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.

model_manager/workflows/minimal_retrain.py

@@ -1,14 +1,16 @@
 from temporalio import workflow
 
 with workflow.unsafe.imports_passed_through():
-    from model_manager.activities.activities import Activities
-    from typing import Any
-    from sientia_do.temporal.policies import retry_policy
     from datetime import timedelta
+    from typing import Any
+
+    from sientia_do.temporal.policies import retry_policy
+
+    from model_manager.activities.activities import Activities
 
 
-@workflow.defn(name="minimal_retrain")
-class MinimalRetrain():
+@workflow.defn(name='minimal_retrain')
+class MinimalRetrain:
     """
     Automated model retraining workflow for the Model Manager system.
 
@@ -63,7 +65,7 @@ class MinimalRetrain():
                 'schedule_name': input_data['schedule_name'],
                 'model_name': input_data['model_name'],
                 'model_id': input_data['model_id'],
-                'workflow_name': 'minimal_retrain'
+                'workflow_name': 'minimal_retrain',
             }
         }
 
@@ -74,21 +76,17 @@ class MinimalRetrain():
             {
                 **metadata,
                 'query': input_data['query'],
-                'datetime_columns': input_data.get('datetime_columns', [])
+                'datetime_columns': input_data.get('datetime_columns', []),
             },
             retry_policy=retry_policy,
-            start_to_close_timeout=timedelta(seconds=60)
+            start_to_close_timeout=timedelta(seconds=60),
         )
 
         experiment_response = await workflow.execute_activity_method(
             Activities.retrain_model,
-            {
-                **metadata,
-                'data': data,
-                'model_name': model_name
-            },
+            {**metadata, 'data': data, 'model_name': model_name},
             retry_policy=retry_policy,
-            start_to_close_timeout=timedelta(seconds=60)
+            start_to_close_timeout=timedelta(seconds=60),
         )
 
         report = await workflow.execute_activity_method(
@@ -97,10 +95,10 @@ class MinimalRetrain():
                 **metadata,
                 'model_name': model_name,
                 'model_id': input_data['model_id'],
-                **experiment_response
+                **experiment_response,
             },
             retry_policy=retry_policy,
-            start_to_close_timeout=timedelta(seconds=60)
+            start_to_close_timeout=timedelta(seconds=60),
         )
 
         await workflow.execute_activity_method(
@@ -109,8 +107,8 @@ class MinimalRetrain():
                 **metadata,
                 'data': report,
                 'schema': input_data['schema'],
-                'table_name': input_data['table_name']
+                'table_name': input_data['table_name'],
             },
             retry_policy=retry_policy,
-            start_to_close_timeout=timedelta(seconds=60)
+            start_to_close_timeout=timedelta(seconds=60),
         )