Files
2026-07-13 13:35:51 +08:00

26 lines
627 B
Python

from sklearn.metrics import accuracy_score, f1_score, log_loss, roc_auc_score
def evaluate_auc(pred, label):
res = roc_auc_score(y_score=pred, y_true=label)
return res
def evaluate_acc(pred, label):
res = []
for _value in pred:
res.append(1 if _value >= 0.5 else 0)
return accuracy_score(y_pred=res, y_true=label)
def evaluate_f1_score(pred, label):
res = []
for _value in pred:
res.append(1 if _value >= 0.5 else 0)
return f1_score(y_pred=res, y_true=label)
def evaluate_logloss(pred, label):
res = log_loss(y_true=label, y_pred=pred, normalize=True)
return res