classification using LogSoftmax vs Softmax and calculating precision-recall curve?

In case of binary classification we could get final output using LogSoftmax or Softmax. In case of softmax we get results that add up to 1. I understand that LogSoftmax penalizes more for a wrong classification and few other mathematical advantage.

I have binary classification problem with class 1 occurring very rarely (2% times)

my questions:

1. If I am using probability cutoff of 0.5 (predicting to class 1 if prob is above 0.5) with Softmax then will I get same values for overall accuracy, class 1 - recall, precision and f1 as when using LogSoftmax (and using the lower value of output as prediction class)?
2. How to calculate precision-recall curve when using LogSoftmax ? This link says that The precision-recall curve is constructed by calculating and plotting the precision against the recall for a single classifier at a variety of thresholds. How are we going to choose those thresholds if output is not between 0 to 1?