`sklearn.cluster`.AffinityPropagation¶

class sklearn.cluster.AffinityPropagation(damping=0.5, max_iter=200, convergence_iter=15, copy=True, preference=None, affinity='euclidean', verbose=False)[source]¶

Perform Affinity Propagation Clustering of data.

Read more in the User Guide.

Parameters

dampingfloat, optional, default: 0.5: Damping factor (between 0.5 and 1) is the extent to which the current value is maintained relative to incoming values (weighted 1 - damping). This in order to avoid numerical oscillations when updating these values (messages).
max_iterint, optional, default: 200: Maximum number of iterations.
convergence_iterint, optional, default: 15: Number of iterations with no change in the number of estimated clusters that stops the convergence.
copyboolean, optional, default: True: Make a copy of input data.
preferencearray-like, shape (n_samples,) or float, optional: Preferences for each point - points with larger values of preferences are more likely to be chosen as exemplars. The number of exemplars, ie of clusters, is influenced by the input preferences value. If the preferences are not passed as arguments, they will be set to the median of the input similarities.
affinitystring, optional, default=``euclidean``: Which affinity to use. At the moment precomputed and euclidean are supported. euclidean uses the negative squared euclidean distance between points.
verboseboolean, optional, default: False: Whether to be verbose.

Attributes

cluster_centers_indices_array, shape (n_clusters,): Indices of cluster centers
cluster_centers_array, shape (n_clusters, n_features): Cluster centers (if affinity != precomputed).
labels_array, shape (n_samples,): Labels of each point
affinity_matrix_array, shape (n_samples, n_samples): Stores the affinity matrix used in fit.
n_iter_int: Number of iterations taken to converge.

Notes

For an example, see examples/cluster/plot_affinity_propagation.py.

The algorithmic complexity of affinity propagation is quadratic in the number of points.

When fit does not converge, cluster_centers_ becomes an empty array and all training samples will be labelled as -1. In addition, predict will then label every sample as -1.

When all training samples have equal similarities and equal preferences, the assignment of cluster centers and labels depends on the preference. If the preference is smaller than the similarities, fit will result in a single cluster center and label 0 for every sample. Otherwise, every training sample becomes its own cluster center and is assigned a unique label.

References

Brendan J. Frey and Delbert Dueck, “Clustering by Passing Messages Between Data Points”, Science Feb. 2007

Examples

>>> from sklearn.cluster import AffinityPropagation
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
...               [4, 2], [4, 4], [4, 0]])
>>> clustering = AffinityPropagation().fit(X)
>>> clustering
AffinityPropagation()
>>> clustering.labels_
array([0, 0, 0, 1, 1, 1])
>>> clustering.predict([[0, 0], [4, 4]])
array([0, 1])
>>> clustering.cluster_centers_
array([[1, 2],
       [4, 2]])

Methods

`fit`(self, X[, y])	Fit the clustering from features, or affinity matrix.
`fit_predict`(self, X[, y])	Fit the clustering from features or affinity matrix, and return cluster labels.
`get_params`(self[, deep])	Get parameters for this estimator.
`predict`(self, X)	Predict the closest cluster each sample in X belongs to.
`set_params`(self, \\params)	Set the parameters of this estimator.

__init__(self, damping=0.5, max_iter=200, convergence_iter=15, copy=True, preference=None, affinity='euclidean', verbose=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

fit(self, X, y=None)[source]¶

Fit the clustering from features, or affinity matrix.

Parameters

Xarray-like or sparse matrix, shape (n_samples, n_features), or array-like, shape (n_samples, n_samples): Training instances to cluster, or similarities / affinities between instances if affinity='precomputed'. If a sparse feature matrix is provided, it will be converted into a sparse csr_matrix.
yIgnored: Not used, present here for API consistency by convention.

Returns

self

fit_predict(self, X, y=None)[source]¶

Fit the clustering from features or affinity matrix, and return cluster labels.

Parameters

Xarray-like or sparse matrix, shape (n_samples, n_features), or array-like, shape (n_samples, n_samples): Training instances to cluster, or similarities / affinities between instances if affinity='precomputed'. If a sparse feature matrix is provided, it will be converted into a sparse csr_matrix.
yIgnored: Not used, present here for API consistency by convention.

Returns

labelsndarray, shape (n_samples,): Cluster labels.

get_params(self, deep=True)[source]¶

Get parameters for this estimator.

Parameters

deepboolean, optional: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsmapping of string to any: Parameter names mapped to their values.

predict(self, X)[source]¶

Predict the closest cluster each sample in X belongs to.

Parameters

Xarray-like or sparse matrix, shape (n_samples, n_features): New data to predict. If a sparse matrix is provided, it will be converted into a sparse csr_matrix.

Returns

labelsndarray, shape (n_samples,): Cluster labels.

set_params(self, **params)[source]¶

Set the parameters of this estimator.

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

Returns

self

sklearn.cluster.AffinityPropagation¶

`sklearn.cluster`.AffinityPropagation¶