Cosine Similarity¶

Functional Interface¶

torchmetrics.functional.pairwise_cosine_similarity(x, y=None, reduction=None, zero_diagonal=None)[source]¶

Calculate pairwise cosine similarity.

\[s_{cos}(x,y) = \frac{<x,y>}{||x|| \cdot ||y||} = \frac{\sum_{d=1}^D x_d \cdot y_d }{\sqrt{\sum_{d=1}^D x_i^2} \cdot \sqrt{\sum_{d=1}^D y_i^2}}\]

If both \(x\) and \(y\) are passed in, the calculation will be performed pairwise between the rows of \(x\) and \(y\). If only \(x\) is passed in, the calculation will be performed between the rows of \(x\).

Parameters:

x¶ (Tensor) – Tensor with shape [N, d]
y¶ (Optional[Tensor]) – Tensor with shape [M, d], optional
reduction¶ (Optional[Literal['mean', 'sum', 'none', None]]) – reduction to apply along the last dimension. Choose between ‘mean’, ‘sum’ (applied along column dimension) or ‘none’, None for no reduction
zero_diagonal¶ (Optional[bool]) – if the diagonal of the distance matrix should be set to 0. If only \(x\) is given this defaults to True else if \(y\) is also given it defaults to False

Return type:

Tensor

Returns:

A [N,N] matrix of distances if only x is given, else a [N,M] matrix

Example

>>> import torch
>>> from torchmetrics.functional.pairwise import pairwise_cosine_similarity
>>> x = torch.tensor([[2, 3], [3, 5], [5, 8]], dtype=torch.float32)
>>> y = torch.tensor([[1, 0], [2, 1]], dtype=torch.float32)
>>> pairwise_cosine_similarity(x, y)
tensor([[0.5547, 0.8682],
        [0.5145, 0.8437],
        [0.5300, 0.8533]])
>>> pairwise_cosine_similarity(x)
tensor([[0.0000, 0.9989, 0.9996],
        [0.9989, 0.0000, 0.9998],
        [0.9996, 0.9998, 0.0000]])