Metadata-Version: 2.1
Name: graph-ensembles
Version: 0.3.2
Summary: The graph ensemble package contains a set of methods to build fitness based graph ensembles from marginal information.
Home-page: https://github.com/LeonardoIalongo/graph-ensembles
Author: Leonardo Niccolò Ialongo
Author-email: leonardo.ialongo@gmail.com
License: GNU General Public License v3
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: OS Independent
Requires-Python: >=3.0
Description-Content-Type: text/x-rst
License-File: LICENSE.txt
Requires-Dist: numpy>=1.22
Requires-Dist: numba>=0.56
Requires-Dist: scipy>=1.9
Requires-Dist: pandas>=1.1
Requires-Dist: networkx>=3.0

.. image:: https://travis-ci.com/LeonardoIalongo/graph-ensembles.svg?branch=master
    :target: https://travis-ci.com/LeonardoIalongo/graph-ensembles

=================
Graph ensembles
=================

The graph ensemble package contains a set of methods to build fitness based 
graph ensembles from marginal information. These methods can be used to build 
randomized ensembles preserving the marginal information provided. 

* Free software: GNU General Public License v3
* Documentation: https://graph-ensembles.readthedocs.io.


Installation
------------
Install using:

.. code-block:: python

   pip install graph_ensembles

Usage
-----
Currently only the RandomGraph and StripeFitnessModel are fully implemented.
An example of how it can be used is the following. 
For more see the example notebooks in the examples folder.

.. code-block:: python

    import graph_ensembles as ge
    import pandas as pd

    v = pd.DataFrame([['ING', 'NL'],
                     ['ABN', 'NL'],
                     ['BNP', 'FR'],
                     ['BNP', 'IT']],
                     columns=['name', 'country'])

    e = pd.DataFrame([['ING', 'NL', 'ABN', 'NL', 1e6, 'interbank', False],
                     ['BNP', 'FR', 'ABN', 'NL', 2.3e7, 'external', False],
                     ['BNP', 'IT', 'ABN', 'NL', 7e5, 'interbank', True],
                     ['BNP', 'IT', 'ABN', 'NL', 3e3, 'interbank', False],
                     ['ABN', 'NL', 'BNP', 'FR', 1e4, 'interbank', False],
                     ['ABN', 'NL', 'ING', 'NL', 4e5, 'external', True]],
                     columns=['creditor', 'c_country',
                              'debtor', 'd_country',
                              'value', 'type', 'EUR'])

    g = ge.MultiDiGraph(v, e, v_id=['name', 'country'],
                 src=['creditor', 'c_country'],
                 dst=['debtor', 'd_country'],
                 edge_label=['type', 'EUR'],
                 weight='value')

    # Initialize model
    model = ge.MultiFitnessModel(g)

    # Fit model parameters
    model.fit()

    # Sample from the ensemble
    model.sample()

Development
-----------
Please work on a feature branch and create a pull request to the development 
branch. If necessary to merge manually do so without fast forward:

.. code-block:: bash

    git merge --no-ff myfeature

To build a development environment run:

.. code-block:: bash

    python3 -m venv env 
    source env/bin/activate 
    pip install -e .
    pip install -r requirements.txt

For testing:

.. code-block:: bash

    pytest --cov

Credits
-------
This is a project by `Leonardo Niccolò Ialongo <https://datasciencephd.eu/students/leonardo-niccol%C3%B2-ialongo/>`_ and `Emiliano Marchese <https://www.imtlucca.it/en/emiliano.marchese/>`_, under 
the supervision of `Diego Garlaschelli <https://networks.imtlucca.it/members/diego>`_.



=======
History
=======

0.3.2 (2023-12-01)
------------------
* Improved and corrected support for the computation of average nearest neighbour properties and degrees. 

0.3.1 (2023-11-20)
------------------
* Added to the spark module a function to compute the confusion matrix elements at various thresholds of the probability matrix.

0.3.0 (2023-11-03)
------------------
* Major update of graph classes into four new categories (Graph, DiGraph, MultiGraph, MultiDiGraph) in line with Networkx. 
* Cleaned up models and organized in three modules (dense, sparse, spark) based on how the computations are performed and results are stored. 
* Introduced better inheritance through Ensemble classes based on the newly defined graph classes. 
* Added testing in spark and updated testing of MultiDiGraph Ensemble classes.  

0.2.3 (2023-07-03)
------------------
* Improved and corrected spark submodule.

0.2.2 (2023-05-11)
------------------
* Created submodule spark for allowing some models to be parallelize computations using spark

0.2.1 (2021-08-03)
------------------
* Added option for faster computation of average nearest neighbour properties by allowing for multiple links between the same nodes.
* Added compression option in to_networkx function.

0.2.0 (2021-07-12)
------------------
* Added likelihood and nearest neighbour properties.
* Revisited API for measures to ensure correct recompute if necessary.

0.1.3 (2021-04-29)
------------------
* Added new option for fitting the stripe model that ensures that the minimum non-zero expected degree is one
* Corrected issue in expected degree calculations

0.1.2 (2021-04-07)
------------------
* Added scale invariant probability functional to all models
* Improved methods for convergence with change in API, xtol now a relative measure
* Added pagerank and trophic depth to the library
* Added methods for graph conversion to networkx
* Added methods for computing the adjacency matrix as a sparse matrix

0.1.1 (2021-03-29)
------------------
* Fixed bug in stripe expected degree computation
* Added testing of expected degree performance

0.1.0 (2021-03-29)
------------------
* Added the block model and group info to graphs
* Added fast implementation of theoretical expected degrees
* Fixed some compatibility issues with multiple item assignments

0.0.4 (2021-03-15)
------------------
* Fixed issues with slow pandas index conversion

0.0.3 (2021-03-14)
------------------
* Large changes in API with great improvements in usability
* Added sampling function
* Added RandomGraph model
* Added Graph classes for ease of use


0.0.2 (2020-11-13)
------------------
* Added steps for CI. 
* Corrected broken links. 
* Removed support for python 3.5 and 3.6

0.0.1 (2020-10-28)
------------------

* First release on PyPI. StripeFitnessModel available, all other model classes still dummies.

