SIENTIAPDE-1273

Update requirements and enhance metrics and data handling

- Updated the sientia-dataops-library dependency version in requirements.txt to 1.5.3.
- Added new metrics for model analysis, including lag, count, and error count in metrics.py.
- Implemented a new method for formatting transformed data in gates.py.
- Enhanced MLFlowRepository with methods to load artifact dataframes and calculate model metrics, including drift and performance metrics.
- Updated the prediction process to handle transformed data and ensure proper execution of related activities in format_and_export_prediction.py and prediction_process.py.

laborious/activities/gates.py

@@ -402,6 +402,28 @@ class Gates(SientiaMonitoring):
 
         return policy_type, int(policy_value)
 
+
+    @activity.defn(name='format_transformed_data')
+    async def format_transformed_data(self, input_data: dict[str, Any]) -> dict[Any, Any]:
+        """
+        Format transformed data according to configured storage policies.
+        """
+        metadata = input_data['metadata']
+        
+        model_id = input_data['model_id']
+
+        self.info('Formatting transformed data...', metadata)
+
+        data = DataFrame(input_data['data'])
+
+        data['timestamp'] = data.index
+        data = data.reset_index(drop=True)
+
+        data = data.melt(id_vars='timestamp', var_name='variable', value_name='value')
+        data['model_id'] = model_id
+
+        return data.to_dict()
+
     @activity.defn(name='format_prediction')
     async def format_prediction(self, input_data: dict[str, Any]) -> dict[Any, Any]:
         """