--- a
+++ b/utilities/metricUtils.py
@@ -0,0 +1,63 @@
+import os, csv
+import numpy as np
+import torch
+from sklearn import metrics as skmetrics
+import matplotlib.pyplot as plt
+
+class MultiClassMetrics():
+    def __init__(self, logpath):
+        self.tgt = []
+        self.prd = []
+        self.nnloss = []
+        self.logpath = logpath
+
+    def reset(self, save_results = False):
+        if save_results: self._write_predictions()
+        self.__init__(self.logpath)
+
+    def add_entry(self, prd, tgt, loss=0):
+        self.prd.extend(prd.cpu().detach().numpy())
+        self.tgt.extend(tgt.cpu().detach().numpy())
+        if loss: self.nnloss.append(loss.cpu().detach().numpy())
+
+    def get_loss(self):
+        return sum(self.nnloss) / len(self.nnloss)
+
+    def get_accuracy(self):
+        return skmetrics.accuracy_score(self.tgt, self.prd)
+
+    def get_balanced_accuracy(self):
+        return skmetrics.balanced_accuracy_score(self.tgt, self.prd)
+
+    def get_f1score(self):
+        return skmetrics.f1_score(self.tgt, self.prd, average='macro')
+
+    def get_class_report(self):
+        return skmetrics.classification_report(self.tgt, self.prd,
+                    output_dict= True)
+
+    def get_confusion_matrix(self, save_png = False, title=""):
+        lbls = sorted(list(set(self.tgt)))
+        cm = skmetrics.confusion_matrix(self.tgt, self.prd,
+                                labels= lbls)
+        if save_png:
+            disp = skmetrics.ConfusionMatrixDisplay(confusion_matrix=cm,
+                                        display_labels=lbls).plot()
+            plt.savefig(self.logpath+f'/{title}Confusion.png', bbox_inches='tight')
+        return cm
+
+    def _write_predictions(self, title=""):
+        with open(os.path.join(self.logpath, f"{title}Predict.csv"), 'w') as f:
+            writer = csv.writer(f)
+            writer.writerow(["target", "prediction"])
+            writer.writerows(zip(self.tgt, self.prd))
+
+
+
+if __name__ == "__main__":
+
+    obj = MultiClassMetrics()
+    obj.tgt = [1,1,1,2,2,2,3,3,3,4,4,4,5,5,5]
+    obj.prd = [1,1,2,2,2,3,3,3,4,4,4,5,5,5,1]
+
+    print(obj.get_class_report())
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