def MAP(ids, labels, vectors, mingle, classifier):
pids = [x[1] for x in ids]
xlat = group_by_author(ids, zip(labels, vectors))
prec = 0
N = 0
def comparer(x,y):
return int(classifier.predict(mingle(x[1], y[1]))[0])
for _, scores in xlat.iteritems():
rank = [x[0] for x in sorted(scores, cmp=comparer, reverse=True)]
prec += avg_prec(lambda x:x, rank)
N += 1
return prec/float(N)
def write_csv(train_set, vectors, mingle, classifier):
def getId(obj):
return obj.Id if type(obj) is record else obj
authorIds = [(getId(a), ) for a,p in train_set]
paperIds = [getId(p) for a,p in train_set]
table = group_by_author(authorIds, zip(paperIds, vectors))
def comparer(x,y):
return int(classifier.predict(mingle(x[1], y[1]))[0])
csv = open("output.csv", "w")
print >> csv, "AuthorId, PaperIds"
for authorId, ranking in table.iteritems():
ranking = sorted(ranking, cmp=comparer, reverse=True)
print >> csv, "%d," % authorId,
for paperId, _ in ranking:
print >> csv, paperId,
print >> csv
