sir, can you please help me to convert this python code into matlab

 #false positive rate,fpr= FP/(TN+FP) OR fpr=1-specificty, tpr=sensitivity 

 y_pred_knn_p =knn.predict_proba(X_test)[:,1]            

 models=[y_pred_knn_p]
 label=['KNN']

 # plotting ROC curves
 plt.figure(figsize=(10, 8))
 m=np.arange(1)
 for m in m:
    fpr, tpr,thresholds= metrics.roc_curve(y_test,models[m])
    print('model:',label[m])
    print('thresholds:',np.round(thresholds,3))
    print('tpr:       ',np.round(tpr,3))
    print('fpr:       ',np.round(fpr,3))
    plt.plot(fpr,tpr,label=label[m])
 plt.xlim([0.0,1.0])
 plt.ylim([0.0,1.0])
 plt.title('ROC curve for Cancer classifer')
 plt.xlabel('False positive rate (1-specificity)')
 plt.ylabel('True positive rate (sensitivity)')
 plt.legend(loc=4,)

Answer 1

[ANKUR KUMAR]

Commented lines are the python code and uncommented lines are the matlab codes. There are few python functions which you have use like predict_proba and roc_curve for which you have to write these function in matlab.

% y_pred_knn_p =knn.predict_proba(X_test)[:,1]  
y_pred_knn_p= predict_proba(X_test); y_pred_knn_p=y_pred_knn_p(:,2); % make sure that you must have predict_proba function.  python reads first row or column as 0 but matlab starts with 1. 
%  models=[y_pred_knn_p]
 models=[y_pred_knn_p];
%  label=['KNN']
label={'KNN'}
% %  # plotting ROC curves
%  plt.figure(figsize=(10, 8))
figure()
%  m=np.arange(1)
m=1;
%  for m in m:
for mm=1:length(m)
    mmm=m(mm);
%     fpr, tpr,thresholds= metrics.roc_curve(y_test,models[mmm])
  [fpr, tpr,thresholds]= roc_curve(y_test,models{mmm}) %if models is in cells then use models{m} otherwise model(:,mmm) can also be use as per your need.
%     print('model:',label[mmm])
disp(strcat('model:',label{mmm})) % one can use sprintf to the same
%     print('thresholds:',np.round(thresholds,3))
disp(strcat('thresholds:',num2str(round(thresholds,3)))) 
%     print('tpr:       ',np.round(tpr,3))
%     print('fpr:       ',np.round(fpr,3))
%     plt.plot(fpr,tpr,label=label[m])
plot(fpr,tpr)
leg=legend(label{mmm})
%  plt.xlim([0.0,1.0])
xlim([0 1])
%  plt.ylim([0.0,1.0])
ylim([0 1])
%  plt.title('ROC curve for Cancer classifer')
title('ROC curve for Cancer classifer')
%  plt.xlabel('False positive rate (1-specificity)')
xlabel('False positive rate (1-specificity)')
%  plt.ylabel('True positive rate (sensitivity)')
ylabel('True positive rate (sensitivity)')
%  plt.legend(loc=4,)
leg.Location='southeast';
end

Hope this helps you. If you are still facing problem, then comeup with some maltab code so that you can get help a bit more effectively.