Source code for catalyst.metrics.functional._hitrate

from typing import List

import torch

from catalyst.metrics.functional._misc import process_recsys_components

def _nan_to_num(tensor, nan=0.0):
    tensor = torch.where(torch.isnan(tensor), torch.ones_like(tensor) * nan, tensor)
    return tensor

# nan_to_num is available in PyTorch only from 1.8.0 version
NAN_TO_NUM_FN = torch.__dict__.get("nan_to_num", _nan_to_num)

[docs]def hitrate( outputs: torch.Tensor, targets: torch.Tensor, topk: List[int], zero_division: int = 0 ) -> List[torch.Tensor]: """ Calculate the hit rate (aka recall) score given model outputs and targets. Hit-rate is a metric for evaluating ranking systems. Generate top-N recommendations and if one of the recommendation is actually what user has rated, you consider that a hit. By rate we mean any explicit form of user's interactions. Add up all of the hits for all users and then divide by number of users Compute top-N recommendation for each user in the training stage and intentionally remove one of this items fro the training data. Args: outputs (torch.Tensor): Tensor with predicted score size: [batch_size, slate_length] model outputs, logits targets (torch.Tensor): Binary tensor with ground truth. 1 means the item is relevant for the user and 0 not relevant size: [batch_size, slate_length] ground truth, labels topk (List[int]): Parameter fro evaluation on top-k items zero_division (int): value, returns in the case of the divison by zero should be one of 0 or 1 Returns: hitrate_at_k (List[torch.Tensor]): the hitrate score Example: .. code-block:: python import torch from catalyst import metrics metrics.hitrate( outputs=torch.Tensor([[4.0, 2.0, 3.0, 1.0], [1.0, 2.0, 3.0, 4.0]]), targets=torch.Tensor([[0, 0, 1.0, 1.0], [0, 0, 0.0, 0.0]]), topk=[1, 2, 3, 4], ) # [tensor(0.), tensor(0.2500), tensor(0.2500), tensor(0.5000)] """ results = [] targets_sort_by_outputs = process_recsys_components(outputs, targets) for k in topk: k = min(outputs.size(1), k) hits_score = torch.sum(targets_sort_by_outputs[:, :k], dim=1) / targets.sum(dim=1) hits_score = NAN_TO_NUM_FN(hits_score, zero_division) results.append(torch.mean(hits_score)) return results
__all__ = ["hitrate"]