Refactor aggregate

osbenchmark/aggregator.py

@@ -3,12 +3,12 @@
 from typing import Any, Dict, List, Union
 import uuid
 
-from osbenchmark.metrics import FileTestExecutionStore
+from osbenchmark.metrics import FileTestExecutionStore, TestExecution
 from osbenchmark import metrics, workload, config
 from osbenchmark.utils import io as rio
 
 class Aggregator:
-    def __init__(self, cfg, test_executions_dict, args):
+    def __init__(self, cfg, test_executions_dict, args) -> None:
         self.config = cfg
         self.args = args
         self.test_executions = test_executions_dict
@@ -21,69 +21,72 @@ def __init__(self, cfg, test_executions_dict, args):
         self.test_procedure_name = None
         self.loaded_workload = None
 
-    def count_iterations_for_each_op(self, test_execution) -> None:
-        matching_test_procedure = next((tp for tp in self.loaded_workload.test_procedures if tp.name == self.test_procedure_name), None)
+    def count_iterations_for_each_op(self, test_execution: TestExecution) -> None:
+        """Count iterations for each operation in the test execution"""
         workload_params = test_execution.workload_params if test_execution.workload_params else {}
-
         test_execution_id = test_execution.test_execution_id
         self.accumulated_iterations[test_execution_id] = {}
 
-        if matching_test_procedure:
-            for task in matching_test_procedure.schedule:
-                task_name = task.name
-                task_name_iterations = f"{task_name}_iterations"
-                if task_name_iterations in workload_params:
-                    iterations = int(workload_params[task_name_iterations])
-                else:
-                    iterations = task.iterations or 1
-                self.accumulated_iterations[test_execution_id][task_name] = iterations
-        else:
-            raise ValueError(f"Test procedure '{self.test_procedure_name}' not found in the loaded workload.")
+        for task in self.loaded_workload.find_test_procedure_or_default(self.test_procedure_name).schedule:
+            task_name = task.name
+            task_name_iterations = f"{task_name}_iterations"
+            iterations = int(workload_params.get(task_name_iterations, task.iterations or 1))
+            self.accumulated_iterations[test_execution_id][task_name] = iterations
 
-    def accumulate_results(self, test_execution: Any) -> None:
-        for item in test_execution.results.get("op_metrics", []):
-            task = item.get("task", "")
+    def accumulate_results(self, test_execution: TestExecution) -> None:
+        """Accumulate results from a single test execution"""
+        for operation_metric in test_execution.results.get("op_metrics", []):
+            task = operation_metric.get("task", "")
             self.accumulated_results.setdefault(task, {})
             for metric in self.metrics:
                 self.accumulated_results[task].setdefault(metric, [])
-                self.accumulated_results[task][metric].append(item.get(metric))
+                self.accumulated_results[task][metric].append(operation_metric.get(metric))
 
     def aggregate_json_by_key(self, key_path: Union[str, List[str]]) -> Any:
-        all_jsons = [self.test_store.find_by_test_execution_id(id).results for id in self.test_executions.keys()]
-
-        # retrieve nested value from a dictionary given a key path
-        def get_nested_value(obj: Dict[str, Any], path: List[str]) -> Any:
+        """
+        Aggregates JSON results across multiple test executions using a specified key path.
+        Handles nested dictionary structures and calculates averages for numeric values
+        """
+        all_json_results = [self.test_store.find_by_test_execution_id(id).results for id in self.test_executions.keys()]
+
+        def get_nested_value(json_data: Dict[str, Any], path: List[str]) -> Any:
+            """
+            Retrieves a value from a nested dictionary structure using a path of keys.
+            """
             for key in path:
-                if isinstance(obj, dict):
-                    obj = obj.get(key, {})
-                elif isinstance(obj, list) and key.isdigit():
-                    obj = obj[int(key)] if int(key) < len(obj) else {}
+                if isinstance(json_data, dict):
+                    json_data = json_data.get(key, {})
+                elif isinstance(json_data, list) and key.isdigit():
+                    json_data = json_data[int(key)] if int(key) < len(json_data) else {}
                 else:
                     return None
-            return obj
+            return json_data
 
-        def aggregate_helper(objects: List[Any]) -> Any:
-            if not objects:
+        def aggregate_json_elements(json_elements: List[Any]) -> Any:
+            if not json_elements:
                 return None
-            if all(isinstance(obj, (int, float)) for obj in objects):
-                avg = sum(objects) / len(objects)
-                return avg
-            if all(isinstance(obj, dict) for obj in objects):
-                keys = set().union(*objects)
-                return {key: aggregate_helper([obj.get(key) for obj in objects]) for key in keys}
-            if all(isinstance(obj, list) for obj in objects):
-                max_length = max(len(obj) for obj in objects)
-                return [aggregate_helper([obj[i] if i < len(obj) else None for obj in objects]) for i in range(max_length)]
-            return next((obj for obj in objects if obj is not None), None)
+            # If all elements are numbers, calculate the average
+            if all(isinstance(obj, (int, float)) for obj in json_elements):
+                return sum(json_elements) / len(json_elements)
+            # If all elements are dictionaries, recursively aggregate their values
+            if all(isinstance(obj, dict) for obj in json_elements):
+                keys = set().union(*json_elements)
+                return {key: aggregate_json_elements([obj.get(key) for obj in json_elements]) for key in keys}
+            # If all elements are lists, recursively aggregate corresponding elements
+            if all(isinstance(obj, list) for obj in json_elements):
+                max_length = max(len(obj) for obj in json_elements)
+                return [aggregate_json_elements([obj[i] if i < len(obj) else None for obj in json_elements]) for i in range(max_length)]
+            # If elements are of mixed types, return the first non-None value
+            return next((obj for obj in json_elements if obj is not None), None)
 
         if isinstance(key_path, str):
             key_path = key_path.split('.')
 
-        values = [get_nested_value(json, key_path) for json in all_jsons]
-        return aggregate_helper(values)
+        nested_values = [get_nested_value(json_result, key_path) for json_result in all_json_results]
+        return aggregate_json_elements(nested_values)
 
-    def build_aggregated_results(self):
-        test_exe = self.test_store.find_by_test_execution_id(list(self.test_executions.keys())[0])
+    def build_aggregated_results_dict(self) -> Dict[str, Any]:
+        """Builds a dictionary of aggregated metrics from all test executions"""
         aggregated_results = {
             "op_metrics": [],
             "correctness_metrics": self.aggregate_json_by_key("correctness_metrics"),
@@ -147,8 +150,30 @@ def build_aggregated_results(self):
 
             aggregated_results["op_metrics"].append(op_metric)
 
-        # extract the necessary data from the first test execution, since the configurations should be identical for all test executions
+        return aggregated_results
+
+    def update_config_object(self, test_execution: TestExecution) -> None:
+        """
+        Updates the configuration object with values from a test execution.
+        Uses the first test execution as reference since configurations should be identical
+        """
         current_timestamp = self.config.opts("system", "time.start")
+        self.config.add(config.Scope.applicationOverride, "builder",
+                        "provision_config_instance.names", test_execution.provision_config_instance)
+        self.config.add(config.Scope.applicationOverride, "system",
+                        "env.name", test_execution.environment_name)
+        self.config.add(config.Scope.applicationOverride, "system", "time.start", current_timestamp)
+        self.config.add(config.Scope.applicationOverride, "test_execution", "pipeline", test_execution.pipeline)
+        self.config.add(config.Scope.applicationOverride, "workload", "params", test_execution.workload_params)
+        self.config.add(config.Scope.applicationOverride, "builder",
+                        "provision_config_instance.params", test_execution.provision_config_instance_params)
+        self.config.add(config.Scope.applicationOverride, "builder", "plugin.params", test_execution.plugin_params)
+        self.config.add(config.Scope.applicationOverride, "workload", "latency.percentiles", test_execution.latency_percentiles)
+        self.config.add(config.Scope.applicationOverride, "workload", "throughput.percentiles", test_execution.throughput_percentiles)
+
+    def build_aggregated_results(self) -> TestExecution:
+        test_exe = self.test_store.find_by_test_execution_id(list(self.test_executions.keys())[0])
+        aggregated_results = self.build_aggregated_results_dict()
 
         if hasattr(self.args, 'results_file') and self.args.results_file != "":
             normalized_results_file = rio.normalize_path(self.args.results_file, self.cwd)
@@ -165,19 +190,7 @@ def build_aggregated_results(self):
 
         print("Aggregate test execution ID: ", test_execution_id)
 
-        # add values to the configuration object
-        self.config.add(config.Scope.applicationOverride, "builder",
-                        "provision_config_instance.names", test_exe.provision_config_instance)
-        self.config.add(config.Scope.applicationOverride, "system",
-                        "env.name", test_exe.environment_name)
-        self.config.add(config.Scope.applicationOverride, "system", "time.start", current_timestamp)
-        self.config.add(config.Scope.applicationOverride, "test_execution", "pipeline", test_exe.pipeline)
-        self.config.add(config.Scope.applicationOverride, "workload", "params", test_exe.workload_params)
-        self.config.add(config.Scope.applicationOverride, "builder",
-                        "provision_config_instance.params", test_exe.provision_config_instance_params)
-        self.config.add(config.Scope.applicationOverride, "builder", "plugin.params", test_exe.plugin_params)
-        self.config.add(config.Scope.applicationOverride, "workload", "latency.percentiles", test_exe.latency_percentiles)
-        self.config.add(config.Scope.applicationOverride, "workload", "throughput.percentiles", test_exe.throughput_percentiles)
+        self.update_config_object(test_exe)
 
         loaded_workload = workload.load_workload(self.config)
         test_procedure_object = loaded_workload.find_test_procedure_or_default(self.test_procedure_name)
@@ -223,7 +236,7 @@ def calculate_weighted_average(self, task_metrics: Dict[str, List[Any]], task_na
 
         return weighted_metrics
 
-    def calculate_rsd(self, values: List[Union[int, float]], metric_name: str):
+    def calculate_rsd(self, values: List[Union[int, float]], metric_name: str) -> Union[float, str]:
         if not values:
             raise ValueError(f"Cannot calculate RSD for metric '{metric_name}': empty list of values")
         if len(values) == 1:

tests/aggregator_test.py

@@ -50,6 +50,7 @@ def test_count_iterations_for_each_op(aggregator):
     mock_test_procedure.schedule = mock_schedule
     mock_workload.test_procedures = [mock_test_procedure]
 
+    mock_workload.find_test_procedure_or_default = Mock(return_value=mock_test_procedure)
     mock_test_execution = Mock(test_execution_id="test1", workload_params={})
 
     aggregator.loaded_workload = mock_workload