sklearn.cluster
.SpectralCoclustering¶
-
class
sklearn.cluster.
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 fromX
as follows: the edge between row vertexi
and column vertexj
has weightX[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 : int, default=3
The number of biclusters to find.
svd_method : {‘randomized’, ‘arpack’}, 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’, usescipy.sparse.linalg.svds
, which is more accurate, but possibly slower in some cases.n_svd_vecs : int, default=None
Number of vectors to use in calculating the SVD. Corresponds to
ncv
whensvd_method=arpack
andn_oversamples
whensvd_method
is ‘randomized`.mini_batch : bool, default=False
Whether to use mini-batch k-means, which is faster but may get different results.
init : {‘k-means++’, ‘random’, or ndarray of shape (n_clusters, n_features), default=’k-means++’
Method for initialization of k-means algorithm; defaults to ‘k-means++’.
n_init : int, 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, 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 ajoblib.parallel_backend
context.-1
means using all processors. See Glossary for more details.random_state : int, RandomState instance, default=None
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 of shape (n_row_clusters, n_rows)) Results of the clustering.
rows[i, r]
is True if clusteri
contains rowr
. Available only after callingfit
.columns_
(array-like of shape (n_column_clusters, n_columns)) Results of the clustering, like
rows
.row_labels_
(array-like of shape (n_rows,)) The bicluster label of each row.
column_labels_
(array-like of 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
(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)Return 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]¶ Initialize self. See help(type(self)) for accurate signature.
-
property
biclusters_
¶ Convenient way to get row and column indicators together.
Returns the
rows_
andcolumns_
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_
andcolumns_
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 : bool, default=True
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]¶ Return 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_
andcolumns_
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.- Parameters
**params : dict
Estimator parameters.
- Returns
self : object
Estimator instance.