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mirror of https://github.com/gsi-upm/soil synced 2025-01-25 18:38:11 +00:00
* Pandas integration
* Improved environment
* Logging and data dumps
* Tests
* Added Finite State Machine models
* Rewritten ipython notebook and documentation
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J. Fernando Sánchez 2017-06-20 17:45:43 +02:00
parent 764177c634
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__pycache__/
.idea/
.ipynb_checkpoints/
*.png
*.gexf
.*
results
soil_output
docs/_build*
build/*
dist/*

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include requirements.txt
include test-requirements.txt
include README.rst
graft soil

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#[Soil](https://github.com/gsi-upm/soil)
The purpose of Soil (SOcial network sImuLator) is provding an Agent-based Social Simulator written in Python for Social Networks.
In order to see quickly how to use Soil, you can follow the following [tutorial](https://github.com/gsi-upm/soil/blob/master/soil_tutorial.ipynb).
# [Soil](https://github.com/gsi-upm/soil)
Soil is an extensible and user-friendly Agent-based Social Simulator for Social Networks.
Learn how to run your own simulations with our [documentation](http://soilsim.readthedocs.io).
Follow our [tutorial](notebooks/soil_tutorial.ipynb) to develop your own agent models.
@Copyright GSI - Universidad Politécnica de Madrid 2017

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import soil
soil.main()
import pdb

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# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
config: d241ad8149638a17f61def88be87f400
tags: 645f666f9bcd5a90fca523b33c5a78b7

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.. Soil documentation master file, created by
sphinx-quickstart on Tue Apr 25 12:48:56 2017.
You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive.
Welcome to Soil's documentation!
================================
Soil is an Agent-based Social Simulator in Python for modelling and simulation of Social Networks.
.. toctree::
:maxdepth: 2
:caption: Learn more about soil:
installation
usage
models
.. Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`

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Installation
------------
The latest version can be installed through GitLab.
.. code:: bash
git clone https://lab.cluster.gsi.dit.upm.es/soil/soil.git

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Developing new models
---------------------
This document describes how to develop a new analysis model.
What is a model?
================
A model defines the behaviour of the agents with a view to assessing their effects on the system as a whole.
In practice, a model consists of at least two parts:
* Python module: the actual code that describes the behaviour.
* Setting up the variables in the Settings JSON file.
This separation allows us to run the simulation with different agents.
Models Code
===========
All the models are imported to the main file. The initialization look like this:
.. code:: python
import settings
networkStatus = {} # Dict that will contain the status of every agent in the network
sentimentCorrelationNodeArray = []
for x in range(0, settings.network_params["number_of_nodes"]):
sentimentCorrelationNodeArray.append({'id': x})
# Initialize agent states. Let's assume everyone is normal.
init_states = [{'id': 0, } for _ in range(settings.network_params["number_of_nodes"])]
# add keys as as necessary, but "id" must always refer to that state category
A new model have to inherit the BaseBehaviour class which is in the same module.
There are two basics methods:
* __init__
* step: used to define the behaviour over time.
Variable Initialization
=======================
The different parameters of the model have to be initialize in the Simulation Settings JSON file which will be
passed as a parameter to the simulation.
.. code:: json
{
"agent": ["SISaModel","ControlModelM2"],
"neutral_discontent_spon_prob": 0.04,
"neutral_discontent_infected_prob": 0.04,
"neutral_content_spon_prob": 0.18,
"neutral_content_infected_prob": 0.02,
"discontent_neutral": 0.13,
"discontent_content": 0.07,
"variance_d_c": 0.02,
"content_discontent": 0.009,
"variance_c_d": 0.003,
"content_neutral": 0.088,
"standard_variance": 0.055,
"prob_neutral_making_denier": 0.035,
"prob_infect": 0.075,
"prob_cured_healing_infected": 0.035,
"prob_cured_vaccinate_neutral": 0.035,
"prob_vaccinated_healing_infected": 0.035,
"prob_vaccinated_vaccinate_neutral": 0.035,
"prob_generate_anti_rumor": 0.035
}
In this file you will also define the models you are going to simulate. You can simulate as many models as you want.
The simulation returns one result for each model, executing each model separately. For the usage, see :doc:`usage`.
Example Model
=============
In this section, we will implement a Sentiment Correlation Model.
The class would look like this:
.. code:: python
from ..BaseBehaviour import *
from .. import sentimentCorrelationNodeArray
class SentimentCorrelationModel(BaseBehaviour):
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.outside_effects_prob = environment.environment_params['outside_effects_prob']
self.anger_prob = environment.environment_params['anger_prob']
self.joy_prob = environment.environment_params['joy_prob']
self.sadness_prob = environment.environment_params['sadness_prob']
self.disgust_prob = environment.environment_params['disgust_prob']
self.time_awareness = []
for i in range(4): # In this model we have 4 sentiments
self.time_awareness.append(0) # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
sentimentCorrelationNodeArray[self.id][self.env.now] = 0
def step(self, now):
self.behaviour() # Method which define the behaviour
super().step(now)
The variables will be modified by the user, so you have to include them in the Simulation Settings JSON file.

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Usage
-----
First of all, you need to install the package. See :doc:`installation` for installation instructions.
Simulation Settings
===================
Once installed, before running a simulation, you need to configure it.
* In the Settings JSON file you will find the configuration of the network.
.. code:: python
{
"network_type": 1,
"number_of_nodes": 1000,
"max_time": 50,
"num_trials": 1,
"timeout": 2
}
* In the Settings JSON file, you will also find the configuration of the models.
Network Types
=============
There are three types of network implemented, but you could add more.
.. code:: python
if settings.network_type == 0:
G = nx.complete_graph(settings.number_of_nodes)
if settings.network_type == 1:
G = nx.barabasi_albert_graph(settings.number_of_nodes, 10)
if settings.network_type == 2:
G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
# More types of networks can be added here
Models Settings
===============
After having configured the simulation, the next step is setting up the variables of the models.
For this, you will need to modify the Settings JSON file again.
.. code:: json
{
"agent": ["SISaModel","ControlModelM2"],
"neutral_discontent_spon_prob": 0.04,
"neutral_discontent_infected_prob": 0.04,
"neutral_content_spon_prob": 0.18,
"neutral_content_infected_prob": 0.02,
"discontent_neutral": 0.13,
"discontent_content": 0.07,
"variance_d_c": 0.02,
"content_discontent": 0.009,
"variance_c_d": 0.003,
"content_neutral": 0.088,
"standard_variance": 0.055,
"prob_neutral_making_denier": 0.035,
"prob_infect": 0.075,
"prob_cured_healing_infected": 0.035,
"prob_cured_vaccinate_neutral": 0.035,
"prob_vaccinated_healing_infected": 0.035,
"prob_vaccinated_vaccinate_neutral": 0.035,
"prob_generate_anti_rumor": 0.035
}
In this file you will define the different models you are going to simulate. You can simulate as many models
as you want. Each model will be simulated separately.
After setting up the models, you have to initialize the parameters of each one. You will find the parameters needed
in the documentation of each model.
Parameter validation will fail if a required parameter without a default has not been provided.
Running the Simulation
======================
After setting all the configuration, you will be able to run the simulation. All you need to do is execute:
.. code:: bash
python3 soil.py
The simulation will return a dynamic graph .gexf file which could be visualized with
`Gephi <https://gephi.org/users/download/>`__.
It will also return one .png picture for each model simulated.

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border-top: 1px solid #ddd;
border-bottom: 1px solid #ddd;
margin: 1em 0 1em 0;
padding: 0.4em;
}
div.genindex-jumpbox {
border-top: 1px solid #ddd;
border-bottom: 1px solid #ddd;
margin: 1em 0 1em 0;
padding: 0.4em;
}
/* -- domain module index --------------------------------------------------- */
table.modindextable td {
padding: 2px;
border-collapse: collapse;
}
/* -- general body styles --------------------------------------------------- */
div.body p, div.body dd, div.body li, div.body blockquote {
-moz-hyphens: auto;
-ms-hyphens: auto;
-webkit-hyphens: auto;
hyphens: auto;
}
a.headerlink {
visibility: hidden;
}
h1:hover > a.headerlink,
h2:hover > a.headerlink,
h3:hover > a.headerlink,
h4:hover > a.headerlink,
h5:hover > a.headerlink,
h6:hover > a.headerlink,
dt:hover > a.headerlink,
caption:hover > a.headerlink,
p.caption:hover > a.headerlink,
div.code-block-caption:hover > a.headerlink {
visibility: visible;
}
div.body p.caption {
text-align: inherit;
}
div.body td {
text-align: left;
}
.first {
margin-top: 0 !important;
}
p.rubric {
margin-top: 30px;
font-weight: bold;
}
img.align-left, .figure.align-left, object.align-left {
clear: left;
float: left;
margin-right: 1em;
}
img.align-right, .figure.align-right, object.align-right {
clear: right;
float: right;
margin-left: 1em;
}
img.align-center, .figure.align-center, object.align-center {
display: block;
margin-left: auto;
margin-right: auto;
}
.align-left {
text-align: left;
}
.align-center {
text-align: center;
}
.align-right {
text-align: right;
}
/* -- sidebars -------------------------------------------------------------- */
div.sidebar {
margin: 0 0 0.5em 1em;
border: 1px solid #ddb;
padding: 7px 7px 0 7px;
background-color: #ffe;
width: 40%;
float: right;
}
p.sidebar-title {
font-weight: bold;
}
/* -- topics ---------------------------------------------------------------- */
div.topic {
border: 1px solid #ccc;
padding: 7px 7px 0 7px;
margin: 10px 0 10px 0;
}
p.topic-title {
font-size: 1.1em;
font-weight: bold;
margin-top: 10px;
}
/* -- admonitions ----------------------------------------------------------- */
div.admonition {
margin-top: 10px;
margin-bottom: 10px;
padding: 7px;
}
div.admonition dt {
font-weight: bold;
}
div.admonition dl {
margin-bottom: 0;
}
p.admonition-title {
margin: 0px 10px 5px 0px;
font-weight: bold;
}
div.body p.centered {
text-align: center;
margin-top: 25px;
}
/* -- tables ---------------------------------------------------------------- */
table.docutils {
border: 0;
border-collapse: collapse;
}
table caption span.caption-number {
font-style: italic;
}
table caption span.caption-text {
}
table.docutils td, table.docutils th {
padding: 1px 8px 1px 5px;
border-top: 0;
border-left: 0;
border-right: 0;
border-bottom: 1px solid #aaa;
}
table.footnote td, table.footnote th {
border: 0 !important;
}
th {
text-align: left;
padding-right: 5px;
}
table.citation {
border-left: solid 1px gray;
margin-left: 1px;
}
table.citation td {
border-bottom: none;
}
/* -- figures --------------------------------------------------------------- */
div.figure {
margin: 0.5em;
padding: 0.5em;
}
div.figure p.caption {
padding: 0.3em;
}
div.figure p.caption span.caption-number {
font-style: italic;
}
div.figure p.caption span.caption-text {
}
/* -- field list styles ----------------------------------------------------- */
table.field-list td, table.field-list th {
border: 0 !important;
}
.field-list ul {
margin: 0;
padding-left: 1em;
}
.field-list p {
margin: 0;
}
/* -- other body styles ----------------------------------------------------- */
ol.arabic {
list-style: decimal;
}
ol.loweralpha {
list-style: lower-alpha;
}
ol.upperalpha {
list-style: upper-alpha;
}
ol.lowerroman {
list-style: lower-roman;
}
ol.upperroman {
list-style: upper-roman;
}
dl {
margin-bottom: 15px;
}
dd p {
margin-top: 0px;
}
dd ul, dd table {
margin-bottom: 10px;
}
dd {
margin-top: 3px;
margin-bottom: 10px;
margin-left: 30px;
}
dt:target, .highlighted {
background-color: #fbe54e;
}
dl.glossary dt {
font-weight: bold;
font-size: 1.1em;
}
.optional {
font-size: 1.3em;
}
.sig-paren {
font-size: larger;
}
.versionmodified {
font-style: italic;
}
.system-message {
background-color: #fda;
padding: 5px;
border: 3px solid red;
}
.footnote:target {
background-color: #ffa;
}
.line-block {
display: block;
margin-top: 1em;
margin-bottom: 1em;
}
.line-block .line-block {
margin-top: 0;
margin-bottom: 0;
margin-left: 1.5em;
}
.guilabel, .menuselection {
font-family: sans-serif;
}
.accelerator {
text-decoration: underline;
}
.classifier {
font-style: oblique;
}
abbr, acronym {
border-bottom: dotted 1px;
cursor: help;
}
/* -- code displays --------------------------------------------------------- */
pre {
overflow: auto;
overflow-y: hidden; /* fixes display issues on Chrome browsers */
}
span.pre {
-moz-hyphens: none;
-ms-hyphens: none;
-webkit-hyphens: none;
hyphens: none;
}
td.linenos pre {
padding: 5px 0px;
border: 0;
background-color: transparent;
color: #aaa;
}
table.highlighttable {
margin-left: 0.5em;
}
table.highlighttable td {
padding: 0 0.5em 0 0.5em;
}
div.code-block-caption {
padding: 2px 5px;
font-size: small;
}
div.code-block-caption code {
background-color: transparent;
}
div.code-block-caption + div > div.highlight > pre {
margin-top: 0;
}
div.code-block-caption span.caption-number {
padding: 0.1em 0.3em;
font-style: italic;
}
div.code-block-caption span.caption-text {
}
div.literal-block-wrapper {
padding: 1em 1em 0;
}
div.literal-block-wrapper div.highlight {
margin: 0;
}
code.descname {
background-color: transparent;
font-weight: bold;
font-size: 1.2em;
}
code.descclassname {
background-color: transparent;
}
code.xref, a code {
background-color: transparent;
font-weight: bold;
}
h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
background-color: transparent;
}
.viewcode-link {
float: right;
}
.viewcode-back {
float: right;
font-family: sans-serif;
}
div.viewcode-block:target {
margin: -1px -10px;