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Developing new models
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---------------------
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This document describes how to develop a new analysis model.
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What is a model?
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================
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A model defines the behaviour of the agents with a view to assessing their effects on the system as a whole.
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In practice, a model consists of at least two parts:
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* Python module: the actual code that describes the behaviour.
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* Setting up the variables in the Simulation Settings JSON file.
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This separation allows us to run the simulation with different agents.
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Models Code
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===========
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All the models are imported to the main file. The initialization look like this:
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.. code:: python
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import settings
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networkStatus = {} # Dict that will contain the status of every agent in the network
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sentimentCorrelationNodeArray = []
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for x in range(0, settings.number_of_nodes):
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sentimentCorrelationNodeArray.append({'id': x})
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# Initialize agent states. Let's assume everyone is normal.
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init_states = [{'id': 0, } for _ in range(settings.number_of_nodes)]
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# add keys as as necessary, but "id" must always refer to that state category
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A new model have to inherit the BaseBehaviour class which is in the same module.
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There are two basics methods:
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* __init__
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* step: used to define the behaviour over time.
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Variable Initialization
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=======================
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The different parameters of the model have to be initialize in the Simulation Settings JSON file which will be
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passed as a parameter to the simulation.
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.. code:: json
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{
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"agent": ["SISaModel","ControlModelM2"],
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"neutral_discontent_spon_prob": 0.04,
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"neutral_discontent_infected_prob": 0.04,
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"neutral_content_spon_prob": 0.18,
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"neutral_content_infected_prob": 0.02,
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"discontent_neutral": 0.13,
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"discontent_content": 0.07,
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"variance_d_c": 0.02,
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"content_discontent": 0.009,
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"variance_c_d": 0.003,
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"content_neutral": 0.088,
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"standard_variance": 0.055,
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"prob_neutral_making_denier": 0.035,
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"prob_infect": 0.075,
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"prob_cured_healing_infected": 0.035,
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"prob_cured_vaccinate_neutral": 0.035,
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"prob_vaccinated_healing_infected": 0.035,
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"prob_vaccinated_vaccinate_neutral": 0.035,
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"prob_generate_anti_rumor": 0.035
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}
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In this file you will also define the models you are going to simulate. You can simulate as many models as you want.
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The simulation returns one result for each model. For the usage, see :doc:`usage`.
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Example Model
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=============
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In this section, we will implement a Sentiment Correlation Model.
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The class would look like this:
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.. code:: python
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from ..BaseBehaviour import *
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from .. import sentimentCorrelationNodeArray
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class SentimentCorrelationModel(BaseBehaviour):
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def __init__(self, environment=None, agent_id=0, state=()):
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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self.outside_effects_prob = environment.environment_params['outside_effects_prob']
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self.anger_prob = environment.environment_params['anger_prob']
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self.joy_prob = environment.environment_params['joy_prob']
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self.sadness_prob = environment.environment_params['sadness_prob']
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self.disgust_prob = environment.environment_params['disgust_prob']
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self.time_awareness = []
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for i in range(4): # In this model we have 4 sentiments
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self.time_awareness.append(0) # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
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sentimentCorrelationNodeArray[self.id][self.env.now] = 0
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def step(self, now):
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self.behaviour() # Method which define the behaviour
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super().step(now)
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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
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-----
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First of all, you need to install the package. See :doc:`installation` for installation instructions.
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Simulation Settings
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===================
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Once installed, before running a simulation, you need to configure it.
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* In the settings.py file you will find the configuration of the network.
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.. code:: python
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# Network settings
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network_type = 1
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number_of_nodes = 1000
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max_time = 50
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num_trials = 1
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timeout = 2
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* In the Simulation Settings JSON file, you will find the configuration of the models.
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Network Types
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=============
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There are three types of network implemented, but you could add more.
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.. code:: python
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if settings.network_type == 0:
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G = nx.complete_graph(settings.number_of_nodes)
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if settings.network_type == 1:
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G = nx.barabasi_albert_graph(settings.number_of_nodes, 10)
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if settings.network_type == 2:
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G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
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# More types of networks can be added here
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Models Settings
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===============
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After having configured the simulation, the next step is setting up the variables of the models.
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For this, you will need to modify the Simulation Settings JSON file.
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.. code:: json
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{
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"agent": ["SISaModel","ControlModelM2"],
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"neutral_discontent_spon_prob": 0.04,
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"neutral_discontent_infected_prob": 0.04,
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"neutral_content_spon_prob": 0.18,
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"neutral_content_infected_prob": 0.02,
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"discontent_neutral": 0.13,
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"discontent_content": 0.07,
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"variance_d_c": 0.02,
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"content_discontent": 0.009,
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"variance_c_d": 0.003,
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"content_neutral": 0.088,
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"standard_variance": 0.055,
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"prob_neutral_making_denier": 0.035,
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"prob_infect": 0.075,
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"prob_cured_healing_infected": 0.035,
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"prob_cured_vaccinate_neutral": 0.035,
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"prob_vaccinated_healing_infected": 0.035,
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"prob_vaccinated_vaccinate_neutral": 0.035,
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"prob_generate_anti_rumor": 0.035
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}
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In this file you will define the different models you are going to simulate. You can simulate as many models
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as you want.
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After setting up the models, you have to initialize the parameters of each one. You will find the parameters needed
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in the documentation of each model.
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Parameter validation will fail if a required parameter without a default has not been provided.
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Running the Simulation
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======================
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After setting all the configuration, you will be able to run the simulation. All you need to do is execute:
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.. code:: bash
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python soil.py
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The simulation will return a dynamic graph .gexf file which could be visualized with
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`Gephi <https://gephi.org/users/download/>`__.
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It will also return one .png picture for each model simulated.
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<div class="section" id="developing-new-models">
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<h1>Developing new models<a class="headerlink" href="#developing-new-models" title="Permalink to this headline">¶</a></h1>
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<p>This document describes how to develop a new analysis model.</p>
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<div class="section" id="what-is-a-model">
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<h2>What is a model?<a class="headerlink" href="#what-is-a-model" title="Permalink to this headline">¶</a></h2>
|
||||
<p>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:</p>
|
||||
<ul class="simple">
|
||||
<li>Python module: the actual code that describes the behaviour.</li>
|
||||
<li>Setting up the variables in the Simulation Settings JSON file.</li>
|
||||
</ul>
|
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<p>This separation allows us to run the simulation with different agents.</p>
|
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</div>
|
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<div class="section" id="models-code">
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<h2>Models Code<a class="headerlink" href="#models-code" title="Permalink to this headline">¶</a></h2>
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<p>All the models are imported to the main file. The initialization look like this:</p>
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<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">settings</span>
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<span class="n">networkStatus</span> <span class="o">=</span> <span class="p">{}</span> <span class="c1"># Dict that will contain the status of every agent in the network</span>
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<span class="n">sentimentCorrelationNodeArray</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">settings</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">):</span>
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<span class="n">sentimentCorrelationNodeArray</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s1">'id'</span><span class="p">:</span> <span class="n">x</span><span class="p">})</span>
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<span class="c1"># Initialize agent states. Let's assume everyone is normal.</span>
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<span class="n">init_states</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">'id'</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="p">}</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">)]</span>
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<span class="c1"># add keys as as necessary, but "id" must always refer to that state category</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>A new model have to inherit the BaseBehaviour class which is in the same module.
|
||||
There are two basics methods:</p>
|
||||
<ul class="simple">
|
||||
<li>__init__</li>
|
||||
<li>step: used to define the behaviour over time.</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class="section" id="variable-initialization">
|
||||
<h2>Variable Initialization<a class="headerlink" href="#variable-initialization" title="Permalink to this headline">¶</a></h2>
|
||||
<p>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.</p>
|
||||
<div class="code json highlight-default"><div class="highlight"><pre><span></span><span class="p">{</span>
|
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<span class="s2">"agent"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"SISaModel"</span><span class="p">,</span><span class="s2">"ControlModelM2"</span><span class="p">],</span>
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<span class="s2">"neutral_discontent_spon_prob"</span><span class="p">:</span> <span class="mf">0.04</span><span class="p">,</span>
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<span class="s2">"neutral_discontent_infected_prob"</span><span class="p">:</span> <span class="mf">0.04</span><span class="p">,</span>
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<span class="s2">"neutral_content_spon_prob"</span><span class="p">:</span> <span class="mf">0.18</span><span class="p">,</span>
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||||
<span class="s2">"neutral_content_infected_prob"</span><span class="p">:</span> <span class="mf">0.02</span><span class="p">,</span>
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<span class="s2">"discontent_neutral"</span><span class="p">:</span> <span class="mf">0.13</span><span class="p">,</span>
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||||
<span class="s2">"discontent_content"</span><span class="p">:</span> <span class="mf">0.07</span><span class="p">,</span>
|
||||
<span class="s2">"variance_d_c"</span><span class="p">:</span> <span class="mf">0.02</span><span class="p">,</span>
|
||||
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<span class="s2">"content_discontent"</span><span class="p">:</span> <span class="mf">0.009</span><span class="p">,</span>
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||||
<span class="s2">"variance_c_d"</span><span class="p">:</span> <span class="mf">0.003</span><span class="p">,</span>
|
||||
<span class="s2">"content_neutral"</span><span class="p">:</span> <span class="mf">0.088</span><span class="p">,</span>
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||||
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||||
<span class="s2">"standard_variance"</span><span class="p">:</span> <span class="mf">0.055</span><span class="p">,</span>
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<span class="s2">"prob_neutral_making_denier"</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
|
||||
|
||||
<span class="s2">"prob_infect"</span><span class="p">:</span> <span class="mf">0.075</span><span class="p">,</span>
|
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|
||||
<span class="s2">"prob_cured_healing_infected"</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
|
||||
<span class="s2">"prob_cured_vaccinate_neutral"</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
|
||||
|
||||
<span class="s2">"prob_vaccinated_healing_infected"</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
|
||||
<span class="s2">"prob_vaccinated_vaccinate_neutral"</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
|
||||
<span class="s2">"prob_generate_anti_rumor"</span><span class="p">:</span> <span class="mf">0.035</span>
|
||||
<span class="p">}</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>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. For the usage, see <a class="reference internal" href="usage.html"><span class="doc">Usage</span></a>.</p>
|
||||
</div>
|
||||
<div class="section" id="example-model">
|
||||
<h2>Example Model<a class="headerlink" href="#example-model" title="Permalink to this headline">¶</a></h2>
|
||||
<p>In this section, we will implement a Sentiment Correlation Model.</p>
|
||||
<p>The class would look like this:</p>
|
||||
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">..BaseBehaviour</span> <span class="k">import</span> <span class="o">*</span>
|
||||
<span class="kn">from</span> <span class="nn">..</span> <span class="k">import</span> <span class="n">sentimentCorrelationNodeArray</span>
|
||||
|
||||
<span class="k">class</span> <span class="nc">SentimentCorrelationModel</span><span class="p">(</span><span class="n">BaseBehaviour</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">environment</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">agent_id</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">state</span><span class="o">=</span><span class="p">()):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">__init__</span><span class="p">(</span><span class="n">environment</span><span class="o">=</span><span class="n">environment</span><span class="p">,</span> <span class="n">agent_id</span><span class="o">=</span><span class="n">agent_id</span><span class="p">,</span> <span class="n">state</span><span class="o">=</span><span class="n">state</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">outside_effects_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">'outside_effects_prob'</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">anger_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">'anger_prob'</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">joy_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">'joy_prob'</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">sadness_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">'sadness_prob'</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">disgust_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">'disgust_prob'</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">time_awareness</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span> <span class="c1"># In this model we have 4 sentiments</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">time_awareness</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust</span>
|
||||
<span class="n">sentimentCorrelationNodeArray</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">id</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">env</span><span class="o">.</span><span class="n">now</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">now</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">behaviour</span><span class="p">()</span> <span class="c1"># Method which define the behaviour</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">now</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The variables will be modified by the user, so you have to include them in the Simulation Settings JSON file.</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
</div>
|
||||
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|
||||
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|
||||
<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
|
||||
<div class="sphinxsidebarwrapper">
|
||||
<h3><a href="index.html">Table Of Contents</a></h3>
|
||||
<ul>
|
||||
<li><a class="reference internal" href="#">Developing new models</a><ul>
|
||||
<li><a class="reference internal" href="#what-is-a-model">What is a model?</a></li>
|
||||
<li><a class="reference internal" href="#models-code">Models Code</a></li>
|
||||
<li><a class="reference internal" href="#variable-initialization">Variable Initialization</a></li>
|
||||
<li><a class="reference internal" href="#example-model">Example Model</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
<div class="relations">
|
||||
<h3>Related Topics</h3>
|
||||
<ul>
|
||||
<li><a href="index.html">Documentation overview</a><ul>
|
||||
<li>Previous: <a href="usage.html" title="previous chapter">Usage</a></li>
|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
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|
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||||
<div class="section" id="usage">
|
||||
<h1>Usage<a class="headerlink" href="#usage" title="Permalink to this headline">¶</a></h1>
|
||||
<p>First of all, you need to install the package. See <a class="reference internal" href="installation.html"><span class="doc">Installation</span></a> for installation instructions.</p>
|
||||
<div class="section" id="simulation-settings">
|
||||
<h2>Simulation Settings<a class="headerlink" href="#simulation-settings" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Once installed, before running a simulation, you need to configure it.</p>
|
||||
<ul>
|
||||
<li><p class="first">In the settings.py file you will find the configuration of the network.</p>
|
||||
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="c1"># Network settings</span>
|
||||
<span class="n">network_type</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="n">number_of_nodes</span> <span class="o">=</span> <span class="mi">1000</span>
|
||||
<span class="n">max_time</span> <span class="o">=</span> <span class="mi">50</span>
|
||||
<span class="n">num_trials</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="n">timeout</span> <span class="o">=</span> <span class="mi">2</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</li>
|
||||
<li><p class="first">In the Simulation Settings JSON file, you will find the configuration of the models.</p>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class="section" id="network-types">
|
||||
<h2>Network Types<a class="headerlink" href="#network-types" title="Permalink to this headline">¶</a></h2>
|
||||
<p>There are three types of network implemented, but you could add more.</p>
|
||||
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">network_type</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">complete_graph</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">network_type</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">barabasi_albert_graph</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">network_type</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">margulis_gabber_galil_graph</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="c1"># More types of networks can be added here</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section" id="models-settings">
|
||||
<h2>Models Settings<a class="headerlink" href="#models-settings" title="Permalink to this headline">¶</a></h2>
|
||||
<p>After having configured the simulation, the next step is setting up the variables of the models.
|
||||
For this, you will need to modify the Simulation Settings JSON file.</p>
|
||||
<div class="code json highlight-default"><div class="highlight"><pre><span></span><span class="p">{</span>
|
||||
<span class="s2">"agent"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"SISaModel"</span><span class="p">,</span><span class="s2">"ControlModelM2"</span><span class="p">],</span>
|
||||
|
||||
<span class="s2">"neutral_discontent_spon_prob"</span><span class="p">:</span> <span class="mf">0.04</span><span class="p">,</span>
|
||||
<span class="s2">"neutral_discontent_infected_prob"</span><span class="p">:</span> <span class="mf">0.04</span><span class="p">,</span>
|
||||
<span class="s2">"neutral_content_spon_prob"</span><span class="p">:</span> <span class="mf">0.18</span><span class="p">,</span>
|
||||
<span class="s2">"neutral_content_infected_prob"</span><span class="p">:</span> <span class="mf">0.02</span><span class="p">,</span>
|
||||
|
||||
<span class="s2">"discontent_neutral"</span><span class="p">:</span> <span class="mf">0.13</span><span class="p">,</span>
|
||||
<span class="s2">"discontent_content"</span><span class="p">:</span> <span class="mf">0.07</span><span class="p">,</span>
|
||||
<span class="s2">"variance_d_c"</span><span class="p">:</span> <span class="mf">0.02</span><span class="p">,</span>
|
||||
|
||||
<span class="s2">"content_discontent"</span><span class="p">:</span> <span class="mf">0.009</span><span class="p">,</span>
|
||||
<span class="s2">"variance_c_d"</span><span class="p">:</span> <span class="mf">0.003</span><span class="p">,</span>
|
||||
<span class="s2">"content_neutral"</span><span class="p">:</span> <span class="mf">0.088</span><span class="p">,</span>
|
||||
|
||||
<span class="s2">"standard_variance"</span><span class="p">:</span> <span class="mf">0.055</span><span class="p">,</span>
|
||||
|
||||
|
||||
<span class="s2">"prob_neutral_making_denier"</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
|
||||
|
||||
<span class="s2">"prob_infect"</span><span class="p">:</span> <span class="mf">0.075</span><span class="p">,</span>
|
||||
|
||||
<span class="s2">"prob_cured_healing_infected"</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
|
||||
<span class="s2">"prob_cured_vaccinate_neutral"</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
|
||||
|
||||
<span class="s2">"prob_vaccinated_healing_infected"</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
|
||||
<span class="s2">"prob_vaccinated_vaccinate_neutral"</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
|
||||
<span class="s2">"prob_generate_anti_rumor"</span><span class="p">:</span> <span class="mf">0.035</span>
|
||||
<span class="p">}</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>In this file you will define the different models you are going to simulate. You can simulate as many models
|
||||
as you want.</p>
|
||||
<p>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.</p>
|
||||
<p>Parameter validation will fail if a required parameter without a default has not been provided.</p>
|
||||
</div>
|
||||
<div class="section" id="running-the-simulation">
|
||||
<h2>Running the Simulation<a class="headerlink" href="#running-the-simulation" title="Permalink to this headline">¶</a></h2>
|
||||
<p>After setting all the configuration, you will be able to run the simulation. All you need to do is execute:</p>
|
||||
<div class="code bash highlight-default"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="n">soil</span><span class="o">.</span><span class="n">py</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The simulation will return a dynamic graph .gexf file which could be visualized with
|
||||
<a class="reference external" href="https://gephi.org/users/download/">Gephi</a>.</p>
|
||||
<p>It will also return one .png picture for each model simulated.</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
|
||||
<div class="sphinxsidebarwrapper">
|
||||
<h3><a href="index.html">Table Of Contents</a></h3>
|
||||
<ul>
|
||||
<li><a class="reference internal" href="#">Usage</a><ul>
|
||||
<li><a class="reference internal" href="#simulation-settings">Simulation Settings</a></li>
|
||||
<li><a class="reference internal" href="#network-types">Network Types</a></li>
|
||||
<li><a class="reference internal" href="#models-settings">Models Settings</a></li>
|
||||
<li><a class="reference internal" href="#running-the-simulation">Running the Simulation</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
<div class="relations">
|
||||
<h3>Related Topics</h3>
|
||||
<ul>
|
||||
<li><a href="index.html">Documentation overview</a><ul>
|
||||
<li>Previous: <a href="installation.html" title="previous chapter">Installation</a></li>
|
||||
<li>Next: <a href="models.html" title="next chapter">Developing new models</a></li>
|
||||
</ul></li>
|
||||
</ul>
|
||||
</div>
|
||||
<div role="note" aria-label="source link">
|
||||
<h3>This Page</h3>
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<ul class="this-page-menu">
|
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<li><a href="_sources/usage.rst.txt"
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rel="nofollow">Show Source</a></li>
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</ul>
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</div>
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<h3>Quick search</h3>
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<form class="search" action="search.html" method="get">
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<div><input type="text" name="q" /></div>
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<div><input type="submit" value="Go" /></div>
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<div class="clearer"></div>
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</div>
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<div class="footer">
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©2017, GSI.
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Powered by <a href="http://sphinx-doc.org/">Sphinx 1.5.1</a>
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& <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.9</a>
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<a href="_sources/usage.rst.txt"
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rel="nofollow">Page source</a>
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</div>
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|
||||
</body>
|
||||
</html>
|
||||
@ -0,0 +1,112 @@
|
||||
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 Simulation 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.number_of_nodes):
|
||||
sentimentCorrelationNodeArray.append({'id': x})
|
||||
# Initialize agent states. Let's assume everyone is normal.
|
||||
init_states = [{'id': 0, } for _ in range(settings.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. 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.
|
||||
@ -0,0 +1,98 @@
|
||||
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.py file you will find the configuration of the network.
|
||||
|
||||
.. code:: python
|
||||
|
||||
# Network settings
|
||||
network_type = 1
|
||||
number_of_nodes = 1000
|
||||
max_time = 50
|
||||
num_trials = 1
|
||||
timeout = 2
|
||||
|
||||
* In the Simulation Settings JSON file, you will 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 Simulation Settings JSON file.
|
||||
|
||||
.. 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.
|
||||
|
||||
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
|
||||
|
||||
python 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.
|
||||
Loading…
Reference in New Issue