A Python library for bi-cluster editing.

• Task: Given a matrix weights with positive and negative weights, transform the bipartite graph induced by weights into a disjoint collection of bi-cliques (bi-clusters) via edge insertions and deletions, such that the overall transformation cost is minimal.
• The node sets of the bipartite graph induced by weights are the sets of rows and columns. There is an edge between row i and column k if and only if weights[i,k] > 0.
• Deleting an existing edge (i,k) incurs cost weights[i,k].
• Inserting a non-existing edge (i,k) incurs cost -weights[i,k].

1. Download and install Gurobi and obtain a license by following the instructions in the installation guide for Linux, Mac OS, or Windows.
2. Open a shell and execute pip install biclustpy.

After installation, import biclustpy as bp into your Python application. Then use it as follows:

• bp.Algorithm: Use this class to select the algorithm you want to employ.
  • bp.Algorithm.use_ilp(time_limit, tune): Call this function if you want to use Gurobi to solve the ILP formulation suggested in G. F. de Sousa Filho et al (2017): New heuristics for the bicluster editing problem.
  • bp.Algorithm.use_ch(alpha, seed): Call this function if you want to use the constructive heuristic suggested in G. F. de Sousa Filho et al (2017): New heuristics for the bicluster editing problem.
  • More algorithms are following soon.
• bp.compute_bi_clusters(weights, algorithm): Use this function to solve a bi-cluster editing problem.
  • weights: The problem instance given as a numpy.array.
  • algorithm: The selected algorithm given as a bp.Algorithm object.
• bp.save_bi_clusters_as_xml(filename, bi_clusters, obj_val, is_optimal, instance = ""): Use this function to save the obtained solution as an XML file.
  • filename: The name of the XML file.
  • bi_clusters: The bi-clusters returned by bp.compute_bi_clusters.
  • obj_val: The objective value of the bi-clusters returned by bp.compute_bi_clusters.
  • is_optimal: A flag returned by bp.compute_bi_clusters that indicates whether the computed bi-clusters are guaranteed to be optimal.
  • instance: A string that contains information about the problem instance.

import numpy as np
import biclustpy as bp

algorithm = bp.Algorithm()
time_limit = 100
tune = True
algorithm.use_ilp(100, True)

n = 30
m = 40
t = .95
weights = np.random.rand(n, m) - (t * np.ones((n, m)))

bi_clusters, obj_val, is_optimal = bp.compute_bi_clusters(weights, algorithm)

filename = "bi_clusters.xml"
instance = "random instance with 30 rows and 40 columns"
bp.save_bi_clusters_as_xml(filename, bi_clusters, obj_val, is_optimal, instance)

Upon installation, you can run biclustpy from the command line. Usage:

biclustpy [-h]
          (--load input-file | --random num-rows num-cols threshold seed)
          [--save output-file] [--alg {ILP,CH}]
          [--ilp_options time-limit tune]

More more information, execute biclustpy -h.

You may use and distribute biclustpy under the terms of the GNU Lesser General Public License.