f4beaac559e00a3676d942dc7e8fea69efc01cfe,catalyst/metrics/ndcg.py,,dcg,#,9

Before Change

            torch.arange(true_sorted_by_preds.shape[1], dtype=torch.float)
            + 2.0
        )
        discounted_gains = (gains * discounts)[:, :k]

    elif gain_function == "rank":
        discounts = torch.tensor(1) / torch.log2(

After Change

    Raises:
        ValueError: gain function can be either `pow_rank` or `rank`
    
    targets_sort_by_outputs = process_recsys_components(outputs, targets)
    target_device = targets_sort_by_outputs.device

    if gain_function == "exp_rank":
        gain_function = lambda x: torch.pow(2, x) - 1
        gains = gain_function(targets_sort_by_outputs)

Instances