Merge pull request #234 from leondavi/warningFix

normalization option fix
leondavi · Aug 22, 2023 · f66e9c5 · f66e9c5
2 parents 83cdb0f + c183c27
commit f66e9c5
Showing 1 changed file with 6 additions and 4 deletions.
diff --git a/src_py/apiServer/apiServer.py b/src_py/apiServer/apiServer.py
@@ -25,7 +25,6 @@ def is_port_in_use(port: int) -> bool:
     with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
         return s.connect_ex(('localhost', port)) == 0
 
-
 class ApiServer():
     def __init__(self):       
         self.json_dir_parser = JsonDirParser()
@@ -66,7 +65,7 @@ def help(self):
 ==========Experiment info===========
 -print_saved_experiments()          prints saved experiments and their number for statistics later
 -plot_loss(ExpNum)                  saves and shows the loss over time of the chosen experiment
--accuracy_matrix(ExpNum)            shows a graphic for the confusion matrix. Also returns all ConfMat[worker][nueron]
+-accuracy_matrix(ExpNum, Normalize) Normalize = True | False. shows a graphic for the confusion matrix. Also returns all ConfMat[worker][nueron]
 -communication_stats()              prints the communication statistics of the current network. integer => message count, float => avg calc time (ms)
 
 _____GLOBAL VARIABLES / CONSTANTS_____
@@ -313,7 +312,7 @@ def plot_loss(self, expNum):
         plt.savefig(f'/usr/local/lib/nerlnet-lib/NErlNet/Results/{expForStats.name}/Training/{fileName}.png')
         print(f'\n{fileName}.png was Saved...')
 
-    def accuracy_matrix(self, expNum, normalizeEnabled = 'false'):
+    def accuracy_matrix(self, expNum, normalizeEnabled = False):
         expForStats = self.experiments[expNum-1] 
 
         # Choose the matching (to the original labeled CSV) CSV from the prediction results list:
@@ -412,7 +411,10 @@ def accuracy_matrix(self, expNum, normalizeEnabled = 'false'):
             for j in range(labelsLen):
                 # print(f"worker {worker}, has {len(workerNeuronRes[worker][TRUE_LABLE_IND])} labels, with {len(workerNeuronRes[worker][TRUE_LABLE_IND][j])} samples")
                 # print(f"confusion {worker}:{j}, has is of {workerNeuronRes[worker][TRUE_LABLE_IND][j]}, {workerNeuronRes[worker][PRED_LABLE_IND][j]}")
-                confMatList[worker][j] = confusion_matrix(workerNeuronRes[worker][globe.TRUE_LABLE_IND][j], workerNeuronRes[worker][globe.PRED_LABLE_IND][j], normalize=normalizeEnabled)
+                if normalizeEnabled == True :
+                    confMatList[worker][j] = confusion_matrix(workerNeuronRes[worker][globe.TRUE_LABLE_IND][j], workerNeuronRes[worker][globe.PRED_LABLE_IND][j], normalize='all')
+                else:
+                    confMatList[worker][j] = confusion_matrix(workerNeuronRes[worker][globe.TRUE_LABLE_IND][j], workerNeuronRes[worker][globe.PRED_LABLE_IND][j])
                 # print(confMatList[worker][j])
                 disp = ConfusionMatrixDisplay(confMatList[worker][j], display_labels=["X", labelNames[j]])
                 disp.plot(ax=axes[i, j], colorbar=False)