.. 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>_.