Convert a custom accuracy function into torchmetric?

[Abhinav43]

Hello, Thank you for this awesome project. I am new to use torchmetric, and I have one doubt regarding the custom function.

So if I have my own function to calculate the accuracy, such as :

From sklearn.metrics import f1_score

def get_hat(y_hat, threshold):
    hat_y = np.where(np.array(y_hat) > threshold, 1, 0)
    return hat_y

def get_acc(pred_y, y, threshold):

  if type(threshold) !=list:
    threshold = [threshold]
  
  final = {'acc': [], 'thres': []}
  for k in threshold:

    pred_ = get_hat(pred_y, k)
    real_ = y

    # some custom accuracy function defined somewhere else in file
    # just for example I am using sklearn f1-score

    acc   = f1_score(real_, pred_, average='macro')
    final['acc'].append(acc)
    final['thres'].append(k)

  return pd.DataFrame(final)

How I can convert this function into torch metric where all the internal logic will be from my function, Just want to wrap my logic in torch metric.

Thanks!

[SkafteNicki]

Hi @Abhinav43,
We have this page in the documentation: https://torchmetrics.readthedocs.io/en/latest/pages/implement.html which should help you get stated (I am assuming that you want to turn it into a modular metric). If this does not help you then feel free to reach out to me again.