def score_pairs_nonpairs(pairs, elut_fs, spbase, scores=['poisson'],
score_cutoff=0.25, do_nonpairs=False):
pdpairs = pd.PairDict(pairs)
for p in pdpairs.d: pdpairs.d[p] = 1 #marked as true paralog
if do_nonpairs:
for np in pu.nonpairs_gen(pairs, len(pairs)):
pdpairs.set(np, 0) # marked as non-paralog
arr = ppi.new_score_array(pdpairs, scores, elut_fs, [])
del pdpairs #lots of memory
scored_arr = ppi.score_and_filter(arr, scores, elut_fs, score_cutoff,
spbase, [], '', do_filter=False)
return scored_arr
def hist_pairs_nonpairs(scores, pairs, negmult=1, do_plot=True, **kwargs):
"""
scores: list of tuples (id1, id2, score)
pairs: list of tuples (id1, id2)
Make a histogram for scores of pairs against random sampling of non-pairs
from the set of ids making up pairs.
"""
assert len(pairs[0])==2, "Too many data points"
nonpairs = pu.nonpairs_gen(pairs, len(pairs)*negmult)
def scorelist_pairs(pairs, scores):
pdscores = pd.PairDict([s[:3] for s in scores])
for p in pairs:
puse = pdscores.find(p)
yield float(pdscores.d[puse][0]) if puse else 0
pscores, nscores = [[x for x in scorelist_pairs(l, scores)] for l in pairs, nonpairs]
if do_plot:
hist(pscores, **kwargs)
hist(nscores, **kwargs)
return pscores, nscores
