ntra = len(traintargets)
ntes = len(testtargets)
totalpreds = [0] * len(targets)
for itr in range(0, ITER):
# write target
for i in range(0, ntra):
p.stdin.write('%2.4f\n' % traintargets[i])
# read prediction
for i in range(0, len(totalpreds)):
l = p.stdout.readline()
try:
totalpreds[i] += float(l.split(' ', 1)[0])
except:
print l
sys.exit(1)
# get and store results
preds = [float(i) / (itr + 1) for i in totalpreds]
[TRrmse, TRerr, TRndcg] = evaluate(preds[0:ntra], trainqueries,
traintargets)
[TErmse, TEerr, TEndcg] = evaluate(preds[ntra:], testqueries, testtargets)
print >> sys.stderr, "%i,%2.5f,%2.5f,%2.5f,%2.5f,%2.5f,%2.5f" % (
itr, TRrmse, TRerr, TRndcg, TErmse, TEerr, TEndcg)
p.stdin.flush()
# print preds to stdout
print '\n'.join([str(i) for i in preds])
W=[a for a in open(WEIGHTDATA, 'r').read().split('\n') if len(a)>0];
weights=[float(a) for a in W]
ntra=len(traintargets)
ntst=len(testtargets)
nall=ntra+ntst
preds = [0]*nall
# Read initial cumulative probabilities if available
if INITPRED!='':
preds = [float(l.split(' ', 1)[0]) for l in open(INITPRED)]
# Run boosting
for itr in range(ITER):
if (itr % 10)==0:
[TRrmse,TRerr,TRndcg]=evaluate(preds[0:ntra],trainqueries,traintargets)
[TErmse,TEerr,TEndcg]=evaluate(preds[ntra:nall],testqueries,testtargets)
print >>sys.stderr,"%i,%2.5f,%2.5f,%2.5f,%2.5f,%2.5f,%2.5f" % (itr,TRrmse,TRerr,TRndcg,TErmse,TEerr,TEndcg)
sys.stdout.flush()
# write target
for i in range(ntra):p.stdin.write('%2.4f\n' % (traintargets[i]-preds[i]))
for i in range(0,ntra):p.stdin.write('%2.4f\n'%(weights[i]))
p.stdin.flush()
# read prediction
for i in range(nall):
l=p.stdout.readline()
preds[i] += STEPSIZE*float(l.split(' ',1)[0])
# print predictions to stdout
# readpreds
def readpreds(filename):
preds = []
for line in open(filename):
preds += [float(line.strip())]
return preds
# if -h in args, print help
if "-h" in argv:
printhelp()
exit()
# check args
if len(argv) != 3:
print("illegal usage, incorrect number of arguments")
printhelp()
exit()
# get args
testfile = argv[1]
predfile = argv[2]
# read targets and predictions
labels, queries = readtargets(testfile)
preds = readpreds(predfile)
# compute and print metrics
rmse, err, ndcg = evaluate(preds, queries, labels)
print("rmse: %f, err: %f, ndcg: %f" % (rmse, err, ndcg))
preds = []
for line in open(filename):
preds += [float(line.strip())]
return preds
# if -h in args, print help
if '-h' in argv:
printhelp()
exit()
# check args
if len(argv) != 3:
print('illegal usage, incorrect number of arguments')
printhelp()
exit()
# get args
testfile = argv[1]
predfile = argv[2]
# read targets and predictions
labels,queries = readtargets(testfile)
preds = readpreds(predfile)
max_grade = max(labels)
# compute and print metrics
rmse,err,ndcg = evaluate(preds,queries,labels,max_grade=max_grade)
print("rmse: %f, err: %f, ndcg: %f" % (rmse,err,ndcg))
# readpreds
def readpreds(filename):
preds = []
for line in open(filename):
preds += [float(line.strip())]
return preds
# if -h in args, print help
if '-h' in argv:
printhelp()
exit()
# check args
if len(argv) != 3:
print('illegal usage, incorrect number of arguments')
printhelp()
exit()
# get args
testfile = argv[1]
predfile = argv[2]
# read targets and predictions
labels, queries = readtargets(testfile)
preds = readpreds(predfile)
# compute and print metrics
rmse, err, ndcg = evaluate(preds, queries, labels)
print("rmse: %f, err: %f, ndcg: %f" % (rmse, err, ndcg))
preds = []
for line in open(filename):
preds += [float(line.strip())]
return preds
# if -h in args, print help
if '-h' in argv:
printhelp()
exit()
# check args
if len(argv) != 3:
print('illegal usage, incorrect number of arguments')
printhelp()
exit()
# get args
testfile = argv[1]
predfile = argv[2]
# read targets and predictions
labels, queries = readtargets(testfile)
preds = readpreds(predfile)
max_grade = max(labels)
# compute and print metrics
rmse, err, ndcg = evaluate(preds, queries, labels, max_grade=max_grade)
print("rmse: %f, err: %f, ndcg: %f" % (rmse, err, ndcg))
