sklearn.metrics.cluster.pair_confusion_matrix

sklearn.metrics.cluster.pair_confusion_matrix(labels_true, labels_pred)

Pair confusion matrix arising from two clusterings [1].

The pair confusion matrix \(C\) computes a 2 by 2 similarity matrix between two clusterings by considering all pairs of samples and counting pairs that are assigned into the same or into different clusters under the true and predicted clusterings.

Considering a pair of samples that is clustered together a positive pair, then as in binary classification the count of true negatives is \(C_{00}\), false negatives is \(C_{10}\), true positives is \(C_{11}\) and false positives is \(C_{01}\).

Read more in the User Guide.

Parameters:

labels_truearray-like of shape (n_samples,), dtype=integral

Ground truth class labels to be used as a reference.

labels_predarray-like of shape (n_samples,), dtype=integral

Cluster labels to evaluate.

Returns:

Cndarray of shape (2, 2), dtype=np.int64

The contingency matrix.

See also

sklearn.metrics.rand_score

Rand Score.

sklearn.metrics.adjusted_rand_score

Adjusted Rand Score.

sklearn.metrics.adjusted_mutual_info_score

Adjusted Mutual Information.

References

[1]

Hubert, L., Arabie, P. “Comparing partitions.” Journal of Classification 2, 193–218 (1985).

Examples

Perfectly matching labelings have all non-zero entries on the diagonal regardless of actual label values:

>>> from sklearn.metrics.cluster import pair_confusion_matrix
>>> pair_confusion_matrix([0, 0, 1, 1], [1, 1, 0, 0])
array([[8, 0],
       [0, 4]]...

Labelings that assign all classes members to the same clusters are complete but may be not always pure, hence penalized, and have some off-diagonal non-zero entries:

>>> pair_confusion_matrix([0, 0, 1, 2], [0, 0, 1, 1])
array([[8, 2],
       [0, 2]]...

Note that the matrix is not symmetric.