# Creating the Confusion Matrix
from sklearn import metrics
from sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_auc_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from sklearn.metrics import precision_score
from sklearn.metrics import cohen_kappa_score
from sklearn.metrics import balanced_accuracy_score
import math
cm = confusion_matrix(y_real, y_pred)
print(cm)
recall=recall_score(y_real, y_pred, average=None)
Acc=accuracy_score(y_real, y_pred)
Bacc=balanced_accuracy_score(y_real, y_pred)
Kap=cohen_kappa_score(y_real, y_pred)
AUC=roc_auc_score(y_real, y_pred)
F1=f1_score(y_real, y_pred)
GM=math.sqrt(recall[0]*recall[1])
#mean = ค่าเฉลี่ยของ Accuracy AUC F1 GM
mean=(Acc+AUC+F1+GM)/4
print("Acc:{:.6f}||Bacc:{:.6f}||AUC:{:.6f}||Kap:{:.6f}||rec:{:.6f}||spec:{:.6f}||pre:{:.6f}||F1:{:.6f}||GM={:.6f}||Mean:{:.6f}".format(Acc,Bacc,AUC,Kap,recall[0],recall[1],precision_score(y_real, y_pred),F1,GM,mean))