class ibex.sklearn.cluster.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)

Bases: sklearn.cluster.bicluster.SpectralCoclustering, ibex._base.FrameMixin


The documentation following is of the class wrapped by this class. There are some changes, in particular:

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.

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.
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.
fit(X, y=None)


The documentation following is of the class wrapped by this class. There are some changes, in particular:

Creates a biclustering for X.

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

y : Ignored