According to IsolationForest papers (refs are given in documentation) the score produced by Isolation Forest should be between 0 and 1.
The implementation in scikit-learn negates the scores (so high score is more on inlier) and also seems to shift it by some amount. I've tried to figure out how to reverse it but was not successful so far. The code has some methods and attributes like score_samples() and self.offset_ that are not accessible from a fitted object. The documentation and comments in code on usage of self.contamination seem contradictory...
I have version 19.1 of scikit-learn (can't tell if there were significant changes in IsolationForest implementation since then)
Any ideas/suggestions would be appreciated!