contingency_matrix (original) (raw)

sklearn.metrics.cluster.contingency_matrix(labels_true, labels_pred, *, eps=None, sparse=False, dtype=<class 'numpy.int64'>)[source]#

Build a contingency matrix describing the relationship between labels.

Read more in the User Guide.

Parameters:

labels_truearray-like of shape (n_samples,)

Ground truth class labels to be used as a reference.

labels_predarray-like of shape (n_samples,)

Cluster labels to evaluate.

epsfloat, default=None

If a float, that value is added to all values in the contingency matrix. This helps to stop NaN propagation. If None, nothing is adjusted.

sparsebool, default=False

If True, return a sparse CSR contingency matrix. If eps is notNone and sparse is True will raise ValueError.

Added in version 0.18.

dtypenumeric type, default=np.int64

Output dtype. Ignored if eps is not None.

Added in version 0.24.

Returns:

contingency{array-like, sparse}, shape=[n_classes_true, n_classes_pred]

Matrix \(C\) such that \(C_{i, j}\) is the number of samples in true class \(i\) and in predicted class \(j\). Ifeps is None, the dtype of this array will be integer unless set otherwise with the dtype argument. If eps is given, the dtype will be float. Will be a sklearn.sparse.csr_matrix if sparse=True.

Examples

from sklearn.metrics.cluster import contingency_matrix labels_true = [0, 0, 1, 1, 2, 2] labels_pred = [1, 0, 2, 1, 0, 2] contingency_matrix(labels_true, labels_pred) array([[1, 1, 0], [0, 1, 1], [1, 0, 1]])