Merge pull request #389 from leondavi/stat_update

[stat_update] update the Title of the graph "plot_batches_status()"

src_py/apiServer/stats.py

@@ -353,37 +353,37 @@ def plot_batches_status(self, plot=False):
         missed_batches = self.get_missed_batches()
 
         # Initialize dictionaries to store batch counts for each worker
-        batches_received_train = {worker: 0 for worker in workers_names}
-        batches_dropped_train = {worker: 0 for worker in workers_names}
+        batches_received = {worker: 0 for worker in workers_names}
+        batches_dropped = {worker: 0 for worker in workers_names}
 
         # Fill the dictionaries with the counts of received and missed batches
         for key, batches in received_batches.items():
             worker = key.split('->')[-1]
-            batches_received_train[worker] += len(batches)
+            batches_received[worker] += len(batches)
 
         for key, batches in missed_batches.items():
             worker = key.split('->')[-1]
-            batches_dropped_train[worker] += len(batches)
+            batches_dropped[worker] += len(batches)
 
         # Create a DataFrame for plotting
         workers_comm_dict = {
-            'Worker': list(batches_received_train.keys()),
-            'batches_received_train': list(batches_received_train.values()),
-            'batches_dropped_train': list(batches_dropped_train.values())
+            'Worker': list(batches_received.keys()),
+            'batches_received': list(batches_received.values()),
+            'batches_dropped': list(batches_dropped.values())
         }
-        df_train = pd.DataFrame(workers_comm_dict)
+        df = pd.DataFrame(workers_comm_dict)
 
         # Sort the DataFrame by the worker names
-        df_train = df_train.sort_values(by='Worker')
+        df = df.sort_values(by='Worker')
 
         # Plotting
         if plot:
             plt.figure(figsize=(10, 6))
-            data_train = pd.melt(df_train, id_vars=['Worker'], value_vars=['batches_received_train', 'batches_dropped_train'])
-            batches_stats = sns.barplot(x='Worker', y='value', hue='variable', data=data_train, order=sorted(workers_names))
+            data = pd.melt(df, id_vars=['Worker'], value_vars=['batches_received', 'batches_dropped'])
+            batches_stats = sns.barplot(x='Worker', y='value', hue='variable', data=data, order=sorted(workers_names))
             plt.ylabel('Number Of Batches')
             plt.xlabel('Worker')
-            plt.title(f"Received & Dropped Batches At Freq. 5B/s ({self.experiment_phase.get_name()})")
+            plt.title(f"Received & Dropped Batches in Phase: ({self.experiment_phase.get_name()})")
 
             batches_stats.legend(loc='upper right', bbox_to_anchor=(1.5, 0.2), shadow=True, ncol=1)
             plt.show()