Update evaluation.py

            self.eval_metrics['FP'] = FP

            self.eval_metrics['Fpr'] = fpr

            self.eval_metrics['Tpr'] = tpr      

            self.logger.log_evaluator(self.eval_metrics)    

            # If the recall has been reached to 1.0, we break the loop, due to the best f1-score has been achieved 

            # Since as the threshold increases, recall remains unchanged (1.0), while precision decreases and thus f1-score decreases

            if float(recall) < 1.0: