Cosine Similarity

Module Interface

class torchmetrics.CosineSimilarity(reduction='sum', **kwargs)

Compute the Cosine Similarity.

\[cos_{sim}(x,y) = \frac{x \cdot y}{||x|| \cdot ||y||} = \frac{\sum_{i=1}^n x_i y_i}{\sqrt{\sum_{i=1}^n x_i^2}\sqrt{\sum_{i=1}^n y_i^2}}\]

where \(y\) is a tensor of target values, and \(x\) is a tensor of predictions.

As input to forward and update the metric accepts the following input:

• preds (Tensor): Predicted float tensor with shape (N,d)

• target (Tensor): Ground truth float tensor with shape (N,d)

As output of forward and compute the metric returns the following output:

• cosine_similarity (Tensor): A float tensor with the cosine similarity

Parameters:

• reduction (Literal['mean', 'sum', 'none', None]) – how to reduce over the batch dimension using ‘sum’, ‘mean’ or ‘none’ (taking the individual scores)

• kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Example

>>> from torch import tensor
>>> from torchmetrics.regression import CosineSimilarity
>>> target = tensor([[0, 1], [1, 1]])
>>> preds = tensor([[0, 1], [0, 1]])
>>> cosine_similarity = CosineSimilarity(reduction = 'mean')
>>> cosine_similarity(preds, target)
tensor(0.8536)

plot(val=None, ax=None)

Plot a single or multiple values from the metric.

Parameters:

• val (Union[Tensor, Sequence[Tensor], None]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.

• ax (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type:

tuple[Figure, Union[Axes, ndarray]]

Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> from torch import randn
>>> # Example plotting a single value
>>> from torchmetrics.regression import CosineSimilarity
>>> metric = CosineSimilarity()
>>> metric.update(randn(10,2), randn(10,2))
>>> fig_, ax_ = metric.plot()

>>> from torch import randn
>>> # Example plotting multiple values
>>> from torchmetrics.regression import CosineSimilarity
>>> metric = CosineSimilarity()
>>> values = []
>>> for _ in range(10):
...     values.append(metric(randn(10,2), randn(10,2)))
>>> fig, ax = metric.plot(values)

Functional Interface

torchmetrics.functional.cosine_similarity(preds, target, reduction='sum')

Compute the Cosine Similarity.

\[cos_{sim}(x,y) = \frac{x \cdot y}{||x|| \cdot ||y||} = \frac{\sum_{i=1}^n x_i y_i}{\sqrt{\sum_{i=1}^n x_i^2}\sqrt{\sum_{i=1}^n y_i^2}}\]

where \(y\) is a tensor of target values, and \(x\) is a tensor of predictions.

Parameters:

• preds (Tensor) – Predicted tensor with shape (N,d)

• target (Tensor) – Ground truth tensor with shape (N,d)

• reduction (Optional[str]) – The method of reducing along the batch dimension using sum, mean or taking the individual scores

Return type:

Tensor

Example

>>> from torchmetrics.functional.regression import cosine_similarity
>>> target = torch.tensor([[1, 2, 3, 4],
...                        [1, 2, 3, 4]])
>>> preds = torch.tensor([[1, 2, 3, 4],
...                       [-1, -2, -3, -4]])
>>> cosine_similarity(preds, target, 'none')
tensor([ 1.0000, -1.0000])