Include statistics metrics

nebula/frontend/app.py

@@ -757,6 +757,9 @@ def stop_scenario(scenario_name):
     ScenarioManagement.stop_participants()
     ScenarioManagement.stop_blockchain()
     scenario_set_status_to_finished(scenario_name)
+    # Generate statistics for the scenario
+    path = Utils.check_path(settings.log_dir, scenario_name)
+    ScenarioManagement.generate_statistics(path)
 
 
 def stop_all_scenarios():

nebula/scenarios.py

@@ -12,6 +12,7 @@
 from datetime import datetime
 
 import docker
+import tensorboard_reducer as tbr
 
 from nebula.addons.blockchain.blockchain_deployer import BlockchainDeployer
 from nebula.addons.topologymanager import TopologyManager
@@ -1034,3 +1035,51 @@ def scenario_finished(self, timeout_seconds):
                 return False
 
             time.sleep(5)
+
+    @classmethod
+    def generate_statistics(cls, path):
+        try:
+            # Generate statistics
+            logging.info(f"Generating statistics for scenario {path}")
+
+            # Define input directories
+            input_event_dirs = sorted(glob.glob(os.path.join(path, "metrics/*")))
+            # Where to write reduced TB events
+            tb_events_output_dir = os.path.join(path, "metrics", "reduced-data")
+            csv_out_path = os.path.join(path, "metrics", "reduced-data-as.csv")
+            # Whether to abort or overwrite when csv_out_path already exists
+            overwrite = False
+            reduce_ops = ("mean", "min", "max", "median", "std", "var")
+
+            # Handle duplicate steps
+            handle_dup_steps = "keep-first"
+            # Strict steps
+            strict_steps = False
+
+            events_dict = tbr.load_tb_events(
+                input_event_dirs, handle_dup_steps=handle_dup_steps, strict_steps=strict_steps
+            )
+
+            # Number of recorded tags. e.g. would be 3 if you recorded loss, MAE and R^2
+            n_scalars = len(events_dict)
+            n_steps, n_events = list(events_dict.values())[0].shape
+
+            logging.info(f"Loaded {n_events} TensorBoard runs with {n_scalars} scalars and {n_steps} steps each")
+            logging.info(f"Events dict keys: {events_dict.keys()}")
+
+            reduced_events = tbr.reduce_events(events_dict, reduce_ops)
+
+            for op in reduce_ops:
+                logging.info(f"Writing '{op}' reduction to '{tb_events_output_dir}-{op}'")
+
+            tbr.write_tb_events(reduced_events, tb_events_output_dir, overwrite)
+
+            logging.info(f"Writing results to '{csv_out_path}'")
+
+            tbr.write_data_file(reduced_events, csv_out_path, overwrite)
+
+            logging.info("Reduction complete")
+
+        except Exception as e:
+            logging.exception(f"Error generating statistics: {e}")
+            return False

pyproject.toml

@@ -124,6 +124,8 @@ frontend = [
     "setuptools==74.1.2",
     "tensorboard==2.17.1",
     "tensorboardx==2.6.2.2",
+    "tensorboard-reducer==0.3.1",
+    "torch==2.4.1",
     "uvicorn==0.30.6",
     "web3==6.20.0",
     "wheel==0.44.0",