def roc(scores_path, true_path, auroc_path, curve_path):
#Read in scores data
lines = filelines2deeplist(scores_path, skipComments=True, skipEmptyLines=True)
scores = {pair[0]:pair[1] for pair in lines}
#Read in true classification data
lines = filelines2deeplist(true_path, skipComments=True, skipEmptyLines=True)
true = {pair[0]:pair[1] for pair in lines}
data = list()
for seq, classification in true.items():
if classification == 'weak':
class_type = 0
elif classification == 'strong':
class_type = 1
else:
continue
if seq in scores:
data.append((class_type, scores[seq]))
else:
print 'Error: ' + seq + ' not found in results.'
import pyroc
roc_data = pyroc.ROCData(data)
if auroc_path is not None:
auroc = roc_data.auc()
list2file([auroc], auroc_path)
if curve_path is not None:
roc_data.plot_and_save(curve_path, 'STATIUM-based Binding Prediction Model')
def print_merged(score_path, true_path, out):
#Read in scores data
lines = filelines2deeplist(score_path, skipComments=True, skipEmptyLines=True)
scores = {pair[0]:pair[1] for pair in lines}
#Read in true classification data
lines = filelines2deeplist(true_path, skipComments=True, skipEmptyLines=True)
true = {pair[0]:pair[1] for pair in lines}
data = list()
for seq, classification in true.items():
if seq in scores:
data.append((seq, scores[seq], classification))
else:
data.append((seq, float('inf'), classification))
data.sort(key=lambda t: t[1])
with open(out, 'w') as f:
for datum in data:
f.write(datum[0] + '\t' + datum[1] + '\t' + datum[2]+ '\n')
