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- from .imports import *
- from .torch_imports import *
- def fbeta_torch(y_true, y_pred, beta, threshold, eps=1e-9):
- y_pred = (y_pred.float() > threshold).float()
- y_true = y_true.float()
- tp = (y_pred * y_true).sum(dim=1)
- precision = tp / (y_pred.sum(dim=1)+eps)
- recall = tp / (y_true.sum(dim=1)+eps)
- return torch.mean(
- precision*recall / (precision*(beta**2)+recall+eps) * (1+beta**2))
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