scalex.metrics.silhouette_score
- scalex.metrics.silhouette_score(X, labels, *, metric='euclidean', sample_size=None, random_state=None, **kwds)
Compute the mean Silhouette Coefficient of all samples.
The Silhouette Coefficient is calculated using the mean intra-cluster distance (
a
) and the mean nearest-cluster distance (b
) for each sample. The Silhouette Coefficient for a sample is(b - a) / max(a, b)
. To clarify,b
is the distance between a sample and the nearest cluster that the sample is not a part of. Note that Silhouette Coefficient is only defined if number of labels is2 <= n_labels <= n_samples - 1
.This function returns the mean Silhouette Coefficient over all samples. To obtain the values for each sample, use
silhouette_samples()
.The best value is 1 and the worst value is -1. Values near 0 indicate overlapping clusters. Negative values generally indicate that a sample has been assigned to the wrong cluster, as a different cluster is more similar.
Read more in the User Guide.
- Parameters:
X (array-like of shape (n_samples_a, n_samples_a) if metric == "precomputed" or (n_samples_a, n_features) otherwise) – An array of pairwise distances between samples, or a feature array.
labels (array-like of shape (n_samples,)) – Predicted labels for each sample.
metric (str or callable, default='euclidean') – The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options allowed by
metrics.pairwise.pairwise_distances
. IfX
is the distance array itself, usemetric="precomputed"
.sample_size (int, default=None) – The size of the sample to use when computing the Silhouette Coefficient on a random subset of the data. If
sample_size is None
, no sampling is used.random_state (int, RandomState instance or None, default=None) – Determines random number generation for selecting a subset of samples. Used when
sample_size is not None
. Pass an int for reproducible results across multiple function calls. See Glossary.**kwds (optional keyword parameters) – Any further parameters are passed directly to the distance function. If using a scipy.spatial.distance metric, the parameters are still metric dependent. See the scipy docs for usage examples.
- Returns:
silhouette – Mean Silhouette Coefficient for all samples.
- Return type:
float
References