sklearn.cluster.bicluster.SpectralBiclustering

class sklearn.cluster.bicluster.SpectralBiclustering(n_clusters=3, method='bistochastic', n_components=6, n_best=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 biclustering (Kluger, 2003).

Partitions rows and columns under the assumption that the data has an underlying checkerboard structure. For instance, if there are two row partitions and three column partitions, each row will belong to three biclusters, and each column will belong to two biclusters. The outer product of the corresponding row and column label vectors gives this checkerboard structure.

Read more in the User Guide.

Parameters:
n_clusters : integer or tuple (n_row_clusters, n_column_clusters)

The number of row and column clusters in the checkerboard structure.

method : string, optional, default: ‘bistochastic’

Method of normalizing and converting singular vectors into biclusters. May be one of ‘scale’, ‘bistochastic’, or ‘log’. The authors recommend using ‘log’. If the data is sparse, however, log normalization will not work, which is why the default is ‘bistochastic’. CAUTION: if method=’log’, the data must not be sparse.

n_components : integer, optional, default: 6

Number of singular vectors to check.

n_best : integer, optional, default: 3

Number of best singular vectors to which to project the data for clustering.

svd_method : string, optional, default: ‘randomized’

Selects the algorithm for finding singular vectors. May be ‘randomized’ or ‘arpack’. If ‘randomized’, uses sklearn.utils.extmath.randomized_svd, which may be faster for large matrices. If ‘arpack’, uses 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,)

Row partition labels.

column_labels_ : array-like, shape (n_cols,)

Column partition labels.

References

Examples

>>> from sklearn.cluster import SpectralBiclustering
>>> import numpy as np
>>> X = np.array([[1, 1], [2, 1], [1, 0],
...               [4, 7], [3, 5], [3, 6]])
>>> clustering = SpectralBiclustering(n_clusters=2, random_state=0).fit(X)
>>> clustering.row_labels_
array([1, 1, 1, 0, 0, 0], dtype=int32)
>>> clustering.column_labels_
array([0, 1], dtype=int32)
>>> clustering 
SpectralBiclustering(init='k-means++', method='bistochastic',
           mini_batch=False, n_best=3, n_clusters=2, n_components=6,
           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, method='bistochastic', n_components=6, n_best=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