wehub-resource-sync
47 lines
1.7 KiB
Python
47 lines
1.7 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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__all__ = ["BPRLoss"]
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class BPRLoss(nn.Module):
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r"""This criterion computes the Bayesian Personalized Ranking (BPR) loss between the positive scores and the negative scores.
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Parameters:
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``alpha`` (``float``, optional): The weight for the positive scores in the BPR loss. Defaults to ``1.0``.
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``beta`` (``float``, optional): The weight for the negative scores in the BPR loss. Defaults to ``1.0``.
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``activation`` (``str``, optional): The activation function to use can be one of ``"sigmoid_then_log"``, ``"softplus"``. Defaults to ``"sigmoid_then_log"``.
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"""
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def __init__(
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self,
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alpha: float = 1.0,
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beta: float = 1.0,
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activation: str = "sigmoid_then_log",
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):
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super().__init__()
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assert activation in (
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"sigmoid_then_log",
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"softplus",
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), "activation function of BPRLoss must be sigmoid_then_log or softplus."
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self.activation = activation
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self.alpha = alpha
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self.beta = beta
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def forward(self, pos_scores: torch.Tensor, neg_scores: torch.Tensor):
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r"""The forward function of BPRLoss.
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Parameters:
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``pos_scores`` (``torch.Tensor``): The positive scores.
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``neg_scores`` (``torch.Tensor``): The negative scores.
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"""
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if self.activation == "sigmoid_then_log":
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loss = -(self.alpha * pos_scores - self.beta * neg_scores).sigmoid().log()
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elif self.activation == "softplus":
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loss = F.softplus(self.beta * neg_scores - self.alpha * pos_scores)
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else:
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raise NotImplementedError
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return loss.mean()
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