coclust.coclustering.CoclustInfo

class coclust.coclustering.CoclustInfo(n_row_clusters=2, n_col_clusters=2, init=None, max_iter=20, n_init=1, tol=1e-09, random_state=None)

Information-Theoretic Co-clustering.

Parameters:

• n_row_clusters (int, optional, default: 2) – Number of row clusters to form
• n_col_clusters (int, optional, default: 2) – Number of column clusters to form
• init (numpy array or scipy sparse matrix, shape (n_features, n_clusters), optional, default: None) – Initial column labels
• max_iter (int, optional, default: 20) – Maximum number of iterations
• n_init (int, optional, default: 1) – Number of time the algorithm will be run with different initializations. The final results will be the best output of n_init consecutive runs.
• random_state (integer or numpy.RandomState, optional) – The generator used to initialize the centers. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.
• tol (float, default: 1e-9) – Relative tolerance with regards to criterion to declare convergence

row_labels_

array-like, shape (n_rows,) – Bicluster label of each row

column_labels_

array-like, shape (n_cols,) – Bicluster label of each column

delta_kl_

array-like, shape (k,l) – Value \(\frac{p_{kl}}{p_{k.} \times p_{.l}}\) for each row cluster k and column cluster l

fit(X, y=None)

Perform co-clustering.

Parameters:X (numpy array or scipy sparse matrix, shape=(n_samples, n_features)) – Matrix to be analyzed

get_col_indices(i)

Give the column indices of the i’th co-cluster.

Parameters:i (integer) – Index of the i’th column cluster Returns:list of column indices Return type:list

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 – Parameter names mapped to their values. Return type:mapping of string to any

get_row_indices(i)

Give the row indices of the i’th co-cluster.

Parameters:i (integer) – Index of the i’th row cluster Returns:list of row indices Return type:list

get_shape(i, j)

Give the shape of block corresponding to the i’th row cluster and

the j’th column cluster.

Parameters:

• i (integer) – Index of the row cluster
• j (integer) – Index of the column cluster

Returns:

(number of rows, number of columns)

Return type:

(int, int)

get_submatrix(m, i, j)

Give the submatrix corresponding to row cluster i and column cluster j.

Parameters:

• m (X : numpy array or scipy sparse matrix) – Matrix from which the block has to be extracted
• i (integer) – index of the row cluster
• j (integer) – index of the col cluster

Returns:

Submatrix corresponding to row cluster i and column cluster j

Return type:

numpy array or scipy sparse matrix

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: Return type:self