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

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(self, X[, y]) Creates a biclustering for X.
get_indices(self, i) Row and column indices of the i’th bicluster.
get_params(self[, deep]) Get parameters for this estimator.
get_shape(self, i) Shape of the i’th bicluster.
get_submatrix(self, i, data) Returns the submatrix corresponding to bicluster i.
set_params(self, \*\*params) Set the parameters of this estimator.
__init__(self, 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(self, X, y=None)[source]

Creates a biclustering for X.

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