SIENTIAPDE-1231

Enhance MLFlow and MLFlowRepository with improved data handling and logging

- Refactored MLFlow class to sort data by 'created_at' and drop duplicates for better input preparation.
- Updated MLFlowRepository methods to include detailed logging for artifact downloads and model predictions.
- Introduced LzmaPayloadCodec for efficient payload compression in the worker, optimizing data handling for large payloads.
- Enhanced timestamp handling in treated data to ensure compatibility with model expectations.

laborious/activities/mlflow.py

@@ -232,14 +232,23 @@ class MLFlow(BaseActivity):
         timestamp = data['timestamp'].max()
         self.debug(f'Timestamp: {timestamp}', metadata)
 
+        # Sort by created_at in descending order and keep first occurrence of each variable/timestamp pair
+        data = data.sort_values('created_at', ascending=False).drop_duplicates(
+            subset=['variable', 'timestamp'], keep='first'
+        )
+
         data.drop(columns=['model_id'], inplace=True, errors='ignore')
         data.drop(columns=['created_at'], inplace=True, errors='ignore')
 
-        data = data.pivot(index='timestamp', columns='variable',
-                          values='value')
-        data.sort_index(inplace=True)
-        data.reset_index(inplace=True)
+        # Pivot data for model input format
+        data = data.pivot(
+            index='timestamp', columns='variable',
+            values='value')
+        data.fillna(np.nan, inplace=True)
+        # data.reset_index(inplace=True)
+        data.columns.name = None
 
+        data['timestamp'] = data.index
         data['timestamp'] = to_datetime(
             data['timestamp'], format=DATETIME_FORMAT_WITH_TZ).dt.strftime(DATETIME_FORMAT)
         data['timestamp'] = to_datetime(