I confront with a binary classification problem where I do have a few instances with labels (so far this is "semi-supervised" learning as far as I know), but only from the positive class. So I cannot take any negative examples as basis for learning the other class. What is the best practice here? I assume that I should find some examples farthest from the explicit positives and treat these like negatives; but if so, what is a handy way for this in Python (preferably in sklearn)?
Furthermore, following the approach above, I'm a bit confused when to switch to supervised mode (if any time at all) if instances could be separated only with clustering?