This sample code works on binary classification to generatre True Positive Rate and False Positive Rate (TPR, FPR) by class.

tpr_fpr_binary_class.py

"""This sample code works on binary classification to generatre True Positive Rate and False Positive Rate (TPR, FPR)  by class. 
Yes denotes positive class and No denotes negative class. 
Default value 0 and 1.
Created by Ahmed Shahriar Sakib @ahmedshahriar on 06.08.2020 """

yes_tp = 0
yes_fp = 0
no_tp = 0
no_fp = 0
yes_instances =0
no_instances = 0
for i in range(len(predictions)):
  if y_test[i] == 0:
    no_instances+=1
    if predictions[i]==y_test[i]:
      no_tp+=1
    else:
      yes_fp+=1
    # print("for 0 : ",predictions[i]==y[i])

  if y_test[i] == 1:
    yes_instances+=1
    if predictions[i]==y_test[i]:
      yes_tp+=1
    else:
      no_fp+=1
    # print("for 1 : ",predictions[i]==y[i],y[i],y_test[i],predictions[i])

print("Yes Class -- TP : {} ,  FP : {} , Instances : {}\nNo Class -- TP : {} , FP : {} , Instances : {}".format(yes_tp,yes_fp,yes_instances,no_tp,no_fp,no_instances))

print("Yes class -- tpr : {},\tfpr : {} ".format(yes_tp/yes_instances,yes_fp/no_instances))
print("No class -- tpr : {},\tfpr : {} ".format(no_tp/no_instances, no_fp/yes_instances))