Metadata-Version: 2.1
Name: graph_ensembles
Version: 0.0.2
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
Description: .. 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 StripeFitnessModel is 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
        
           # Define graph marginals
            out_strength = np.array([[0, 0, 2],
                                    [1, 1, 5],
                                    [2, 2, 6],
                                    [3, 2, 1]])
        
            in_strength = np.array([[0, 1, 5],
                                    [0, 2, 4],
                                    [1, 2, 3],
                                    [3, 0, 2]])
        
            num_nodes = 4
            num_links = np.array([1, 1, 3])
        
            # Initialize model
            model = ge.StripeFitnessModel(out_strength, in_strength, num_links)
        
            # Fit model parameters
            model.fit()
        
            # Return probability matrix 
            prob_mat = model.probability_matrix
        
        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 venv 
            source venv/bin/activate 
            pip install -e '.[dev]'
        
        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
        =======
        
        0.0.2 (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.
        
        
Platform: UNKNOWN
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: Operating System :: OS Independent
Requires-Python: >=3.0
Description-Content-Type: text/x-rst
Provides-Extra: dev
