https://github.com/yuhaozhang/ebc

Ensemble biclustering for classification code and examples.
https://github.com/yuhaozhang/ebc

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Ensemble biclustering for classification code and examples.

• Host: GitHub
• URL: https://github.com/yuhaozhang/ebc
• Owner: yuhaozhang
• Created: 2015-10-12T21:27:51.000Z (about 9 years ago)
• Default Branch: master
• Last Pushed: 2015-11-02T18:15:58.000Z (about 9 years ago)
• Last Synced: 2023-05-23T23:31:11.532Z (over 1 year ago)
• Language: Python
• Size: 1.52 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Ensemble Biclustering for Classification

==============

####Note

This is a forked version of EBC python implementation. For the final release please refer to [this repo](https://github.com/blpercha/ebc).

A python implementation of the Ensemble Biclustering for Classification (EBC) algorithm. Although having "biclustering" in its name, EBC is a co-clustering algorithm that allows you to perform co-clustering on very large sparse N-dimensional matrix. For details and examples of using EBC please reference [this paper](http://www.ncbi.nlm.nih.gov/pubmed/26219079).

##Files

- `ebc.py`: an implementation of EBC algorithm using sparse matrix.

- `ebc2d.py`: a vectorized implementation of EBC algorithm using (2D) numpy dense array.

- `matrix.py`: a N-dimensional sparse matrix implementation using python dict that allows basic get/set/sum operations.

- `resources/`: this directory contains many example data that could be used to run EBC on.

##Usage

####General Usage

Using ebc is easy. First you need a sparse matrix constructed as a SparseMatrix instance defined in `matrix.py`. You can use `matrix.py`'s built in `read_data()` method to construct the sparse matrix.

Once you have the sparse matrix and import the ebc module, you can simply do the following:

    ebc = EBC(sparse_matrix, n_clusters=[30, 125], max_iterations=10)

    cXY, objective, iter = ebc.run()

The returned `cXY` contains the clustering assignments along each axis in a list of lists, `objective` contains the final objective function value, `iter` contains the iterations that it ran for to converge.

####Efficiency Considerations

In short, `EBC` is built for highly sparse multi-dimensional matrix, while `EBC2D` is a vectorized version of `EBC`, and is built for less sparse 2D matrix. The running time of `EBC` increases linearly with the number of non-zero elements, while the running time of `EBC2D` will only increase when matrix size increases.

- If your input matrix is highly sparse or you need support for N-dimensional matrix with N > 2, you should use `EBC` class in `ebc.py`.

- If your input matrix is not very sparse (e.g. 5% density), and can fit into memory with `numpy`, you can choose to use `EBC2D` class in `ebc2d.py`. Note that `EBC2D` only supports 2D matrix, since large multi-dimensional dense matrix can hardly fit into memory.

To give you more sense of the efficiency, the `EBC` implementation runs for ~50 seconds on our 14052 x 7272 example matrix (which is >99% sparse) on a MacBook, and in this case it is faster than `EBC2D`. However, in our testing, for a 5000 x 5000 matrix of 95% sparsity, `EBC2D` can get a `x5` speedup over `EBC`. And this speedup grows as the sparsity decreases.

####Dependency

To run this implementation of EBC algorithm, you need to have `numpy` installed.

##References

- Percha, Bethany, and Russ B. Altman. "Learning the Structure of Biomedical Relationships from Unstructured Text." PLoS computational biology 11.7 (2015).

- Dhillon, Inderjit S., Subramanyam Mallela, and Dharmendra S. Modha. "Information-theoretic co-clustering." Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2003.