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Separation

Average distance from a point to the points assigned to other clusters. The bigger the better.

Attributes

  • bigger_is_better

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

Examples

>>> from river import cluster
>>> from river import stream
>>> from river import metrics

>>> X = [
...     [1, 2],
...     [1, 4],
...     [1, 0],
...     [4, 2],
...     [4, 4],
...     [4, 0],
...     [-2, 2],
...     [-2, 4],
...     [-2, 0]
... ]

>>> k_means = cluster.KMeans(n_clusters=3, halflife=0.4, sigma=3, seed=0)
>>> metric = metrics.cluster.Separation()

>>> for x, _ in stream.iter_array(X):
...     k_means = k_means.learn_one(x)
...     y_pred = k_means.predict_one(x)
...     metric = metric.update(x, y_pred, k_means.centers)

>>> metric
Separation: 4.54563

Methods

get

Return the current value of the metric.

revert

Revert the metric.

Parameters

  • x (Dict[Hashable, numbers.Number])
  • y_pred (numbers.Number)
  • centers
  • sample_weight – defaults to 1.0
update

Update the metric.

Parameters

  • x (Dict[Hashable, numbers.Number])
  • y_pred (numbers.Number)
  • centers
  • 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. Bifet, A. et al. (2018). "Machine Learning for Data Streams". DOI: 10.7551/mitpress/10654.001.0001. ↩