from sklearn.metrics import precision_recall_fscore_support as score
from sklearn.metrics import accuracy_score

predict_file = "SVM-result/output_c15_t0_unclassified_curated_celltype_intl2f2-4gram.txt"
f = open(predict_file,"r")
lines=f.readlines()
predict_label=[]
for line in lines[1:]:
	predict_label.append(int(line.split()[0]))
f.close()

true_file = "label_feature/c15_unclassified_curated_celltype_intl2.txt"
f = open(true_file,"r")
lines=f.readlines()
true_label=[]
for line in lines:
	true_label.append(int(line))
f.close()

precision,recall,fscore,support = score(true_label,predict_label, average='micro')
accuracy = accuracy_score(true_label, predict_label)

print('accuracy: {}'.format(accuracy))
print('precision: {}'.format(precision))
print('recall: {}'.format(recall))
print('fscore: {}'.format(fscore))
print('support: {}'.format(support))