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computeAccuracy_nfold.py
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computeAccuracy_nfold.py
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#from CPNet import CPNet
#a=CPNet()
#a.initFromFile("xml/examples5_alldiff/cpnet_n5c4d2_0014.xml")
#a.getPartialOrder()
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, cohen_kappa_score
import numpy as np
f1_mean=0.0
k_mean=0.0
mae = 0.0
folds = 10
dim=4
all_results = []
fscores=[]
dir="/home/aloreggi/cpnet/xml/lists/results/examples"+str(dim)+"/"
for i in range(folds):
file="test_PRETRAIN_NOPOOL_UNBALANCED_DIM_"+str(dim)+"_11_70_FOLD_"+str(i)+".txt.npy"
#print(file)
l=np.load(dir+file)
#print(np.shape(l))
if all_results == []:
all_results = l
else:
all_results = np.concatenate((all_results,l),axis=0)
'''
print(np.sum(l[:,0]==l[:,1],axis=0))
print(len(l))
mae += (float(np.sum(abs(l[:,0]-l[:,1]),axis=0)) / len(l))
'''
print("F1-score: \t" + str(f1_score(l[:,0], l[:,1],average="micro")))
fscores.append(f1_score(l[:,0], l[:,1],average="micro"))
print("Cohen k: \t" + str(cohen_kappa_score(l[:,0], l[:,1])))
print("")
mae += (float(np.sum(abs(l[:,0]-l[:,1]),axis=0)) / len(l))
print("F1-score: \t" + str(f1_score(all_results[:,0], all_results[:,1],average="micro")))
print("Cohen k: \t" + str(cohen_kappa_score(all_results[:,0], all_results[:,1])))
print("MAE: \t\t"+ str(mae / float(folds)))
print("STD: \t\t"+str(np.std(fscores)))