Cohesion¶
Mean distance from the points to their assigned cluster centroids. The smaller 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.Cohesion()
>>> 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
Cohesion: 1.682748
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¶
-
Bifet, A. et al. (2018). "Machine Learning for Data Streams". DOI: 10.7551/mitpress/10654.001.0001. ↩