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An implementation of the BiMax biclustering algorithm by Prelic, et. al. (2006).
"""Implements the BiMax biclustering algorithm.
Authors : Kemal Eren
License: BSD 3 clause
import numpy as np
class BiMax():
"""Method to find all maximal biclusters in a boolean array.
`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`.
def fit(self, X):
"""Creates a biclustering for X.
X : array-like, shape (n_samples, n_features)
n_rows, n_cols = X.shape
result = self._conquer(X, set(range(n_rows)),
set(range(n_cols)), [])
row_ind = []
col_ind = []
for rows, cols in result:
ri, ci = self._get_indicators(rows, cols, X.shape)
self.rows_ = np.vstack(row_ind)
self.columns_ = np.vstack(col_ind)
def _conquer(self, data, rows, cols, col_sets):
if np.all(data[np.array(list(rows))[:, np.newaxis], list(cols)]):
return [(rows, cols)]
rows_all, rows_none, rows_some, cols_all, cols_none = \
self._divide(data, rows, cols, col_sets)
results_all = []
results_none = []
if rows_all:
results_all = self._conquer(data, rows_all.union(rows_some),
cols_all, col_sets)
if rows_none and not rows_some:
results_none = self._conquer(data, rows_none, cols_none, col_sets)
elif rows_some:
new_col_sets = col_sets[:]
results_none = self._conquer(data,
return results_all + results_none
def _divide(self, data, rows, cols, col_sets):
new_rows, nz_cols = self._reduce(data, rows, cols, col_sets)
n_cols = len(cols)
cols_all = cols
for r in new_rows:
if 0 < len(nz_cols[r]) < n_cols:
cols_all = nz_cols[r]
cols_none = cols.difference(cols_all)
rows_all = set()
rows_none = set()
rows_some = set()
for r in new_rows:
if nz_cols[r].issubset(cols_all):
elif nz_cols[r].issubset(cols_none):
return rows_all, rows_none, rows_some, cols_all, cols_none
def _reduce(self, data, rows, cols, col_sets):
row_idxs = np.array(list(rows))
col_idxs = np.array(list(cols))
subarray = data[row_idxs[:, np.newaxis], col_idxs]
nz_cols = {row_idxs[r]: set(col_idxs[np.nonzero(subarray[r])[0]])
for r in range(row_idxs.shape[0])}
new_rows = set(r for r in row_idxs
if nz_cols[r] and
for cset in col_sets))
return new_rows, nz_cols
def _get_indicators(self, rows, columns, shape):
"""Convert indices to indicator vectors"""
row_ind = np.zeros(shape[0], dtype=np.bool)
col_ind = np.zeros(shape[1], dtype=np.bool)
row_ind[list(rows)] = True
col_ind[list(columns)] = True
return row_ind, col_ind
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