SIENTIAPDE-1182

Implement prediction store policy handling in Gates activity

- Added a new method `get_prediction_store_policy` to validate and parse the prediction store policy.
- Updated `format_prediction` method to utilize the new policy handling, allowing for sorting of predictions based on the specified policy.
- Enhanced test coverage for the new policy handling, including various scenarios for valid and invalid policies.
- Removed the obsolete `coverage.sh` script.

laborious/activities/gates.py

@@ -242,6 +242,28 @@ class Gates(BaseActivity):
         self.info("Nothing was filtered by the mlflow content gate", metadata)
         return None, 0, ""
 
+    def get_prediction_store_policy(self,
+                                    prediction_store_policy: str,
+                                    metadata: dict[str, Any]) -> tuple[str, int]:
+        policy_elements = prediction_store_policy.split(':')
+
+        if len(policy_elements) < 2:
+            self.error(
+                f"Invalid prediction store policy: {prediction_store_policy}, using default policy", metadata)
+            return 'lts', 1
+
+        policy_type = policy_elements[0]
+        policy_value = policy_elements[1]
+
+        # If the policy_type is not lts or erl, we use the default policy
+        # If the policty_value is not a number or 0, we use the default policy
+        if policy_type not in ['lts', 'erl'] or not policy_value.isdigit() or int(policy_value) == 0:
+            self.error(
+                f"Invalid prediction store policy: {prediction_store_policy}, using default policy", metadata)
+            return 'lts', 1
+
+        return policy_type, int(policy_value)
+
     @activity.defn(name="format_prediction")
     async def format_prediction(self, input_data: dict[str, Any]) -> dict[Any, Any]:
         """
@@ -252,21 +274,58 @@ class Gates(BaseActivity):
                 - timestamp (str): The timestamp of the data.
                 - model_id (str): The id of the model.
                 - prediction_confidence (float): The confidence of the prediction.
+                - prediction_store_policy (str): The policy to store the prediction.
         Returns:
             dict: The formatted data.
         """
         metadata = input_data['metadata']
+        prediction_store_policy = input_data['prediction_store_policy']
         self.info("Formatting prediction...", metadata)
 
         data = DataFrame(input_data['data'])
-        data['timestamp'] = input_data['timestamp']
+
+        self.debug(
+            f"Prediction store policy: {prediction_store_policy}", metadata)
+
+        policy_type, policy_value = self.get_prediction_store_policy(
+            prediction_store_policy, metadata)
+
+        # If data has no timestamp, we use the default timestamp and not sort the data
+        if 'timestamp' not in data.columns:
+            self.warning(
+                "Data has no timestamp, using default timestamp", metadata)
+            data['timestamp'] = input_data['timestamp']
+        else:
+            self.debug(
+                f"Data has timestamp, sorting data by timestamp", metadata)
+
+            # If policy_type is lts, we need to sort the data by timestamp descending and take the first policy_value rows
+            if policy_type == 'lts':
+                self.debug(
+                    f"Sorting data by timestamp descending", metadata)
+                data = data.sort_values(by='timestamp', ascending=False)
+            # If policy_type is erl, we need to sort the data by timestamp ascending and take the first policy_value rows
+            elif policy_type == 'erl':
+                self.debug(
+                    f"Sorting data by timestamp ascending", metadata)
+                data = data.sort_values(by='timestamp', ascending=True)
+            else:
+                self.error(
+                    f"Invalid policy type: {policy_type}, using default policy", metadata)
+                raise ValueError(
+                    f"Invalid policy type: {policy_type}")
+
+        data = data.head(int(policy_value))
+
         data['model_id'] = input_data['model_id']
         data['prediction_confidence'] = input_data['prediction_confidence']
         data['prediction_status'] = 'Good'
         data['comments'] = ""
-        data = data.sort_values(by='timestamp')
+        data = data.sort_values(by='timestamp', ascending=False)
+        data = data.reset_index(drop=True)
 
         self.info(f"Prediction formatted: {data.size} rows", metadata)
+        self.debug(f"Prediction data: {data.to_string()}", metadata)
 
         return data.to_dict()