python - How to compute auc score manually without using sklearn?

Question

I want to compute auc_score with out using sklearn.

I have a csv file with 2 columns (actual,predicted(probability)). And I want to compute auc score using numpy.trapz() function .

And here is my code

from tqdm import tqdm
def AUC_SCORE(x):
  t=[]
  f=[]
  x=x.sort_values(by=["proba"],ascending=False)
  for t in tqdm(x["proba"].unique()):
    x['y_pred'] =np.where( x['proba']>=t,1,0)
    tp=(x["y"]==1)&(x["y_pred"]==1).sum()
    fp=(x["y"]==0)&(x["y_pred"]==1).sum()
    tn=(x["y"]==0)&(x["y_pred"]==0).sum()
    fn=(x["y"]==1)&(x["y_pred"]==0).sum()
    tpr= tp/(fp+fn)
    fpr= fp/(tn+fp)
    t.append(tpr)
    f.append(fpr)
  return np.trapz(t,f)
e=AUC_SCORE(a)

and i have around 10100 points and it almost takes above 1 hr using google colab. and i din't get my result and i am getting errors while modifying my code. is there there any better/any way to compute auc score with out using sklearn.

Answer 1

The problem with your implementation seems to be here:

x=x.sort_values(by=["proba"],ascending=False)
for t in tqdm(x["proba"].unique()):

You seem to get through each unique values of probabilities, but these are in range 0-1 (probably) and are most likely barely unique, which leads to very long run. You need to translate probability into the label. If you are using binary labels (which from your attempt seems so), you can do following list comprehension:

df["prediction"] = [0 if x<0.5 else 1 for x in df["proba"]]

This way you translate the probability to label and then can sort according to prediction and use unique values in predictions. If you use multilabel predictions, you can extend the above condition according to your needs.