Comments (2)
Hi! thanks for your contribution!, great first issue!
from torchmetrics.
Just noticed it was because of the number of samples 😅
The following example should solve
preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2, 0.1, 0.2, 0.5, 0.1, 0.3, 0.5, 0.1])
indexes = tensor([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1])
target = tensor([False, False, True, False, True, False, True, False, True, True, True, False, True, True])
Default top_k
ndcg = RetrievalNormalizedDCG()
print(f'top_k=default: {ndcg(preds, target, indexes=indexes)}')
output:
top_k=default: 0.903104305267334
top_k = 5
ndcg = RetrievalNormalizedDCG(top_k=5)
print(f'top_k=5: {ndcg(preds, target, indexes=indexes)}')
output:
top_k=5: 0.7864415645599365
top_k = 10
ndcg = RetrievalNormalizedDCG(top_k=10)
print(f'top_k=10: {ndcg(preds, target, indexes=indexes)}')
output:
top_k=10: 0.903104305267334
from torchmetrics.
Related Issues (20)
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from torchmetrics.