# src.evaluation_metric.clustering_accuracy¶

src.evaluation_metric.clustering_accuracy(pred_clusters: torch.Tensor, gt_classes: torch.Tensor) torch.Tensor[source]

Clustering accuracy for clusters.

$$\mathcal{A}, \mathcal{B}, ...$$ are ground truth classes and $$\mathcal{A}^\prime, \mathcal{B}^\prime, ...$$ are predicted classes and $$k$$ is the number of classes:

$\text{clustering accuracy} = 1 - \frac{1}{k} \left(\sum_{\mathcal{A}} \sum_{\mathcal{A}^\prime \neq \mathcal{B}^\prime} \frac{|\mathcal{A}^\prime \cap \mathcal{A}| |\mathcal{B}^\prime \cap \mathcal{A}|}{|\mathcal{A}| |\mathcal{A}|} + \sum_{\mathcal{A}^\prime} \sum_{\mathcal{A} \neq \mathcal{B}} \frac{|\mathcal{A}^\prime \cap \mathcal{A}| |\mathcal{A}^\prime \cap \mathcal{B}|}{|\mathcal{A}| |\mathcal{B}|} \right)$
Parameters
• pred_clusters

$$(b\times n)$$ predicted clusters. $$n$$: number of instances.

e.g. [[0,0,1,2,1,2]
[0,1,2,2,1,0]]


• gt_classes

$$(b\times n)$$ ground truth classes

e.g. [['car','car','bike','bike','person','person'],
['bus','bus','cat', 'sofa',  'cat',  'sofa' ]]


Returns

$$(b)$$ clustering accuracy