first_mlflow_response_gate(workflow_mock, prediction_process):
     prediction_process.path_flag_handler = AsyncMock(side_effect=[False, True])
     # Arrange
@@ -197,19 +259,16 @@ async def test_run_stop_at_first_mlflow_response_gate(workflow_mock, prediction_
         'mlflow_transform_filters': {'test': 'filter'},
         'mlflow_predict_filters': {'test': 'filter'},
         'model_name': 'test_model_name',
-        'model_config': {
-            'retention': '30'
-        },
+        'model_config': {'retention': '30'},
         'path_priority': ['continue', 'repeat', 'stop'],
-
     }
 
     # Mock the activity responses
     workflow_mock.execute_local_activity_method.side_effect = [
         '2024-01-01',  # get_last_timestamp
-        ('repeat', 0.95, "Input data with bad quality"),  # input_gate
+        ('repeat', 0.95, 'Input data with bad quality'),  # input_gate
         {'content': 'transformed_data', 'timestamp': '2024-01-01'},  # transform_data
-        ('continue', 0.95, "Error"),  # mlflow_response_gate (transform)
+        ('continue', 0.95, 'Error'),  # mlflow_response_gate (transform)
     ]
 
     # Act
@@ -217,46 +276,72 @@ async def test_run_stop_at_first_mlflow_response_gate(workflow_mock, prediction_
 
     # Assert
     assert workflow_mock.execute_local_activity_method.call_count == 4
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.get_last_timestamp, {
-             'data': input_data['data'],
-             **metadata,
-             },
-             retry_policy=ANY, start_to_close_timeout=ANY)])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.input_gate, {
-             'filters': input_data['input_filters'],
-             'data': input_data['data'],
-             'path_priority': input_data['path_priority'],
-             **metadata,
-             },
-             retry_policy=ANY, start_to_close_timeout=ANY)])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.request_transform, {
-            'data': input_data['data'],
-            'model_name': input_data['model_name'],
-            'model_config': input_data['model_config'],
-            **metadata
-        },
-            retry_policy=ANY, start_to_close_timeout=ANY)
-    ])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.mlflow_response_gate, {
-            'filters': input_data['mlflow_transform_filters'],
-            'data': {'content': 'transformed_data', 'timestamp': '2024-01-01'},
-            'type': 'transform',
-            'path_priority': input_data['path_priority'],
-            **metadata
-        }, retry_policy=ANY, start_to_close_timeout=ANY)
-    ])
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.get_last_timestamp,
+                {
+                    'data': input_data['data'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.input_gate,
+                {
+                    'filters': input_data['input_filters'],
+                    'data': input_data['data'],
+                    'path_priority': input_data['path_priority'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.request_transform,
+                {
+                    'data': input_data['data'],
+                    'model_name': input_data['model_name'],
+                    'model_config': input_data['model_config'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.mlflow_response_gate,
+                {
+                    'filters': input_data['mlflow_transform_filters'],
+                    'data': {'content': 'transformed_data', 'timestamp': '2024-01-01'},
+                    'type': 'transform',
+                    'path_priority': input_data['path_priority'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
     workflow_mock.execute_child_workflow.assert_not_called()
 
 
 @mark.asyncio
-@patch("model_manager.workflows.sub_workflows.prediction_process.workflow", new_callable=AsyncMock)
+@patch('model_manager.workflows.sub_workflows.prediction_process.workflow', new_callable=AsyncMock)
 async def test_run_stop_at_mlflow_content_gate(workflow_mock, prediction_process):
-    prediction_process.path_flag_handler = AsyncMock(
-        side_effect=[False, False, True])
+    prediction_process.path_flag_handler = AsyncMock(side_effect=[False, False, True])
     # Arrange
     input_data = {
         'metadata': metadata,
@@ -268,22 +353,19 @@ async def test_run_stop_at_mlflow_content_gate(workflow_mock, prediction_process
         'mlflow_transform_filters': {'test': 'filter'},
         'mlflow_predict_filters': {'test': 'filter'},
         'model_name': 'test_model_name',
-        'model_config': {
-            'retention': '30'
-        },
+        'model_config': {'retention': '30'},
         'path_priority': ['continue', 'repeat', 'stop'],
-
     }
 
     # Mock the activity responses
     workflow_mock.execute_local_activity_method.side_effect = [
         '2024-01-01',  # get_last_timestamp
-        ('continue', 0.95, "Input data with bad quality"),  # input_gate
+        ('continue', 0.95, 'Input data with bad quality'),  # input_gate
         {'content': 'transformed_data', 'timestamp': '2024-01-01'},  # transform_data
         # mlflow_response_gate (transform)
-        ('continue', 0.95, "Error"),
+        ('continue', 0.95, 'Error'),
         # mlflow_content_gate (transform)
-        ('continue', 0.95, "Transformed data not passed the content filter"),
+        ('continue', 0.95, 'Transformed data not passed the content filter'),
     ]
 
     # Act
@@ -292,51 +374,88 @@ async def test_run_stop_at_mlflow_content_gate(workflow_mock, prediction_process
     # Assert
     assert workflow_mock.execute_local_activity_method.call_count == 5
 
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.get_last_timestamp, {
-             'data': input_data['data'],
-             **metadata,
-             },
-             retry_policy=ANY, start_to_close_timeout=ANY)])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.input_gate, {
-             'filters': input_data['input_filters'],
-             'data': input_data['data'],
-             'path_priority': input_data['path_priority'],
-             **metadata,
-             },
-             retry_policy=ANY, start_to_close_timeout=ANY)])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.request_transform, {
-            'data': input_data['data'],
-            'model_name': input_data['model_name'],
-            'model_config': input_data['model_config'],
-            **metadata
-        }, retry_policy=ANY, start_to_close_timeout=ANY)])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.mlflow_response_gate, {
-            'filters': input_data['mlflow_transform_filters'],
-            'data': {'content': 'transformed_data', 'timestamp': '2024-01-01'},
-            'type': 'transform',
-            'path_priority': input_data['path_priority'],
-            **metadata,
-        }, retry_policy=ANY, start_to_close_timeout=ANY)])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.mlflow_content_gate, {
-            'filters': input_data['mlflow_transform_filters'],
-            'data': 'transformed_data',
-            'type': 'transform',
-            'path_priority': input_data['path_priority'],
-            **metadata,
-        }, retry_policy=ANY, start_to_close_timeout=ANY)])
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.get_last_timestamp,
+                {
+                    'data': input_data['data'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.input_gate,
+                {
+                    'filters': input_data['input_filters'],
+                    'data': input_data['data'],
+                    'path_priority': input_data['path_priority'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.request_transform,
+                {
+                    'data': input_data['data'],
+                    'model_name': input_data['model_name'],
+                    'model_config': input_data['model_config'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.mlflow_response_gate,
+                {
+                    'filters': input_data['mlflow_transform_filters'],
+                    'data': {'content': 'transformed_data', 'timestamp': '2024-01-01'},
+                    'type': 'transform',
+                    'path_priority': input_data['path_priority'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.mlflow_content_gate,
+                {
+                    'filters': input_data['mlflow_transform_filters'],
+                    'data': 'transformed_data',
+                    'type': 'transform',
+                    'path_priority': input_data['path_priority'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
     workflow_mock.execute_child_workflow.assert_not_called()
 
 
 @mark.asyncio
-@patch("model_manager.workflows.sub_workflows.prediction_process.workflow", new_callable=AsyncMock)
+@patch('model_manager.workflows.sub_workflows.prediction_process.workflow', new_callable=AsyncMock)
 async def test_run_stop_at_mlflow_last_response_gate(workflow_mock, prediction_process):
-    prediction_process.path_flag_handler = AsyncMock(
-        side_effect=[False, False, False, True])
+    prediction_process.path_flag_handler = AsyncMock(side_effect=[False, False, False, True])
     # Arrange
     input_data = {
         'metadata': metadata,
@@ -348,24 +467,21 @@ async def test_run_stop_at_mlflow_last_response_gate(workflow_mock, prediction_p
         'mlflow_transform_filters': {'test': 'filter'},
         'mlflow_predict_filters': {'test': 'filter'},
         'model_name': 'test_model_name',
-        'model_config': {
-            'retention': '30'
-        },
+        'model_config': {'retention': '30'},
         'path_priority': ['continue', 'repeat', 'stop'],
-
     }
 
     # Mock the activity responses
     workflow_mock.execute_local_activity_method.side_effect = [
         '2024-01-01',  # get_last_timestamp
-        ('continue', 0.95, "Input data with bad quality"),  # input_gate
+        ('continue', 0.95, 'Input data with bad quality'),  # input_gate
         {'content': 'transformed_data', 'timestamp': '2024-01-01'},  # transform_data
         # mlflow_response_gate (transform)
-        ('continue', 0.95, "Error"),
+        ('continue', 0.95, 'Error'),
         # mlflow_content_gate (transform)
-        ('continue', 0.95, "Transformed data not passed the content filter"),
+        ('continue', 0.95, 'Transformed data not passed the content filter'),
         {'content': 'predicted_data', 'timestamp': '2024-01-01'},  # request_predict
-        ('continue', 0.95, "Error"),  # mlflow_response_gate (predict)
+        ('continue', 0.95, 'Error'),  # mlflow_response_gate (predict)
     ]
 
     # Act
@@ -373,63 +489,117 @@ async def test_run_stop_at_mlflow_last_response_gate(workflow_mock, prediction_p
 
     # Assert
     assert workflow_mock.execute_local_activity_method.call_count == 7
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.get_last_timestamp, {
-             'data': input_data['data'],
-             **metadata,
-             },
-             retry_policy=ANY, start_to_close_timeout=ANY)])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.input_gate, {
-             'filters': input_data['input_filters'],
-             'data': input_data['data'],
-             'path_priority': input_data['path_priority'],
-             **metadata,
-             },
-             retry_policy=ANY, start_to_close_timeout=ANY)])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.request_transform, {
-            'data': input_data['data'],
-            'model_name': input_data['model_name'],
-            'model_config': input_data['model_config'],
-            **metadata
-        }, retry_policy=ANY, start_to_close_timeout=ANY)])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.mlflow_response_gate, {
-            'filters': input_data['mlflow_transform_filters'],
-            'data': {'content': 'transformed_data', 'timestamp': '2024-01-01'},
-            'type': 'transform',
-            'path_priority': input_data['path_priority'],
-            **metadata,
-        }, retry_policy=ANY, start_to_close_timeout=ANY)])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.mlflow_content_gate, {
-            'filters': input_data['mlflow_transform_filters'],
-            'data': 'transformed_data',
-            'type': 'transform',
-            'path_priority': input_data['path_priority'],
-            **metadata,
-        }, retry_policy=ANY, start_to_close_timeout=ANY)])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.request_predict, {
-            'data': 'transformed_data',
-            'model_name': input_data['model_name'],
-            'model_config': input_data['model_config'],
-            **metadata
-        }, retry_policy=ANY, start_to_close_timeout=ANY)])
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(Activities.mlflow_response_gate, {
-            'filters': input_data['mlflow_predict_filters'],
-            'data': {'content': 'predicted_data', 'timestamp': '2024-01-01'},
-            'type': 'predict',
-            'path_priority': input_data['path_priority'],
-            **metadata,
-        }, retry_policy=ANY, start_to_close_timeout=ANY)])
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.get_last_timestamp,
+                {
+                    'data': input_data['data'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.input_gate,
+                {
+                    'filters': input_data['input_filters'],
+                    'data': input_data['data'],
+                    'path_priority': input_data['path_priority'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.request_transform,
+                {
+                    'data': input_data['data'],
+                    'model_name': input_data['model_name'],
+                    'model_config': input_data['model_config'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.mlflow_response_gate,
+                {
+                    'filters': input_data['mlflow_transform_filters'],
+                    'data': {'content': 'transformed_data', 'timestamp': '2024-01-01'},
+                    'type': 'transform',
+                    'path_priority': input_data['path_priority'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.mlflow_content_gate,
+                {
+                    'filters': input_data['mlflow_transform_filters'],
+                    'data': 'transformed_data',
+                    'type': 'transform',
+                    'path_priority': input_data['path_priority'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.request_predict,
+                {
+                    'data': 'transformed_data',
+                    'model_name': input_data['model_name'],
+                    'model_config': input_data['model_config'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.mlflow_response_gate,
+                {
+                    'filters': input_data['mlflow_predict_filters'],
+                    'data': {'content': 'predicted_data', 'timestamp': '2024-01-01'},
+                    'type': 'predict',
+                    'path_priority': input_data['path_priority'],
+                    **metadata,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
     workflow_mock.execute_child_workflow.assert_not_called()
 
 
 @mark.asyncio
-@patch("model_manager.workflows.sub_workflows.prediction_process.workflow", new_callable=AsyncMock)
+@patch('model_manager.workflows.sub_workflows.prediction_process.workflow', new_callable=AsyncMock)
 async def test_path_flag_handler_stop(workflow_mock, prediction_process):
     # Arrange
     data = {'test': 'data'}
@@ -440,21 +610,24 @@ async def test_path_flag_handler_stop(workflow_mock, prediction_process):
     model = 'test_model'
     last_timestamp = '2024-01-01'
     model_name = 'test_model_name'
-    model_config = {
-        'retention': '30'
-    }
+    model_config = {'retention': '30'}
 
     # Act
     result = await prediction_process.path_flag_handler(
-        data, path_flag, {
+        data,
+        path_flag,
+        {
             'metadata': metadata,
             'schema': schema,
             'table_name': table_name,
             'model_id': model,
             'last_timestamp': last_timestamp,
             'model_name': model_name,
-            'model_config': model_config
-        }, confidence, last_timestamp, ""
+            'model_config': model_config,
+        },
+        confidence,
+        last_timestamp,
+        '',
     )
 
     # Assert
@@ -464,7 +637,7 @@ async def test_path_flag_handler_stop(workflow_mock, prediction_process):
 
 
 @mark.asyncio
-@patch("model_manager.workflows.sub_workflows.prediction_process.workflow", new_callable=AsyncMock)
+@patch('model_manager.workflows.sub_workflows.prediction_process.workflow', new_callable=AsyncMock)
 async def test_path_flag_handler_repeat(workflow_mock, prediction_process):
     # Arrange
     data = {'test': 'data'}
@@ -475,21 +648,24 @@ async def test_path_flag_handler_repeat(workflow_mock, prediction_process):
     model = 'test_model'
     last_timestamp = '2024-01-01'
     model_name = 'test_model_name'
-    model_config = {
-        'retention': '30'
-    }
+    model_config = {'retention': '30'}
 
     # Act
     result = await prediction_process.path_flag_handler(
-        data, path_flag, {
+        data,
+        path_flag,
+        {
             'metadata': metadata,
             'schema': schema,
             'table_name': table_name,
             'model_id': model,
             'last_timestamp': last_timestamp,
             'model_name': model_name,
-            'model_config': model_config
-        }, confidence, last_timestamp, ""
+            'model_config': model_config,
+        },
+        confidence,
+        last_timestamp,
+        '',
     )
 
     # Assert
@@ -504,13 +680,13 @@ async def test_path_flag_handler_repeat(workflow_mock, prediction_process):
             'last_timestamp': last_timestamp,
         },
         retry_policy=ANY,
-        start_to_close_timeout=ANY
+        start_to_close_timeout=ANY,
     )
     workflow_mock.execute_child_workflow.assert_not_called()
 
 
 @mark.asyncio
-@patch("model_manager.workflows.sub_workflows.prediction_process.workflow", new_callable=AsyncMock)
+@patch('model_manager.workflows.sub_workflows.prediction_process.workflow', new_callable=AsyncMock)
 async def test_path_flag_handler_continue(workflow_mock, prediction_process):
     # Arrange
     data = {'test': 'data'}
@@ -521,14 +697,14 @@ async def test_path_flag_handler_continue(workflow_mock, prediction_process):
     model = 'test_model'
     last_timestamp = '2024-01-01'
     model_name = 'test_model_name'
-    model_config = {
-        'retention': '30'
-    }
+    model_config = {'retention': '30'}
     prediction_store_policy = 'erl:1'
 
     # Act
     result = await prediction_process.path_flag_handler(
-        data, path_flag, {
+        data,
+        path_flag,
+        {
             'metadata': metadata,
             'schema': schema,
             'table_name': table_name,
@@ -536,9 +712,11 @@ async def test_path_flag_handler_continue(workflow_mock, prediction_process):
             'last_timestamp': last_timestamp,
             'model_name': model_name,
             'model_config': model_config,
-    
-            'prediction_store_policy': prediction_store_policy
-        }, confidence, last_timestamp, 'Prediction Process'
+            'prediction_store_policy': prediction_store_policy,
+        },
+        confidence,
+        last_timestamp,
+        'Prediction Process',
     )
 
     # Assert
@@ -558,14 +736,13 @@ async def test_path_flag_handler_continue(workflow_mock, prediction_process):
             'schema': schema,
             'table_name': table_name,
             'comment': 'Prediction Process',
-    
-            'prediction_store_policy': prediction_store_policy
-        }
+            'prediction_store_policy': prediction_store_policy,
+        },
     )
 
 
 @mark.asyncio
-@patch("model_manager.workflows.sub_workflows.prediction_process.workflow", new_callable=AsyncMock)
+@patch('model_manager.workflows.sub_workflows.prediction_process.workflow', new_callable=AsyncMock)
 async def test_path_flag_handler_unknown(workflow_mock, prediction_process):
     # Arrange
     data = {'test': 'data'}
@@ -576,13 +753,13 @@ async def test_path_flag_handler_unknown(workflow_mock, prediction_process):
     model = 'test_model'
     last_timestamp = '2024-01-01'
     model_name = 'test_model_name'
-    model_config = {
-        'retention': '30'
-    }
+    model_config = {'retention': '30'}
     prediction_store_policy = 'erl:1'
     # Act
     result = await prediction_process.path_flag_handler(
-        data, path_flag, {
+        data,
+        path_flag,
+        {
             **metadata,
             'schema': schema,
             'table_name': table_name,
@@ -590,9 +767,11 @@ async def test_path_flag_handler_unknown(workflow_mock, prediction_process):
             'last_timestamp': last_timestamp,
             'model_name': model_name,
             'model_config': model_config,
-    
-            'prediction_store_policy': prediction_store_policy
-        }, confidence, last_timestamp, ""
+            'prediction_store_policy': prediction_store_policy,
+        },
+        confidence,
+        last_timestamp,
+        '',
     )
 
     # Assert

tests/laborious/workflows/test_minimal_retrain.py

@@ -1,5 +1,7 @@
-from unittest.mock import AsyncMock, MagicMock, call, patch, ANY
+from unittest.mock import ANY, AsyncMock, call, patch
+
 from pytest import fixture, mark
+
 from model_manager.activities.activities import Activities
 from model_manager.workflows.minimal_retrain import MinimalRetrain
 
@@ -10,11 +12,11 @@ def minimal_retrain() -> MinimalRetrain:
 
 
 metadata = {
-    "metadata": {
-        "model_id": "test_model_id",
-        "model_name": "test_model",
-        "workflow_name": "minimal_retrain",
-        "schedule_name": "test_schedule",
+    'metadata': {
+        'model_id': 'test_model_id',
+        'model_name': 'test_model',
+        'workflow_name': 'minimal_retrain',
+        'schedule_name': 'test_schedule',
     },
 }
 
@@ -23,19 +25,19 @@ metadata = {
 @patch('model_manager.workflows.minimal_retrain.workflow', new_callable=AsyncMock)
 async def test_run(workflow_mock: AsyncMock, minimal_retrain: MinimalRetrain):
     input_data = {
-        "model_id": "test_model_id",
-        "model_name": "test_model",
-        "workflow_name": "minimal_retrain",
-        "schedule_name": "test_schedule",
-        "query": "test_query",
-        "schema": "test_schema",
-        "table_name": "test_table",
+        'model_id': 'test_model_id',
+        'model_name': 'test_model',
+        'workflow_name': 'minimal_retrain',
+        'schedule_name': 'test_schedule',
+        'query': 'test_query',
+        'schema': 'test_schema',
+        'table_name': 'test_table',
     }
 
     workflow_mock.execute_activity_method = AsyncMock(
         return_value={
-            "data1": "1",
-            "data2": "2",
+            'data1': '1',
+            'data2': '2',
         }
     )
 
@@ -47,52 +49,58 @@ async def test_run(workflow_mock: AsyncMock, minimal_retrain: MinimalRetrain):
                 Activities.load_custom_query,
                 {
                     **metadata,
-                    "query": input_data["query"],
-                    'datetime_columns': input_data.get('datetime_columns', [])
+                    'query': input_data['query'],
+                    'datetime_columns': input_data.get('datetime_columns', []),
                 },
                 retry_policy=ANY,
-                start_to_close_timeout=ANY
+                start_to_close_timeout=ANY,
             )
         ]
     )
 
-    workflow_mock.execute_activity_method.assert_has_calls([
-        call(
-            Activities.retrain_model,
-            {
-                **metadata,
-                'data': workflow_mock.execute_local_activity_method.return_value,
-                'model_name': input_data['model_name'],
-            },
-            retry_policy=ANY,
-            start_to_close_timeout=ANY
-        )
-    ])
+    workflow_mock.execute_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.retrain_model,
+                {
+                    **metadata,
+                    'data': workflow_mock.execute_local_activity_method.return_value,
+                    'model_name': input_data['model_name'],
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
 
-    workflow_mock.execute_activity_method.assert_has_calls([
-        call(
-            Activities.update_production_model,
-            {
-                **metadata,
-                'model_name': input_data['model_name'],
-                'model_id': input_data['model_id'],
-                **workflow_mock.execute_activity_method.return_value,
-            },
-            retry_policy=ANY,
-            start_to_close_timeout=ANY
-        )
-    ])
+    workflow_mock.execute_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.update_production_model,
+                {
+                    **metadata,
+                    'model_name': input_data['model_name'],
+                    'model_id': input_data['model_id'],
+                    **workflow_mock.execute_activity_method.return_value,
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
 
-    workflow_mock.execute_activity_method.assert_has_calls([
-        call(
-            Activities.export_data_to_postgres,
-            {
-                **metadata,
-                'data': workflow_mock.execute_activity_method.return_value,
-                'schema': input_data['schema'],
-                'table_name': input_data['table_name'],
-            },
-            retry_policy=ANY,
-            start_to_close_timeout=ANY
-        )
-    ])
+    workflow_mock.execute_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.export_data_to_postgres,
+                {
+                    **metadata,
+                    'data': workflow_mock.execute_activity_method.return_value,
+                    'schema': input_data['schema'],
+                    'table_name': input_data['table_name'],
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )

tests/laborious/workflows/test_predictions_batch.py

@@ -1,5 +1,7 @@
-from unittest.mock import AsyncMock, call, patch, ANY
+from unittest.mock import ANY, AsyncMock, call, patch
+
 from pytest import fixture, mark
+
 from model_manager.activities.activities import Activities
 from model_manager.workflows.predictions_batch import PredictionsBatch
 
@@ -10,11 +12,11 @@ def predictions_batch() -> PredictionsBatch:
 
 
 metadata = {
-    "metadata": {
-        "model_id": "test_model_id",
-        "model_name": "test_model",
-        "workflow_name": "predictions_batch",
-        "schedule_name": "test_schedule",
+    'metadata': {
+        'model_id': 'test_model_id',
+        'model_name': 'test_model',
+        'workflow_name': 'predictions_batch',
+        'schedule_name': 'test_schedule',
     },
 }
 
@@ -22,9 +24,7 @@ metadata = {
 @mark.asyncio
 @patch('model_manager.workflows.predictions_batch.workflow', new_callable=AsyncMock)
 async def test_run(workflow_mock: AsyncMock, predictions_batch: PredictionsBatch):
-    workflow_mock.execute_local_activity_method.return_value = {
-        'data': 'test_data'
-    }
+    workflow_mock.execute_local_activity_method.return_value = {'data': 'test_data'}
     input_data = {
         'schedule_name': 'test_schedule',
         'model_name': 'test_model',
@@ -32,28 +32,27 @@ async def test_run(workflow_mock: AsyncMock, predictions_batch: PredictionsBatch
         'query': 'SELECT * FROM test',
         'schema': 'test_schema',
         'table_name': 'test_table',
-
         'datetime_columns': ['timestamp', 'created_at'],
         'prediction_store_policy': 'erl:1',
-        'model_config': {
-            'retention': '30'
-        }
+        'model_config': {'retention': '30'},
     }
 
     await predictions_batch.run(input_data)
 
-    workflow_mock.execute_local_activity_method.assert_has_calls([
-        call(
-            Activities.load_custom_query,
-            {
-                **metadata,
-                'query': input_data['query'],
-                'datetime_columns': input_data.get('datetime_columns', [])
-            },
-            retry_policy=ANY,
-            start_to_close_timeout=ANY
-        )
-    ])
+    workflow_mock.execute_local_activity_method.assert_has_calls(
+        [
+            call(
+                Activities.load_custom_query,
+                {
+                    **metadata,
+                    'query': input_data['query'],
+                    'datetime_columns': input_data.get('datetime_columns', []),
+                },
+                retry_policy=ANY,
+                start_to_close_timeout=ANY,
+            )
+        ]
+    )
     prediction_input = {
         'metadata': metadata,
         'data': {'data': 'test_data'},
@@ -61,28 +60,18 @@ async def test_run(workflow_mock: AsyncMock, predictions_batch: PredictionsBatch
         'table_name': input_data['table_name'],
         'model_id': input_data['model_id'],
         'model_name': input_data['model_name'],
-        'input_filters': input_data.get('input_filters', {
-            'EMPTY_DATA': {
-                'POLICY': 'STOP'
-            }
-        }),
-        'mlflow_transform_filters': input_data.get('mlflow_transform_filters', {
-            'API_ERROR': {
-                'POLICY': 'STOP'
-            }
-        }),
-        'mlflow_predict_filters': input_data.get('mlflow_predict_filters', {
-            'API_ERROR': {
-                'POLICY': 'STOP'
-            }
-        }),
+        'input_filters': input_data.get('input_filters', {'EMPTY_DATA': {'POLICY': 'STOP'}}),
+        'mlflow_transform_filters': input_data.get(
+            'mlflow_transform_filters', {'API_ERROR': {'POLICY': 'STOP'}}
+        ),
+        'mlflow_predict_filters': input_data.get(
+            'mlflow_predict_filters', {'API_ERROR': {'POLICY': 'STOP'}}
+        ),
         'model_config': input_data.get('model_config', {}),
         'path_priority': input_data.get('path_priority', ['STOP', 'CONTINUE', 'REPEAT']),
-
-        'prediction_store_policy': input_data.get('prediction_store_policy', 'erl:1')
+        'prediction_store_policy': input_data.get('prediction_store_policy', 'erl:1'),
     }
 
-    workflow_mock.execute_child_workflow.assert_has_calls([
-        call(
-            'prediction_process', prediction_input)
-    ])
+    workflow_mock.execute_child_workflow.assert_has_calls(
+        [call('prediction_process', prediction_input)]
+    )