KMedoids

class baybe.utils.clustering_algorithms.third_party.kmedoids.KMedoids[source]

Bases: BaseEstimator, ClusterMixin, TransformerMixin

k-medoids clustering.

Parameters:

  • n_clusters – int, optional, default: 8 The number of clusters to form as well as the number of medoids to generate.

  • metric – string, or callable, optional, default: ‘euclidean’ What distance metric to use. See :func:metrics.pairwise_distances metric can be ‘precomputed’, the user must then feed the fit method with a precomputed kernel matrix and not the design matrix X.

  • method – {‘alternate’, ‘pam’}, default: ‘alternate’ Which algorithm to use. ‘alternate’ is faster while ‘pam’ is more accurate.

  • init – {‘random’, ‘heuristic’, ‘k-medoids++’, ‘build’} or array-like, default: ‘heuristic’

  • max_iter – int, optional, default : 300 Specify the maximum number of iterations when fitting. It can be zero in which case only the initialization is computed which may be suitable for large datasets when the initialization is sufficiently efficient (i.e. for ‘build’ init).

  • random_state – int, RandomState instance or None, optional Specify random state for the random number generator. Used to initialise medoids when init=’random’.

cluster_centers_
array, shape = (n_clusters, n_features)

or None if metric == ‘precomputed’

Cluster centers, i.e. medoids (elements from the original dataset)

medoid_indices_

array, shape = (n_clusters,) The indices of the medoid rows in X

labels_

array, shape = (n_samples,) Labels of each point

inertia_

float Sum of distances of samples to their closest cluster center.

Examples

>>> from baybe.utils.clustering_algorithms import KMedoids
>>> import numpy as np

>>> X = np.asarray([[1, 2], [1, 4], [1, 0],
...                 [4, 2], [4, 4], [4, 0]])
>>> kmedoids = KMedoids(n_clusters=2, random_state=0).fit(X)
>>> kmedoids.labels_
array([0, 0, 0, 1, 1, 1])
>>> kmedoids.predict([[0,0], [4,4]])
array([0, 1])
>>> kmedoids.cluster_centers_
array([[1., 2.],
       [4., 2.]])
>>> kmedoids.inertia_
8.0

See scikit-learn-extra/examples/plot_kmedoids_digits.py for examples of KMedoids with various distance metrics.

References

Maranzana, F.E., 1963. On the location of supply points to minimize

transportation costs. IBM Systems Journal, 2(2), pp.129-135.

Park, H.S.and Jun, C.H., 2009. A simple and fast algorithm for K-medoids

clustering. Expert systems with applications, 36(2), pp.3336-3341.

See also

KMeans

The KMeans algorithm minimizes the within-cluster sum-of-squares criterion. It scales well to large number of samples.

Notes

Since all pairwise distances are calculated and stored in memory for the duration of fit, the space complexity is O(n_samples ** 2).

Public methods

__init__([n_clusters, metric, method, init, ...])

fit(X[, y])

Fit K-Medoids to the provided data.

fit_predict(X[, y])

Perform clustering on X and returns cluster labels.

fit_transform(X[, y])

Fit to data, then transform it.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

predict(X)

Predict the closest cluster for each sample in X.

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

transform(X)

Transforms X to cluster-distance space.
__init__(n_clusters=8, metric='euclidean', method='alternate', init='heuristic', max_iter=300, random_state=None)[source]
fit(X, y=None)[source]

Fit K-Medoids to the provided data.

Parameters:

  • X – {array-like, sparse matrix}, shape = (n_samples, n_features), or (n_samples, n_samples) if metric == ‘precomputed’ Dataset to cluster.

  • y – Ignored

Returns:

self

fit_predict(X, y=None, **kwargs)

Perform clustering on X and returns cluster labels.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – Input data.

  • y (Ignored) – Not used, present for API consistency by convention.

  • **kwargs (dict) –

    Arguments to be passed to fit.

    Added in version 1.4.

Returns:

labels – Cluster labels.

Return type:

ndarray of shape (n_samples,), dtype=np.int64

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – Input samples.

  • y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).

  • **fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

ndarray array of shape (n_samples, n_features_new)

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

predict(X)[source]

Predict the closest cluster for each sample in X.

Parameters:

X – {array-like, sparse matrix}, shape (n_query, n_features), or (n_query, n_indexed) if metric == ‘precomputed’ New data to predict.

Returns
labelsarray, shape = (n_query,)

Index of the cluster each sample belongs to.

set_output(*, transform=None)

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform ({"default", "pandas", "polars"}, default=None) –

Configure output of transform and fit_transform.

  • ”default”: Default output format of a transformer

  • ”pandas”: DataFrame output

  • ”polars”: Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

self – Estimator instance.

Return type:

estimator instance

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance

transform(X)[source]

Transforms X to cluster-distance space.

Parameters:

  • X ({array-like, sparse matrix}, shape (n_query, n_features), or (n_query, n_indexed) if metric == 'precomputed') – Data to transform.

  • Returns

  • -------

  • X_new ({array-like, sparse matrix}, shape=(n_query, n_clusters)) – X transformed in the new space of distances to cluster centers.