added more fine-grained timing

logscoremetric.py

@@ -143,20 +143,31 @@ class LogScoreMetric(evaluate.Metric):
     def _compute(self, predictions, references):
         """Returns the scores"""
 
-        t_before = time.perf_counter()
+        t_before_logmetric = time.perf_counter()
 
         timestamp_score = np.mean([self.getLogMetric(p,r) for p,r in zip(predictions,references)])
+
+        t_after_logmetric = t_before_timestamp_rm = time.perf_counter()
+
         predictions_without_timestamps = [self.timestamp_pattern.sub('', p) for p in predictions]
         references_without_timestamps = [self.timestamp_pattern.sub('', r) for r in references]
 
+        t_after_timestamp_rm = t_before_sacrebleu = time.perf_counter()
+
         # Sacrebleu score on logs without timestamps
         sb_results = self.sacrebleu.compute(predictions=predictions_without_timestamps, references=references_without_timestamps)
 
-        t_after = time.perf_counter()
-        compute_duration = f" {t_after - t_before:0.4f}"
+        t_after_sacrebleu = time.perf_counter()
+        logmetric_duration = f" {t_after_logmetric - t_before_logmetric:0.10f}"
+        timestamp_rm_duration = f" {t_after_timestamp_rm - t_before_timestamp_rm:0.10f}"
+        sacrebleu_duration = f" {t_after_sacrebleu - t_before_sacrebleu:0.10f}"
+        overall_duration = f" {t_after_sacrebleu - t_before_logmetric:0.10f}"
 
         return {
             "timestamp_score": timestamp_score,
             "sacrebleu_score": sb_results["score"],
-            "compute_duration":compute_duration
+            "logmetric_duration":logmetric_duration,
+            "timestamp_rm_duration":timestamp_rm_duration,
+            "sacrebleu_duration":sacrebleu_duration,
+            "overall_duration":overall_duration
         }