v_measure_score

cuml.metrics.cluster.v_measure_score(labels_true, labels_pred, beta=1.0) → float

cython_v_measure(labels_true, labels_pred, beta=1.0) -> float

V-measure metric of a cluster labeling given a ground truth.

The V-measure is the harmonic mean between homogeneity and completeness:

v = (1 + beta) * homogeneity * completeness
     / (beta * homogeneity + completeness)

This metric is independent of the absolute values of the labels: a permutation of the class or cluster label values won’t change the score value in any way.

This metric is furthermore symmetric: switching label_true with label_pred will return the same score value. This can be useful to measure the agreement of two independent label assignments strategies on the same dataset when the real ground truth is not known.

Parameters:

labels_predarray-like (device or host) shape = (n_samples,)

The labels predicted by the model for the test dataset. Acceptable formats: cuDF DataFrame, NumPy ndarray, Numba device ndarray, cuda array interface compliant array like CuPy

labels_truearray-like (device or host) shape = (n_samples,)

The ground truth labels (ints) of the test dataset. Acceptable formats: cuDF DataFrame, NumPy ndarray, Numba device ndarray, cuda array interface compliant array like CuPy

betafloat, default=1.0

Ratio of weight attributed to homogeneity vs completeness. If beta is greater than 1, completeness is weighted more strongly in the calculation. If beta is less than 1, homogeneity is weighted more strongly.

Returns:

v_measure_valuefloat

score between 0.0 and 1.0. 1.0 stands for perfectly complete labeling