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: pe tasks #2988

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18 changes: 11 additions & 7 deletions torchrec/metrics/auc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,13 +68,17 @@ def _compute_auc_helper(
# TODO - [add flag to set bining dyamically] for use with soft labels, >=0.039 --> 1, <0.039 --> 0
sorted_labels = torch.ge(sorted_labels, 0.039).to(dtype=sorted_labels.dtype)
sorted_weights = torch.index_select(weights, dim=0, index=sorted_indices)
cum_fp = torch.cumsum(sorted_weights * (1.0 - sorted_labels), dim=0)
cum_tp = torch.cumsum(sorted_weights * sorted_labels, dim=0)
auc = torch.where(
cum_fp[-1] * cum_tp[-1] == 0,
0.5, # 0.5 is the no-signal default value for auc.
torch.trapz(cum_tp, cum_fp) / cum_fp[-1] / cum_tp[-1],
)
if sorted_weights.numel() > 0:
cum_fp = torch.cumsum(sorted_weights * (1.0 - sorted_labels), dim=0)
cum_tp = torch.cumsum(sorted_weights * sorted_labels, dim=0)
auc = torch.where(
cum_fp[-1] * cum_tp[-1] == 0,
0.5, # 0.5 is the no-signal default value for auc.
torch.trapz(cum_tp, cum_fp) / cum_fp[-1] / cum_tp[-1],
)
else:
# if empty predictions, default value
auc = torch.tensor(0.5, device=sorted_weights.device)
return auc


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