sklearn.cluster
.AffinityPropagation¶

class
sklearn.cluster.
AffinityPropagation
(*, damping=0.5, max_iter=200, convergence_iter=15, copy=True, preference=None, affinity='euclidean', verbose=False, random_state='warn')[source]¶ Perform Affinity Propagation Clustering of data.
Read more in the User Guide.
 Parameters
 dampingfloat, 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, default=200
Maximum number of iterations.
 convergence_iterint, default=15
Number of iterations with no change in the number of estimated clusters that stops the convergence.
 copybool, default=True
Make a copy of input data.
 preferencearraylike of shape (n_samples,) or float, default=None
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.
 affinity{‘euclidean’, ‘precomputed’}, default=’euclidean’
Which affinity to use. At the moment ‘precomputed’ and
euclidean
are supported. ‘euclidean’ uses the negative squared euclidean distance between points. verbosebool, default=False
Whether to be verbose.
 random_stateint or np.random.RandomStateInstance, default: 0
Pseudorandom number generator to control the starting state. Use an int for reproducible results across function calls. See the Glossary.
New in version 0.23: this parameter was previously hardcoded as 0.
 Attributes
 cluster_centers_indices_ndarray of shape (n_clusters,)
Indices of cluster centers
 cluster_centers_ndarray of shape (n_clusters, n_features)
Cluster centers (if affinity !=
precomputed
). labels_ndarray of shape (n_samples,)
Labels of each point
 affinity_matrix_ndarray of 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 as1
. In addition,predict
will then label every sample as1
.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 label0
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(random_state=5).fit(X) >>> clustering AffinityPropagation(random_state=5) >>> 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
(X[, y])Fit the clustering from features, or affinity matrix.
fit_predict
(X[, y])Fit the clustering from features or affinity matrix, and return cluster labels.
get_params
([deep])Get parameters for this estimator.
predict
(X)Predict the closest cluster each sample in X belongs to.
set_params
(**params)Set the parameters of this estimator.

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

fit
(X, y=None)[source]¶ Fit the clustering from features, or affinity matrix.
 Parameters
 Xarraylike or sparse matrix, shape (n_samples, n_features), or arraylike, 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 sparsecsr_matrix
. yIgnored
Not used, present here for API consistency by convention.
 Returns
 self

fit_predict
(X, y=None)[source]¶ Fit the clustering from features or affinity matrix, and return cluster labels.
 Parameters
 Xarraylike or sparse matrix, shape (n_samples, n_features), or arraylike, 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 sparsecsr_matrix
. yIgnored
Not used, present here for API consistency by convention.
 Returns
 labelsndarray, shape (n_samples,)
Cluster labels.

get_params
(deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
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
(X)[source]¶ Predict the closest cluster each sample in X belongs to.
 Parameters
 Xarraylike 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
(**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. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.