pyLAB = Lean adaptive balancer (Python version)

Given

• a total evaluation budget N
• an initial budget N0
• a pruning factor of P,

this would explore (say) 10,000 unlabelled items as follows:

1. use N0 evalaution to label four items ;
2. divide labelled items into best and rest using a multi-goal criteria;
3. build a classifier to find, for unlabelled item, B=like(best), R=like(resti);
4. sort, in ascending order, the remaining 9996 items by -B/R;
5. label the top item (so N=N-1), and
6. discard the last P% unlabelled items;
7. update best and rest with this new item;
8. if N > 0, loop back to (2)
   • Else terminate, returning the top item in best.