`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_clustersinteger, optional, default: 3

The number of biclusters to find.

svd_methodstring, 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_vecsint, 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_batchbool, 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_initint, 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_jobsint 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_stateint, 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(n_clusters=2, random_state=0)

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]¶: Initialize self. See help(type(self)) for accurate signature.

property 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

Xarray-like, shape (n_samples, n_features)
yIgnored

get_indices(self, i)[source]¶

Row and column indices of the i’th bicluster.

Only works if rows_ and columns_ attributes exist.

Parameters

iint: The index of the cluster.

Returns

row_indnp.array, dtype=np.intp: Indices of rows in the dataset that belong to the bicluster.
col_indnp.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

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.

get_shape(self, i)[source]¶

Shape of the i’th bicluster.

Parameters

iint: 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

iint: The index of the cluster.
dataarray: The data.

Returns

submatrixarray: 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

sklearn.cluster.bicluster.SpectralCoclustering¶

`sklearn.cluster.bicluster`.SpectralCoclustering¶