Skip to content

Commit

Permalink
bug fixing
Browse files Browse the repository at this point in the history
  • Loading branch information
@EmilianoMarchese
EmilianoMarchese committed Feb 9, 2021
1 parent 1dfc1c1 commit 65c896a
Show file tree
Hide file tree
Showing 14 changed files with 362 additions and 51 deletions.
Binary file modified docs/build/doctrees/environment.pickle
View file
Binary file not shown.
Binary file modified docs/build/doctrees/index.doctree
View file
Binary file not shown.
105 changes: 88 additions & 17 deletions docs/build/html/_sources/index.rst.txt
View file
Original file line number Diff line number Diff line change
@@ -1,35 +1,106 @@
NEMtropy documentation
=====================================

NEMtropy is a Maximum-Entropy toolbox for networks, released as a python3 module.

NEMtropy provides the user with a state of the art solver for a range variety of Maximum Entropy Networks models derived from the ERGM family.
This module allows you to solve the desired model and generate a number of randomized graphs from the original one: the so-called *graphs ensemble*.

NEMtropy builds on the current literature on the matter, improving both in speed of convergence and in the scale of the feasible networks.
To explore Maximum-Entropy modeling on networks, checkout [Maximum Entropy Hub](https://meh.imtlucca.it/).
NEMtropy is a Maximum-Entropy toolbox for networks, released as a
python3 module.

NEMtropy provides the user with a state of the art solver for a range
variety of Maximum Entropy Networks models derived from the ERGM family.
This module allows you to solve the desired model and generate a number
of randomized graphs from the original one: the so-called *graphs
ensemble*.

NEMtropy builds on the current literature on the matter, improving both
in speed of convergence and in the scale of the feasible networks. To
explore Maximum-Entropy modeling on networks, checkout `Maximum Entropy
Hub <https://meh.imtlucca.it/>`__.

The models implemented in NEMtropy are presented in a forthcoming
`paper <https://arxiv.org/abs/2101.12625>`__ on arXiv. If you use the
module for your scientific research, please consider citing us:

::

@misc{vallarano2021fast,
title={Fast and scalable likelihood maximization for Exponential Random Graph Models},
author={Nicolò Vallarano and Matteo Bruno and Emiliano Marchese and Giuseppe Trapani and Fabio Saracco and Tiziano Squartini and Giulio Cimini and Mario Zanon},
year={2021},
eprint={2101.12625},
archivePrefix={arXiv},
primaryClass={physics.data-an}
}

Currently Implemented Models
============================

The main feature of NEMtropy is (but not limited to) *network
randomization*. The specific kind of network to randomize and property
to preserve defines the model you need:

- **UBCM** *Undirected Binary Configuration Model* `[1] <#1>`__
- **UECM** *Undirected Enhanced Configuration Model* `[1] <#1>`__
- **DBCM** *Directed Binary Configuration Model* `[1] <#1>`__
- **DECM** *Directed Enhanced Configuration Model* `[1] <#1>`__
- **CReMa** `[2] <#2>`__

The following table may helps you identify the model that fits your
needs in function of the type of network you are working with; for
in-depth discussion please see the references.

+----------------------+--------------------+-------------------+
| [...] | Undirected Graph | Directed Graph |
+======================+====================+===================+
| **Binary Graph** | *UBCM* | *DBCM* |
+----------------------+--------------------+-------------------+
| **Weighted Graph** | *UECM*, *CReMa* | *DECM*, *CReMa* |
+----------------------+--------------------+-------------------+

*References*

- [1] Squartini, Tiziano, Rossana Mastrandrea, and Diego Garlaschelli.
"Unbiased sampling of network ensembles." New Journal of Physics 17.2
(2015): 023052. https://arxiv.org/abs/1406.1197
- [2] Parisi, Federica, Tiziano Squartini, and Diego Garlaschelli. "A
faster horse on a safer trail: generalized inference for the
efficient reconstruction of weighted networks." New Journal of
Physics 22.5 (2020): 053053. https://arxiv.org/abs/1811.09829


Basic functionalities
=====================================
=====================

To install:
NEMtropy can be installed via pip. You can get it from your terminal:

.. code-block:: python
pip install NEMtropy
```
$ pip install NEMtropy
```

To import the module:
If you already install the package and wish to upgrade it,
you can simply type from your terminal:

.. code-block:: python
import NEMtropy
```
$ pip install NEMtropy --upgrade
```


Dependencies
============

This package has been developed for Python 3.5 but it should be compatible with other versions. It works on numpy arrays and it uses numba and multiprocessing to speed up the computation. Feel free to send an inquiry (and please do it!) if you find any incompatibility.
NEMtropy uses <code>numba</code> library. It can be installed via pip by running
in your terminal the following command:

```
$ pip install numba
```

For <code>python3.5</code> users the correct command is the following:

```
$ pip install --prefer-binary numba
```

It avoids an error during the installation of <code>llvmlite</code> due to
the absence of its wheel in <code>python3.5</code>.

Guide
^^^^^^
Expand Down
116 changes: 102 additions & 14 deletions docs/build/html/index.html
View file
Original file line number Diff line number Diff line change
Expand Up @@ -42,26 +42,113 @@ <h3>Navigation</h3>

<div class="section" id="nemtropy-documentation">
<h1>NEMtropy documentation<a class="headerlink" href="#nemtropy-documentation" title="Permalink to this headline"></a></h1>
<p>NEMtropy is a Maximum-Entropy toolbox for networks, released as a python3 module.</p>
<p>NEMtropy provides the user with a state of the art solver for a range variety of Maximum Entropy Networks models derived from the ERGM family.
This module allows you to solve the desired model and generate a number of randomized graphs from the original one: the so-called <em>graphs ensemble</em>.</p>
<p>NEMtropy builds on the current literature on the matter, improving both in speed of convergence and in the scale of the feasible networks.
To explore Maximum-Entropy modeling on networks, checkout [Maximum Entropy Hub](<a class="reference external" href="https://meh.imtlucca.it/">https://meh.imtlucca.it/</a>).</p>
</div>
<div class="section" id="basic-functionalities">
<h1>Basic functionalities<a class="headerlink" href="#basic-functionalities" title="Permalink to this headline"></a></h1>
<p>To install:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">NEMtropy</span>
<p>NEMtropy is a Maximum-Entropy toolbox for networks, released as a
python3 module.</p>
<p>NEMtropy provides the user with a state of the art solver for a range
variety of Maximum Entropy Networks models derived from the ERGM family.
This module allows you to solve the desired model and generate a number
of randomized graphs from the original one: the so-called <em>graphs
ensemble</em>.</p>
<p>NEMtropy builds on the current literature on the matter, improving both
in speed of convergence and in the scale of the feasible networks. To
explore Maximum-Entropy modeling on networks, checkout <a class="reference external" href="https://meh.imtlucca.it/">Maximum Entropy
Hub</a>.</p>
<p>The models implemented in NEMtropy are presented in a forthcoming
<a class="reference external" href="https://arxiv.org/abs/2101.12625">paper</a> on arXiv. If you use the
module for your scientific research, please consider citing us:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@misc</span><span class="p">{</span><span class="n">vallarano2021fast</span><span class="p">,</span>
<span class="n">title</span><span class="o">=</span><span class="p">{</span><span class="n">Fast</span> <span class="ow">and</span> <span class="n">scalable</span> <span class="n">likelihood</span> <span class="n">maximization</span> <span class="k">for</span> <span class="n">Exponential</span> <span class="n">Random</span> <span class="n">Graph</span> <span class="n">Models</span><span class="p">},</span>
<span class="n">author</span><span class="o">=</span><span class="p">{</span><span class="n">Nicolò</span> <span class="n">Vallarano</span> <span class="ow">and</span> <span class="n">Matteo</span> <span class="n">Bruno</span> <span class="ow">and</span> <span class="n">Emiliano</span> <span class="n">Marchese</span> <span class="ow">and</span> <span class="n">Giuseppe</span> <span class="n">Trapani</span> <span class="ow">and</span> <span class="n">Fabio</span> <span class="n">Saracco</span> <span class="ow">and</span> <span class="n">Tiziano</span> <span class="n">Squartini</span> <span class="ow">and</span> <span class="n">Giulio</span> <span class="n">Cimini</span> <span class="ow">and</span> <span class="n">Mario</span> <span class="n">Zanon</span><span class="p">},</span>
<span class="n">year</span><span class="o">=</span><span class="p">{</span><span class="mi">2021</span><span class="p">},</span>
<span class="n">eprint</span><span class="o">=</span><span class="p">{</span><span class="mf">2101.12625</span><span class="p">},</span>
<span class="n">archivePrefix</span><span class="o">=</span><span class="p">{</span><span class="n">arXiv</span><span class="p">},</span>
<span class="n">primaryClass</span><span class="o">=</span><span class="p">{</span><span class="n">physics</span><span class="o">.</span><span class="n">data</span><span class="o">-</span><span class="n">an</span><span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
<p>To import the module:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">NEMtropy</span>
</pre></div>
</div>
<div class="section" id="currently-implemented-models">
<h1>Currently Implemented Models<a class="headerlink" href="#currently-implemented-models" title="Permalink to this headline"></a></h1>
<p>The main feature of NEMtropy is (but not limited to) <em>network
randomization</em>. The specific kind of network to randomize and property
to preserve defines the model you need:</p>
<ul class="simple">
<li><p><strong>UBCM</strong> <em>Undirected Binary Configuration Model</em> <a class="reference external" href="#1">[1]</a></p></li>
<li><p><strong>UECM</strong> <em>Undirected Enhanced Configuration Model</em> <a class="reference external" href="#1">[1]</a></p></li>
<li><p><strong>DBCM</strong> <em>Directed Binary Configuration Model</em> <a class="reference external" href="#1">[1]</a></p></li>
<li><p><strong>DECM</strong> <em>Directed Enhanced Configuration Model</em> <a class="reference external" href="#1">[1]</a></p></li>
<li><p><strong>CReMa</strong> <a class="reference external" href="#2">[2]</a></p></li>
</ul>
<p>The following table may helps you identify the model that fits your
needs in function of the type of network you are working with; for
in-depth discussion please see the references.</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 36%" />
<col style="width: 33%" />
<col style="width: 31%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>[…]</p></th>
<th class="head"><p>Undirected Graph</p></th>
<th class="head"><p>Directed Graph</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><strong>Binary Graph</strong></p></td>
<td><p><em>UBCM</em></p></td>
<td><p><em>DBCM</em></p></td>
</tr>
<tr class="row-odd"><td><p><strong>Weighted Graph</strong></p></td>
<td><p><em>UECM</em>, <em>CReMa</em></p></td>
<td><p><em>DECM</em>, <em>CReMa</em></p></td>
</tr>
</tbody>
</table>
<p><em>References</em></p>
<ul class="simple">
<li><p>[1] Squartini, Tiziano, Rossana Mastrandrea, and Diego Garlaschelli.
“Unbiased sampling of network ensembles.” New Journal of Physics 17.2
(2015): 023052. <a class="reference external" href="https://arxiv.org/abs/1406.1197">https://arxiv.org/abs/1406.1197</a></p></li>
<li><p>[2] Parisi, Federica, Tiziano Squartini, and Diego Garlaschelli. “A
faster horse on a safer trail: generalized inference for the
efficient reconstruction of weighted networks.” New Journal of
Physics 22.5 (2020): 053053. <a class="reference external" href="https://arxiv.org/abs/1811.09829">https://arxiv.org/abs/1811.09829</a></p></li>
</ul>
</div>
<div class="section" id="basic-functionalities">
<h1>Basic functionalities<a class="headerlink" href="#basic-functionalities" title="Permalink to this headline"></a></h1>
<p>NEMtropy can be installed via pip. You can get it from your terminal:</p>
<dl class="simple">
<dt><a href="#id1"><span class="problematic" id="id2">``</span></a><a href="#id3"><span class="problematic" id="id4">`</span></a></dt><dd><p>$ pip install NEMtropy</p>
</dd>
</dl>
<p><a href="#id5"><span class="problematic" id="id6">``</span></a><a href="#id7"><span class="problematic" id="id8">`</span></a></p>
<p>If you already install the package and wish to upgrade it,
you can simply type from your terminal:</p>
<dl class="simple">
<dt><a href="#id9"><span class="problematic" id="id10">``</span></a><a href="#id11"><span class="problematic" id="id12">`</span></a></dt><dd><p>$ pip install NEMtropy –upgrade</p>
</dd>
</dl>
<p><a href="#id13"><span class="problematic" id="id14">``</span></a><a href="#id15"><span class="problematic" id="id16">`</span></a></p>
</div>
<div class="section" id="dependencies">
<h1>Dependencies<a class="headerlink" href="#dependencies" title="Permalink to this headline"></a></h1>
<p>This package has been developed for Python 3.5 but it should be compatible with other versions. It works on numpy arrays and it uses numba and multiprocessing to speed up the computation. Feel free to send an inquiry (and please do it!) if you find any incompatibility.</p>
<p>NEMtropy uses &lt;code&gt;numba&lt;/code&gt; library. It can be installed via pip by running
in your terminal the following command:</p>
<dl class="simple">
<dt><a href="#id17"><span class="problematic" id="id18">``</span></a><a href="#id19"><span class="problematic" id="id20">`</span></a></dt><dd><p>$ pip install numba</p>
</dd>
</dl>
<p><a href="#id21"><span class="problematic" id="id22">``</span></a><a href="#id23"><span class="problematic" id="id24">`</span></a></p>
<p>For &lt;code&gt;python3.5&lt;/code&gt; users the correct command is the following:</p>
<dl class="simple">
<dt><a href="#id25"><span class="problematic" id="id26">``</span></a><a href="#id27"><span class="problematic" id="id28">`</span></a></dt><dd><p>$ pip install –prefer-binary numba</p>
</dd>
</dl>
<p><a href="#id29"><span class="problematic" id="id30">``</span></a><a href="#id31"><span class="problematic" id="id32">`</span></a></p>
<p>It avoids an error during the installation of &lt;code&gt;llvmlite&lt;/code&gt; due to
the absence of its wheel in &lt;code&gt;python3.5&lt;/code&gt;.</p>
<div class="section" id="guide">
<h2>Guide<a class="headerlink" href="#guide" title="Permalink to this headline"></a></h2>
<div class="toctree-wrapper compound">
Expand Down Expand Up @@ -102,6 +189,7 @@ <h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Pe
<h3><a href="#">Table of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">NEMtropy documentation</a></li>
<li><a class="reference internal" href="#currently-implemented-models">Currently Implemented Models</a></li>
<li><a class="reference internal" href="#basic-functionalities">Basic functionalities</a></li>
<li><a class="reference internal" href="#dependencies">Dependencies</a><ul>
<li><a class="reference internal" href="#guide">Guide</a></li>
Expand Down
2 changes: 1 addition & 1 deletion docs/build/html/searchindex.js
View file

Large diffs are not rendered by default.

Loading

0 comments on commit 65c896a

Please sign in to comment.