use jaccard again for log-message comparison

logmetric.py

@@ -111,10 +111,15 @@ class LogMetric(evaluate.Metric):
 
         return 1 - (abs(s1len - s2len) / max(s1len, s2len))
 
-    # Combine a weighted average of different scores
+    # Use minimum edit distance between two sentences
     def get_overall_similarity(self, sentence1, sentence2):
-        return SequenceMatcher(None, sentence1, sentence2).ratio() * 100
+        s1split = sentence1.split()
+        s2split = sentence2.split()
 
+        jaccard_score = self.get_jaccard_similarity(set(s1split), set(s2split))
+        length_score = self.get_length_score(s1split, s2split)
+
+        return (jaccard_score * 0.7 + length_score * 0.3) * 100.0
 
     def getLogMetric(self, pred : str, ref : str):
         ref = ref.strip(' \t\n\r')
@@ -212,7 +217,7 @@ class LogMetric(evaluate.Metric):
         timestamp_score = np.mean([self.getLogMetric(p,r) for p,r in zip(predictions,references)])
         t_after_logmetric = time.perf_counter()
 
-        logmetric_duration = f" {t_after_logmetric - t_before_logmetric:0.10f}"
+        logmetric_duration = f"{t_after_logmetric - t_before_logmetric:0.10f}"
 
         return {
             "score": timestamp_score,