Metadata-Version: 2.4
Name: bicm
Version: 3.5.0
Summary: Package for bipartite configuration model
Author-email: Matteo Bruno <matteobruno180@gmail.com>
License: MIT License
        
        Copyright (c) 2020 Matteo Bruno
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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Project-URL: Homepage, https://github.com/mat701/BiCM
Project-URL: Documentation, https://bipartite-configuration-model.readthedocs.io/en/latest/
Project-URL: Source, https://github.com/mat701/BiCM
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy>=1.22
Requires-Dist: scipy>=1.8
Requires-Dist: numba>=0.56
Requires-Dist: tqdm>=4.52
Provides-Extra: test
Requires-Dist: pytest>=7; extra == "test"
Dynamic: license-file

## BiCM package

This is a Python package for the computation of the maximum entropy bipartite configuration model (BiCM) and the projection of bipartite networks on one layer. It requires Python 3.10 or later.

You can install this package via pip: 

    pip install bicm

Documentation is available at https://bipartite-configuration-model.readthedocs.io/en/latest/ .

This package is also a module of NEMtropy that you can find at https://github.com/nicoloval/NEMtropy .

For more solvers of maximum entropy configuration models visit https://meh.imtlucca.it/ .

NOTE of the developer: there was an error in the projection threshold, validating less links than it should have.
Please re-run your analysis after updating to the last version (>=3.1)


## Basic functionalities

To install:
    
    pip install bicm

To import the module:
    
    import bicm

To generate a Graph object and initialize it (with a biadjacency matrix, edgelist or degree sequences):
    
    from bicm import BipartiteGraph
    myGraph = BipartiteGraph()
    myGraph.set_biadjacency_matrix(my_biadjacency_matrix)
    myGraph.set_adjacency_list(my_adjacency_list)
    myGraph.set_edgelist(my_edgelist)
    myGraph.set_degree_sequences((first_degree_sequence, second_degree_sequence))

Or alternatively, with the respective data structure as input:
    
    from bicm import BipartiteGraph
    myGraph = BipartiteGraph(biadjacency=my_biadjacency_matrix, adjacency_list=my_adjacency_list, edgelist=my_edgelist, degree_sequences=((first_degree_sequence, second_degree_sequence)))

To compute the BiCM probability matrix of the graph or the relative fitnesses coefficients as dictionaries containing the nodes names as keys:

    my_probability_matrix = myGraph.get_bicm_matrix()
    my_x, my_y = myGraph.get_bicm_fitnesses()

This will solve the bicm using recommended settings for the solver. 
To customize the solver you can alternatively use (in advance) the following method:
    
    myGraph.solve_tool(light_mode=False, method='newton', initial_guess=None, tol=1e-8, max_steps=None, verbose=False, linsearch=True, regularise=False, print_error=True, exp=False)

To get the rows or columns projection of the graph:

    myGraph.get_rows_projection()
    myGraph.get_cols_projection()

Alternatively, to customize the projection:

    myGraph.compute_projection(rows=True, alpha=0.05, approx_method='poisson', threads_num=4, progress_bar=True)
    
### Warnings, logging and reproducibility

Problems found while solving or projecting (for example a solver that did not converge) are reported as Python warnings of category `bicm.BiCMWarning`, so they can be filtered:

    import warnings
    import bicm
    warnings.filterwarnings("ignore", category=bicm.BiCMWarning)

Informative messages (for example "Computing the BiCM...") go to the `bicm` logger and are hidden by default. To show them:

    import logging
    logging.basicConfig()
    logging.getLogger("bicm").setLevel(logging.INFO)

The sampling functions accept a `seed` argument (an integer or a `numpy.random.Generator`) to get reproducible samples, e.g. `sample_bicm(avg_mat, seed=42)`. Without it, numpy's global random state is used as in previous versions.

Projections use several processes by default. On macOS, scripts must put their code under `if __name__ == "__main__":` for this to work, or pass `threads_num=1`. On Windows projections always run in a single process.

Now version 3 is online, and you can use the package with weighted networks as well using the BiWCM models!

See a more detailed walkthrough in **tests/bicm_test** or **tests/biwcm_test** notebooks, or check out the API in the documentation.

## Development

To install the package from source together with the test dependencies and run the test suite:

    pip install -e ".[test]"
    pytest

The tests run automatically on GitHub Actions for every push and pull request.
A new version is published to PyPI when a GitHub release is created with a tag matching the package version (e.g. `v3.5.0`).

## How to cite

If you use the `bicm` module, please cite its location on Github
[https://github.com/mat701/BiCM](https://github.com/mat701/BiCM) and the
original articles [Vallarano2021], [Saracco2015] and [Saracco2017].

If you use the weighted models (BiWCM_c or BiWCM_d), please also cite [Bruno2023].

### References

[Vallarano2021] [N. Vallarano, M. Bruno, E. Marchese, G. Trapani, F. Saracco, T. Squartini, G. Cimini, M. Zanon, Fast and scalable likelihood maximization for Exponential Random Graph Models with local constraints, Nature Scientific Reports](https://doi.org/10.1038/s41598-021-93830-4)

[Bruno2023] [M. Bruno, D. Mazzilli, A. Patelli, T. Squartini, F. Saracco, Inferring comparative advantage via entropy maximization. J. Phys. Complex. 4, 045011 (2023), doi:10.1088/2632-072X/ad1411](https://doi.org/10.1088/2632-072X/ad1411)

[Saracco2015] [F. Saracco, R. Di Clemente, A. Gabrielli, T. Squartini, Randomizing bipartite networks: the case of the World Trade Web, Scientific Reports 5, 10595 (2015)](http://www.nature.com/articles/srep10595).

[Saracco2017] [F. Saracco, M. J. Straka, R. Di Clemente, A. Gabrielli, G. Caldarelli, and T. Squartini, Inferring monopartite projections of bipartite networks: an entropy-based approach, New J. Phys. 19, 053022 (2017)](http://stacks.iop.org/1367-2630/19/i=5/a=053022)


_Author_:

[Matteo Bruno](https://csl.sony.it/member/matteo-bruno/) (BiCM) (a.k.a. [mat701](https://github.com/mat701))
