sklearn.cluster.bicluster.SpectralCoclustering

class sklearn.cluster.bicluster.SpectralCoclustering(n_clusters=3, svd_method=’randomized’, n_svd_vecs=None, mini_batch=False, init=’k-means++’, n_init=10, n_jobs=None, random_state=None) [source]

Spectral Co-Clustering algorithm (Dhillon, 2001).

Clusters rows and columns of an array X to solve the relaxed normalized cut of the bipartite graph created from X as follows: the edge between row vertex i and column vertex j has weight X[i, j].

The resulting bicluster structure is block-diagonal, since each row and each column belongs to exactly one bicluster.

Supports sparse matrices, as long as they are nonnegative.

Read more in the User Guide.

Parameters:

n_clusters : integer, optional, default: 3 The number of biclusters to find. svd_method : string, optional, default: ‘randomized’ Selects the algorithm for finding singular vectors. May be ‘randomized’ or ‘arpack’. If ‘randomized’, use sklearn.utils.extmath.randomized_svd, which may be faster for large matrices. If ‘arpack’, use scipy.sparse.linalg.svds, which is more accurate, but possibly slower in some cases. n_svd_vecs : int, optional, default: None Number of vectors to use in calculating the SVD. Corresponds to ncv when svd_method=arpack and n_oversamples when svd_method is ‘randomized`. mini_batch : bool, optional, default: False Whether to use mini-batch k-means, which is faster but may get different results. init : {‘k-means++’, ‘random’ or an ndarray} Method for initialization of k-means algorithm; defaults to ‘k-means++’. n_init : int, optional, default: 10 Number of random initializations that are tried with the k-means algorithm. If mini-batch k-means is used, the best initialization is chosen and the algorithm runs once. Otherwise, the algorithm is run for each initialization and the best solution chosen. n_jobs : int or None, optional (default=None) The number of jobs to use for the computation. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details. random_state : int, RandomState instance or None (default) Used for randomizing the singular value decomposition and the k-means initialization. Use an int to make the randomness deterministic. See Glossary.

Attributes:

rows_ : array-like, shape (n_row_clusters, n_rows) Results of the clustering. rows[i, r] is True if cluster i contains row r. Available only after calling fit. columns_ : array-like, shape (n_column_clusters, n_columns) Results of the clustering, like rows. row_labels_ : array-like, shape (n_rows,) The bicluster label of each row. column_labels_ : array-like, shape (n_cols,) The bicluster label of each column.

References

• Dhillon, Inderjit S, 2001. Co-clustering documents and words using bipartite spectral graph partitioning.

Examples

>>> from sklearn.cluster import SpectralCoclustering
>>> import numpy as np
>>> X = np.array([[1, 1], [2, 1], [1, 0],
...               [4, 7], [3, 5], [3, 6]])
>>> clustering = SpectralCoclustering(n_clusters=2, random_state=0).fit(X)
>>> clustering.row_labels_
array([0, 1, 1, 0, 0, 0], dtype=int32)
>>> clustering.column_labels_
array([0, 0], dtype=int32)
>>> clustering 
SpectralCoclustering(init='k-means++', mini_batch=False, n_clusters=2,
           n_init=10, n_jobs=None, n_svd_vecs=None, random_state=0,
           svd_method='randomized')

Methods

fit(X[, y])	Creates a biclustering for X.
get_indices(i)	Row and column indices of the i’th bicluster.
get_params([deep])	Get parameters for this estimator.
get_shape(i)	Shape of the i’th bicluster.
get_submatrix(i, data)	Returns the submatrix corresponding to bicluster i.
set_params(**params)	Set the parameters of this estimator.

__init__(n_clusters=3, svd_method=’randomized’, n_svd_vecs=None, mini_batch=False, init=’k-means++’, n_init=10, n_jobs=None, random_state=None) [source]

biclusters_

Convenient way to get row and column indicators together.

Returns the rows_ and columns_ members.

fit(X, y=None) [source]

Creates a biclustering for X.

Parameters:	X : array-like, shape (n_samples, n_features) y : Ignored

get_indices(i) [source]

Row and column indices of the i’th bicluster.

Only works if rows_ and columns_ attributes exist.

Parameters:	i : int The index of the cluster.
Returns:	row_ind : np.array, dtype=np.intp Indices of rows in the dataset that belong to the bicluster. col_ind : np.array, dtype=np.intp Indices of columns in the dataset that belong to the bicluster.

get_params(deep=True) [source]

Get parameters for this estimator.

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

get_shape(i) [source]

Shape of the i’th bicluster.

Parameters:	i : int The index of the cluster.
Returns:	shape : (int, int) Number of rows and columns (resp.) in the bicluster.

get_submatrix(i, data) [source]

Returns the submatrix corresponding to bicluster i.

Parameters:	i : int The index of the cluster. data : array The data.
Returns:	submatrix : array The submatrix corresponding to bicluster i.

Notes

Works with sparse matrices. Only works if rows_ and columns_ attributes exist.

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.

Returns:	self