Predictions in Log-Cost-Space

I hope that I correctly remember our discussion yesterday about the predictions in log-cost space since I forgot my notes in my office. @frank-hutter if anything is wrong, please correct me.

@sfalkner Frank explained yesterday how he implemented the prediction in log(cost) space in SMAC and I don't know whether this is right now possible with the new RF. I hope you can please help us here.
* Train the RF using log(cost) values
* to get a marginalized prediction over instances
1. compute a marginalized prediction for each tree using exp(log(cost)) of all values in the leafs -> one prediction for each tree in the original cost space
1. mean and variance over all log(pred_t) for each t (so, again in the log-space)

How can we compute this with the RF? Is it possible using the python interface? Would it be inefficient to do it in Python? Can it be done within C++?