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=1, random_state=None)

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, optional, default: 1

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.

If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used.

random_state : int, RandomState instance or None, optional, default: None

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

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.

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=1, random_state=None)

biclusters_

Convenient way to get row and column indicators together.

Returns the rows_ and columns_ members.

fit(X, y=None)

Creates a biclustering for X.

Parameters:

X : array-like, shape (n_samples, n_features)

y : Ignored

get_indices(i)

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)

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)

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)

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)

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 :