Mismatch of manual computation of a evaluation metrics with Sklearn functions

I wanted to compare the manual computations of the precision and recall with scikit-learn functions. However, recall_score() and precision_score() of scikit-learn functions gave me different results. Not sure why! Could you please give me some advice why I am getting different results? Thanks!

My confusion matrix:

tp, fn, fp, tn = confusion_matrix(y_test, y_test_pred).ravel()
print('Outcome values : n', tp, fn, fp, tn)

Outcome values : 
 3636933 34156 127 151

FDR=tp/(tp+fn) # TPR/Recall/Sensitivity
print('Recall: %.3f' % FDR)

Recall: 0.991

precision=tp/(tp + fp)
print('Precision: %.3f' % precision)

Precision: 1.000

precision = precision_score(y_test, y_test_pred)
print('Precision: %f' % precision)
recall = recall_score(y_test, y_test_pred)
print('Recall: %f' % recall)

Precision: 0.004401
Recall: 0.543165

Answer

It should be (check return value’s ordering):

tn, fp, fn, tp = confusion_matrix(y_test, y_test_pred).ravel()

Please refer: here