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VBeta

VBeta.

VBeta (or V-Measure) 1 is an external entropy-based cluster evaluation measure. It provides an elegant solution to many problems that affect previously defined cluster evaluation measures including

  • Dependance of clustering algorithm or dataset,

  • The "problem of matching", where the clustering of only a portion of data points are evaluated, and

  • Accurate evaluation and combination of two desirable aspects of clustering, homogeneity and completeness.

Based upon the calculations of homogeneity and completeness, a clustering solution's V-measure is calculated by computing the weighted harmonic mean of homogeneity and completeness,

\[ V_{\beta} = \frac{(1 + \beta) \times h \times c}{\beta \times h + c}. \]

Parameters

  • beta (float) – defaults to 1.0

    Weight of Homogeneity in the harmonic mean.

  • cm – defaults to None

    This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.

Attributes

  • bigger_is_better

    Indicate if a high value is better than a low one or not.

  • requires_labels

    Indicates if labels are required, rather than probabilities.

  • works_with_weights

    Indicate whether the model takes into consideration the effect of sample weights

Examples

>>> from river import metrics

>>> y_true = [1, 1, 2, 2, 3, 3]
>>> y_pred = [1, 1, 1, 2, 2, 2]

>>> metric = metrics.VBeta(beta=1.0)
>>> for yt, yp in zip(y_true, y_pred):
...     print(metric.update(yt, yp).get())
1.0
1.0
0.0
0.3437110184854507
0.4580652856440158
0.5158037429793888

>>> metric
VBeta: 51.58%

Methods

get

Return the current value of the metric.

is_better_than
revert

Revert the metric.

Parameters

  • y_true
  • y_pred
  • sample_weight – defaults to 1.0
update

Update the metric.

Parameters

  • y_true
  • y_pred
  • sample_weight – defaults to 1.0
works_with

Indicates whether or not a metric can work with a given model.

Parameters

  • model (river.base.estimator.Estimator)

References

  1. Andrew Rosenberg and Julia Hirschberg (2007). V-Measure: A conditional entropy-based external cluster evaluation measure. Proceedings of the 2007 Joing Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 410 - 420, Prague, June 2007. ↩