mirror of
https://github.com/gsi-upm/soil
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Initial commit
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David García Martín
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include requirements.txt
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include test-requirements.txt
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include README.rst
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graft soil
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32
README.md
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README.md
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# [SOIL](https://github.com/gsi-upm/soil)
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#[Soil](https://github.com/gsi-upm/soil)
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Soil is an extensible and user-friendly Agent-based Social Simulator for Social Networks.
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Learn how to run your own simulations with our [documentation](http://soilsim.readthedocs.io).
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The purpose of Soil (SOcial network sImuLator) is provding an Agent-based Social Simulator written in Python for Social Networks.
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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).
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Follow our [tutorial](notebooks/soil_tutorial.ipynb) to develop your own agent models.
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If you use Soil in your research, don't forget to cite this paper:
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```bibtex
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@inbook{soil-gsi-conference-2017,
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author = "S{\'a}nchez, Jes{\'u}s M. and Iglesias, Carlos A. and S{\'a}nchez-Rada, J. Fernando",
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booktitle = "Advances in Practical Applications of Cyber-Physical Multi-Agent Systems: The PAAMS Collection",
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doi = "10.1007/978-3-319-59930-4_19",
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editor = "Demazeau Y., Davidsson P., Bajo J., Vale Z.",
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isbn = "978-3-319-59929-8",
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keywords = "soil;social networks;agent based social simulation;python",
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month = "June",
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organization = "PAAMS 2017",
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pages = "234-245",
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publisher = "Springer Verlag",
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series = "LNAI",
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title = "{S}oil: {A}n {A}gent-{B}ased {S}ocial {S}imulator in {P}ython for {M}odelling and {S}imulation of {S}ocial {N}etworks",
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url = "https://link.springer.com/chapter/10.1007/978-3-319-59930-4_19",
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volume = "10349",
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year = "2017",
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}
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```
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@Copyright GSI - Universidad Politécnica de Madrid 2017
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[](https://www.gsi.dit.upm.es)
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BIN
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clase_base.pyc
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docs/index.rst
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@ -8,38 +8,13 @@ Welcome to Soil's documentation!
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Soil is an Agent-based Social Simulator in Python for modelling and simulation of Social Networks.
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If you use Soil in your research, do not forget to cite this paper:
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.. code:: bibtex
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@inbook{soil-gsi-conference-2017,
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author = "S{\'a}nchez, Jes{\'u}s M. and Iglesias, Carlos A. and S{\'a}nchez-Rada, J. Fernando",
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booktitle = "Advances in Practical Applications of Cyber-Physical Multi-Agent Systems: The PAAMS Collection",
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doi = "10.1007/978-3-319-59930-4_19",
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editor = "Demazeau Y., Davidsson P., Bajo J., Vale Z.",
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isbn = "978-3-319-59929-8",
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keywords = "soil;social networks;agent based social simulation;python",
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month = "June",
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organization = "PAAMS 2017",
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pages = "234-245",
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publisher = "Springer Verlag",
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series = "LNAI",
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title = "{S}oil: {A}n {A}gent-{B}ased {S}ocial {S}imulator in {P}ython for {M}odelling and {S}imulation of {S}ocial {N}etworks",
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url = "https://link.springer.com/chapter/10.1007/978-3-319-59930-4_19",
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volume = "10349",
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year = "2017",
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}
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.. toctree::
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:maxdepth: 2
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:caption: Learn more about soil:
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installation
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quickstart
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Tutorial - Spreading news
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usage
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models
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docs/installation.rst
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Installation
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------------
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The easiest way to install Soil is through pip:
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The latest version can be installed through GitLab.
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.. code:: bash
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pip install soil
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Now test that it worked by running the command line tool
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.. code:: bash
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soil --help
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Or using soil programmatically:
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.. code:: python
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import soil
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print(soil.__version__)
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The latest version can be installed through `GitLab <https://lab.cluster.gsi.dit.upm.es/soil/soil.git>`_.
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git clone https://lab.cluster.gsi.dit.upm.es/soil/soil.git
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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 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.network_params["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.network_params["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, executing each model separately. 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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Quickstart
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----------
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This section shows how to run simulations from simulation configuration files.
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First of all, you need to install the package (See :doc:`installation`)
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Simulation configuration files are ``json`` or ``yaml`` files that define all the parameters of a simulation.
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Here's an example (``example.yml``).
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.. code:: yaml
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---
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name: MyExampleSimulation
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max_time: 50
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num_trials: 3
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timeout: 2
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network_params:
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network_type: barabasi_albert_graph
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n: 100
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m: 2
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agent_distribution:
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- agent_type: SISaModel
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weight: 1
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state:
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id: content
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- agent_type: SISaModel
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weight: 1
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state:
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id: discontent
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- agent_type: SISaModel
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weight: 8
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state:
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id: neutral
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environment_params:
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prob_infect: 0.075
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Now run the simulation with the command line tool:
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.. code:: bash
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soil example.yml
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Once the simulation finishes, its results will be stored in a folder named ``MyExampleSimulation``.
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Four types of objects are saved by default: a pickle of the simulation, a ``YAML`` representation of the simulation (to re-launch it), for every trial, a csv file with the content of the state of every network node and the environment parameters at every step of the simulation as well as the network in gephi format (``gexf``).
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.. code::
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soil_output
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├── Sim_prob_0
|
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│ ├── Sim_prob_0.dumped.yml
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│ ├── Sim_prob_0.simulation.pickle
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│ ├── Sim_prob_0_trial_0.environment.csv
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│ └── Sim_prob_0_trial_0.gexf
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This example configuration will run three trials of a simulation containing a randomly generated network.
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The 100 nodes in the network will be SISaModel agents, 10% of them will start in the content state, 10% in the discontent state, and the remaining 80% in the neutral state.
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All agents will have access to the environment, which only contains one variable, ``prob_infected``.
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The state of the agents will be updated every 2 seconds (``timeout``).
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|
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Network
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=======
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The network topology for the simulation can be loaded from an existing network file or generated with one of the random network generation methods from networkx.
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Loading a network
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#################
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To load an existing network, specify its path in the configuration:
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.. code:: yaml
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|
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---
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network_params:
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path: /tmp/mynetwork.gexf
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Soil will try to guess what networkx method to use to read the file based on its extension.
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However, we only test using ``gexf`` files.
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Generating a random network
|
||||
###########################
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||||
|
||||
To generate a random network using one of networkx's built-in methods, specify the `graph generation algorithm <https://networkx.github.io/documentation/development/reference/generators.html>`_ and other parameters.
|
||||
For example, the following configuration is equivalent to :code:`nx.complete_graph(100)`:
|
||||
|
||||
.. code:: yaml
|
||||
|
||||
network_params:
|
||||
network_type: complete_graph
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||||
n: 100
|
||||
|
||||
Environment
|
||||
============
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||||
The environment is the place where the shared state of the simulation is stored.
|
||||
For instance, the probability of certain events.
|
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The configuration file may specify the initial value of the environment parameters:
|
||||
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||||
.. code:: yaml
|
||||
|
||||
environment_params:
|
||||
daily_probability_of_earthquake: 0.001
|
||||
number_of_earthquakes: 0
|
||||
|
||||
Agents
|
||||
======
|
||||
Agents are a way of modelling behavior.
|
||||
Agents can be characterized with two variables: an agent type (``agent_type``) and its state.
|
||||
Only one agent is executed at a time (generally, every ``timeout`` seconds), and it has access to its state and the environment parameters.
|
||||
Through the environment, it can access the network topology and the state of other agents.
|
||||
|
||||
There are three three types of agents according to how they are added to the simulation: network agents, environment agent, and other agents.
|
||||
|
||||
Network Agents
|
||||
##############
|
||||
Network agents are attached to a node in the topology.
|
||||
The configuration file allows you to specify how agents will be mapped to topology nodes.
|
||||
|
||||
The simplest way is to specify a single type of agent.
|
||||
Hence, every node in the network will have an associated agent of that type.
|
||||
|
||||
.. code:: yaml
|
||||
|
||||
agent_type: SISaModel
|
||||
|
||||
It is also possible to add more than one type of agent to the simulation, and to control the ratio of each type (``weight``).
|
||||
For instance, with following configuration, it is five times more likely for a node to be assigned a CounterModel type than a SISaModel type.
|
||||
|
||||
.. code:: yaml
|
||||
|
||||
agent_distribution:
|
||||
- agent_type: SISaModel
|
||||
weight: 1
|
||||
- agent_type: CounterModel
|
||||
weight: 5
|
||||
|
||||
In addition to agent type, you may also add a custom initial state to the distribution.
|
||||
This is very useful to add the same agent type with different states.
|
||||
e.g., to populate the network with SISaModel, roughly 10% of them with a discontent state:
|
||||
|
||||
.. code:: yaml
|
||||
|
||||
agent_distribution:
|
||||
- agent_type: SISaModel
|
||||
weight: 9
|
||||
state:
|
||||
id: neutral
|
||||
- agent_type: SISaModel
|
||||
weight: 1
|
||||
state:
|
||||
id: discontent
|
||||
|
||||
Lastly, the configuration may include initial state for one or more nodes.
|
||||
For instance, to add a state for the two nodes in this configuration:
|
||||
|
||||
.. code:: yaml
|
||||
|
||||
agent_type: SISaModel
|
||||
network:
|
||||
network_type: complete_graph
|
||||
n: 2
|
||||
states:
|
||||
- id: content
|
||||
- id: discontent
|
||||
|
||||
|
||||
Or to add state only to specific nodes (by ``id``).
|
||||
For example, to apply special skills to Linux Torvalds in a simulation:
|
||||
|
||||
.. literalinclude:: ../examples/torvalds.yml
|
||||
:language: yaml
|
||||
|
||||
|
||||
Environment Agents
|
||||
##################
|
||||
In addition to network agents, more agents can be added to the simulation.
|
||||
These agens are programmed in much the same way as network agents, the only difference is that they will not be assigned to network nodes.
|
||||
|
||||
|
||||
.. code::
|
||||
|
||||
environment_agents:
|
||||
- agent_type: MyAgent
|
||||
state:
|
||||
mood: happy
|
||||
- agent_type: DummyAgent
|
||||
|
||||
|
||||
Visualizing the results
|
||||
=======================
|
||||
|
||||
The simulation will return a dynamic graph .gexf file which could be visualized with
|
||||
`Gephi <https://gephi.org/users/download/>`__.
|
||||
99
docs/usage.rst
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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.
|
||||
@ -1,24 +0,0 @@
|
||||
---
|
||||
name: simple
|
||||
dir_path: "/tmp/"
|
||||
num_trials: 3
|
||||
max_time: 100
|
||||
interval: 1
|
||||
network_params:
|
||||
generator: complete_graph
|
||||
n: 10
|
||||
network_agents:
|
||||
- agent_type: CounterModel
|
||||
weight: 1
|
||||
state:
|
||||
id: 0
|
||||
- agent_type: AggregatedCounter
|
||||
weight: 0.2
|
||||
environment_agents: []
|
||||
environment_params:
|
||||
am_i_complete: true
|
||||
default_state:
|
||||
incidents: 0
|
||||
states:
|
||||
- name: 'The first node'
|
||||
- name: 'The second node'
|
||||
@ -1,17 +0,0 @@
|
||||
default_state: {}
|
||||
environment_agents: []
|
||||
environment_params: {prob_neighbor_spread: 0.0, prob_tv_spread: 0.01}
|
||||
interval: 1
|
||||
max_time: 20
|
||||
name: Sim_prob_0
|
||||
network_agents:
|
||||
- agent_type: NewsSpread
|
||||
state: {has_tv: false}
|
||||
weight: 1
|
||||
- agent_type: NewsSpread
|
||||
state: {has_tv: true}
|
||||
weight: 2
|
||||
network_params: {generator: erdos_renyi_graph, n: 500, p: 0.1}
|
||||
num_trials: 1
|
||||
states:
|
||||
- {has_tv: true}
|
||||
@ -1,20 +0,0 @@
|
||||
import soil
|
||||
import random
|
||||
|
||||
class NewsSpread(soil.agents.FSM):
|
||||
@soil.agents.default_state
|
||||
@soil.agents.state
|
||||
def neutral(self):
|
||||
r = random.random()
|
||||
if self['has_tv'] and r < self.env['prob_tv_spread']:
|
||||
return self.infected
|
||||
return
|
||||
|
||||
@soil.agents.state
|
||||
def infected(self):
|
||||
prob_infect = self.env['prob_neighbor_spread']
|
||||
for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
|
||||
r = random.random()
|
||||
if r < prob_infect:
|
||||
neighbor.state['id'] = self.infected.id
|
||||
return
|
||||
@ -1,2 +0,0 @@
|
||||
balkian Torvalds {}
|
||||
anonymous Torvalds {}
|
||||
@ -1,14 +0,0 @@
|
||||
---
|
||||
name: torvalds_example
|
||||
max_time: 1
|
||||
interval: 2
|
||||
agent_type: CounterModel
|
||||
default_state:
|
||||
skill_level: 'beginner'
|
||||
network_params:
|
||||
path: 'torvalds.edgelist'
|
||||
states:
|
||||
Torvalds:
|
||||
skill_level: 'God'
|
||||
balkian:
|
||||
skill_level: 'developer'
|
||||
0
logo_gsi.png
Normal file → Executable file
0
logo_gsi.png
Normal file → Executable file
|
Before Width: | Height: | Size: 35 KiB After Width: | Height: | Size: 35 KiB |
0
logo_gsi.svg
Normal file → Executable file
0
logo_gsi.svg
Normal file → Executable file
|
Before Width: | Height: | Size: 18 KiB After Width: | Height: | Size: 18 KiB |
38
models/BaseBehaviour/BaseBehaviour.py
Executable file
38
models/BaseBehaviour/BaseBehaviour.py
Executable file
@ -0,0 +1,38 @@
|
||||
import settings
|
||||
from nxsim import BaseNetworkAgent
|
||||
from .. import networkStatus
|
||||
|
||||
|
||||
class BaseBehaviour(BaseNetworkAgent):
|
||||
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
self._attrs = {}
|
||||
|
||||
@property
|
||||
def attrs(self):
|
||||
now = self.env.now
|
||||
if now not in self._attrs:
|
||||
self._attrs[now] = {}
|
||||
return self._attrs[now]
|
||||
|
||||
@attrs.setter
|
||||
def attrs(self, value):
|
||||
self._attrs[self.env.now] = value
|
||||
|
||||
def run(self):
|
||||
while True:
|
||||
self.step(self.env.now)
|
||||
yield self.env.timeout(settings.network_params["timeout"])
|
||||
|
||||
def step(self, now):
|
||||
networkStatus['agent_%s'% self.id] = self.to_json()
|
||||
|
||||
def to_json(self):
|
||||
final = {}
|
||||
for stamp, attrs in self._attrs.items():
|
||||
for a in attrs:
|
||||
if a not in final:
|
||||
final[a] = {}
|
||||
final[a][stamp] = attrs[a]
|
||||
return final
|
||||
1
models/BaseBehaviour/__init__.py
Executable file
1
models/BaseBehaviour/__init__.py
Executable file
@ -0,0 +1 @@
|
||||
from .BaseBehaviour import BaseBehaviour
|
||||
367
models/TerroristModel/TerroristModel.py
Normal file
367
models/TerroristModel/TerroristModel.py
Normal file
@ -0,0 +1,367 @@
|
||||
import random
|
||||
import numpy as np
|
||||
from ..BaseBehaviour import *
|
||||
import settings
|
||||
import networkx as nx
|
||||
|
||||
|
||||
|
||||
POPULATION = 0
|
||||
LEADERS = 1
|
||||
HAVEN = 2
|
||||
TRAININGENV = 3
|
||||
|
||||
NON_RADICAL = 0
|
||||
NEUTRAL = 1
|
||||
RADICAL = 2
|
||||
|
||||
POPNON =0
|
||||
POPNE=1
|
||||
POPRAD=2
|
||||
|
||||
HAVNON=3
|
||||
HAVNE=4
|
||||
HAVRAD=5
|
||||
|
||||
LEADER=6
|
||||
|
||||
TRAINING = 7
|
||||
|
||||
|
||||
class TerroristModel(BaseBehaviour):
|
||||
num_agents = 0
|
||||
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
|
||||
self.population = settings.network_params["number_of_nodes"] * settings.environment_params['initial_population']
|
||||
self.havens = settings.network_params["number_of_nodes"] * settings.environment_params['initial_havens']
|
||||
self.training_enviroments = settings.network_params["number_of_nodes"] * settings.environment_params['initial_training_enviroments']
|
||||
|
||||
self.initial_radicalism = settings.environment_params['initial_radicalism']
|
||||
self.information_spread_intensity = settings.environment_params['information_spread_intensity']
|
||||
self.influence = settings.environment_params['influence']
|
||||
self.relative_inequality = settings.environment_params['relative_inequality']
|
||||
self.additional_influence = settings.environment_params['additional_influence']
|
||||
|
||||
if TerroristModel.num_agents < self.population:
|
||||
self.state['type'] = POPULATION
|
||||
TerroristModel.num_agents = TerroristModel.num_agents + 1
|
||||
random1 = random.random()
|
||||
if random1 < 0.7:
|
||||
self.state['id'] = NON_RADICAL
|
||||
self.state['fstatus'] = POPNON
|
||||
elif random1 >= 0.7 and random1 < 0.9:
|
||||
self.state['id'] = NEUTRAL
|
||||
self.state['fstatus'] = POPNE
|
||||
elif random1 >= 0.9:
|
||||
self.state['id'] = RADICAL
|
||||
self.state['fstatus'] = POPRAD
|
||||
|
||||
elif TerroristModel.num_agents < self.havens + self.population:
|
||||
self.state['type'] = HAVEN
|
||||
TerroristModel.num_agents = TerroristModel.num_agents + 1
|
||||
random2 = random.random()
|
||||
random1 = random2 + self.initial_radicalism
|
||||
if random1 < 1.2:
|
||||
self.state['id'] = NON_RADICAL
|
||||
self.state['fstatus'] = HAVNON
|
||||
elif random1 >= 1.2 and random1 < 1.6:
|
||||
self.state['id'] = NEUTRAL
|
||||
self.state['fstatus'] = HAVNE
|
||||
elif random1 >= 1.6:
|
||||
self.state['id'] = RADICAL
|
||||
self.state['fstatus'] = HAVRAD
|
||||
|
||||
elif TerroristModel.num_agents < self.training_enviroments + self.havens + self.population:
|
||||
self.state['type'] = TRAININGENV
|
||||
self.state['fstatus'] = TRAINING
|
||||
TerroristModel.num_agents = TerroristModel.num_agents + 1
|
||||
|
||||
def step(self, now):
|
||||
if self.state['type'] == POPULATION:
|
||||
self.population_and_leader_conduct()
|
||||
if self.state['type'] == LEADERS:
|
||||
self.population_and_leader_conduct()
|
||||
if self.state['type'] == HAVEN:
|
||||
self.haven_conduct()
|
||||
if self.state['type'] == TRAININGENV:
|
||||
self.training_enviroment_conduct()
|
||||
|
||||
self.attrs['status'] = self.state['id']
|
||||
self.attrs['type'] = self.state['type']
|
||||
self.attrs['radicalism'] = self.state['rad']
|
||||
self.attrs['fstatus'] = self.state['fstatus']
|
||||
super().step(now)
|
||||
|
||||
def population_and_leader_conduct(self):
|
||||
if self.state['id'] == NON_RADICAL:
|
||||
if self.state['rad'] == 0.000:
|
||||
self.state['rad'] = self.set_radicalism()
|
||||
self.non_radical_behaviour()
|
||||
if self.state['id'] == NEUTRAL:
|
||||
if self.state['rad'] == 0.000:
|
||||
self.state['rad'] = self.set_radicalism()
|
||||
while self.state['id'] == RADICAL:
|
||||
self.radical_behaviour()
|
||||
break
|
||||
self.neutral_behaviour()
|
||||
if self.state['id'] == RADICAL:
|
||||
if self.state['rad'] == 0.000:
|
||||
self.state['rad'] = self.set_radicalism()
|
||||
self.radical_behaviour()
|
||||
|
||||
def haven_conduct(self):
|
||||
non_radical_neighbors = self.get_neighboring_agents(state_id=NON_RADICAL)
|
||||
neutral_neighbors = self.get_neighboring_agents(state_id=NEUTRAL)
|
||||
radical_neighbors = self.get_neighboring_agents(state_id=RADICAL)
|
||||
|
||||
neighbors_of_non_radical = len(neutral_neighbors) + len(radical_neighbors)
|
||||
neighbors_of_neutral = len(non_radical_neighbors) + len(radical_neighbors)
|
||||
neighbors_of_radical = len(non_radical_neighbors) + len(neutral_neighbors)
|
||||
threshold = 8
|
||||
if (len(non_radical_neighbors) > neighbors_of_non_radical) and len(non_radical_neighbors) >= threshold:
|
||||
self.state['id'] = NON_RADICAL
|
||||
elif (len(neutral_neighbors) > neighbors_of_neutral) and len(neutral_neighbors) >= threshold:
|
||||
self.state['id'] = NEUTRAL
|
||||
elif (len(radical_neighbors) > neighbors_of_radical) and len(radical_neighbors) >= threshold:
|
||||
self.state['id'] = RADICAL
|
||||
|
||||
if self.state['id'] == NEUTRAL:
|
||||
for neighbor in non_radical_neighbors:
|
||||
neighbor.state['rad'] = neighbor.state['rad'] + (self.influence + self.additional_influence) * self.information_spread_intensity
|
||||
if neighbor.state['rad'] >= 0.3 and neighbor.state['rad'] <= 0.59:
|
||||
neighbor.state['id'] = NEUTRAL
|
||||
if neighbor.state['type'] == POPULATION:
|
||||
neighbor.state['fstatus'] = POPNE
|
||||
elif neighbor.state['type'] == HAVEN:
|
||||
neighbor.state['fstatus'] = HAVNE
|
||||
elif neighbor.state['rad'] > 0.59:
|
||||
neighbor.state['rad'] = 0.59
|
||||
neighbor.state['id'] = NEUTRAL
|
||||
if neighbor.state['type'] == POPULATION:
|
||||
neighbor.state['fstatus'] = POPNE
|
||||
elif neighbor.state['type'] == HAVEN:
|
||||
neighbor.state['fstatus'] = HAVNE
|
||||
|
||||
if self.state['id'] == RADICAL:
|
||||
|
||||
for neighbor in non_radical_neighbors:
|
||||
neighbor.state['rad'] = neighbor.state['rad'] + (self.influence + self.additional_influence) * self.information_spread_intensity
|
||||
if neighbor.state['rad'] >= 0.3 and neighbor.state['rad'] <= 0.59:
|
||||
neighbor.state['id'] = NEUTRAL
|
||||
if neighbor.state['type'] == POPULATION:
|
||||
neighbor.state['fstatus'] = POPNE
|
||||
elif neighbor.state['type'] == HAVEN:
|
||||
neighbor.state['fstatus'] = HAVNE
|
||||
elif neighbor.state['rad'] > 0.59:
|
||||
neighbor.state['rad'] = 0.59
|
||||
neighbor.state['id'] = NEUTRAL
|
||||
if neighbor.state['type'] == POPULATION:
|
||||
neighbor.state['fstatus'] = POPNE
|
||||
elif neighbor.state['type'] == HAVEN:
|
||||
neighbor.state['fstatus'] = HAVNE
|
||||
|
||||
for neighbor in neutral_neighbors:
|
||||
neighbor.state['rad'] = neighbor.state['rad'] + (self.influence + self.additional_influence) * self.information_spread_intensity
|
||||
if neighbor.state['rad'] >= 0.6:
|
||||
neighbor.state['id'] = RADICAL
|
||||
if neighbor.state['type'] != HAVEN and neighbor.state['type']!=TRAININGENV:
|
||||
if neighbor.state['rad'] >= 0.62:
|
||||
if create_leader(neighbor):
|
||||
neighbor.state['type'] = LEADERS
|
||||
neighbor.state['fstatus'] = LEADER
|
||||
# elif neighbor.state['type'] == LEADERS:
|
||||
# neighbor.state['type'] = POPULATION
|
||||
# neighbor.state['fstatus'] = POPRAD
|
||||
elif neighbor.state['type'] == POPULATION:
|
||||
neighbor.state['fstatus'] = POPRAD
|
||||
elif neighbor.state['type'] == HAVEN:
|
||||
neighbor.state['fstatus'] = HAVRAD
|
||||
|
||||
def training_enviroment_conduct(self):
|
||||
self.state['id'] = RADICAL
|
||||
self.state['rad'] = 1
|
||||
neighbors = self.get_neighboring_agents()
|
||||
for neighbor in neighbors:
|
||||
if neighbor.state['id'] == NON_RADICAL:
|
||||
neighbor.state['rad'] = neighbor.state['rad'] + (self.influence + self.additional_influence) * self.information_spread_intensity
|
||||
if neighbor.state['rad'] >= 0.3 and self.state['rad'] <= 0.59:
|
||||
neighbor.state['id'] = NEUTRAL
|
||||
if neighbor.state['type'] == POPULATION:
|
||||
neighbor.state['fstatus'] = POPNE
|
||||
elif neighbor.state['type'] == HAVEN:
|
||||
neighbor.state['fstatus'] = HAVNE
|
||||
elif neighbor.state['rad'] > 0.59:
|
||||
neighbor.state['rad'] = 0.59
|
||||
neighbor.state['id'] = NEUTRAL
|
||||
if neighbor.state['type'] == POPULATION:
|
||||
neighbor.state['fstatus'] = POPNE
|
||||
elif neighbor.state['type'] == HAVEN:
|
||||
neighbor.state['fstatus'] = HAVNE
|
||||
|
||||
|
||||
neighbor.state['rad'] = neighbor.state['rad'] + (neighbor.influence + neighbor.additional_influence) * neighbor.information_spread_intensity
|
||||
if neighbor.state['rad'] >= 0.3 and neighbor.state['rad'] <= 0.59:
|
||||
neighbor.state['id'] = NEUTRAL
|
||||
if neighbor.state['type'] == POPULATION:
|
||||
neighbor.state['fstatus'] = POPNE
|
||||
elif neighbor.state['type'] == HAVEN:
|
||||
neighbor.state['fstatus'] = HAVNE
|
||||
elif neighbor.state['rad'] >= 0.6:
|
||||
neighbor.state['id'] = RADICAL
|
||||
if neighbor.state['type'] != HAVEN and neighbor.state['type'] != TRAININGENV:
|
||||
if neighbor.state['rad'] >= 0.62:
|
||||
if create_leader(neighbor):
|
||||
neighbor.state['type'] = LEADERS
|
||||
neighbor.state['fstatus'] = LEADER
|
||||
# elif neighbor.state['type'] == LEADERS:
|
||||
# neighbor.state['type'] = POPULATION
|
||||
# neighbor.state['fstatus'] = POPRAD
|
||||
elif neighbor.state['type'] == POPULATION:
|
||||
neighbor.state['fstatus'] = POPRAD
|
||||
elif neighbor.state['type'] == HAVEN:
|
||||
neighbor.state['fstatus'] = HAVRAD
|
||||
|
||||
def non_radical_behaviour(self):
|
||||
neighbors = self.get_neighboring_agents()
|
||||
|
||||
for neighbor in neighbors:
|
||||
if neighbor.state['type'] == POPULATION:
|
||||
if neighbor.state['id'] == NEUTRAL or neighbor.state['id'] == RADICAL:
|
||||
self.state['rad'] = self.state['rad'] + self.influence * self.information_spread_intensity
|
||||
if self.state['rad'] >= 0.3 and self.state['rad'] <= 0.59:
|
||||
self.state['id'] = NEUTRAL
|
||||
|
||||
if self.state['type']==POPULATION:
|
||||
self.state['fstatus'] = POPNE
|
||||
elif self.state['type'] == HAVEN:
|
||||
self.state['fstatus'] = HAVNE
|
||||
elif self.state['rad'] > 0.59:
|
||||
self.state['rad'] = 0.59
|
||||
self.state['id'] = NEUTRAL
|
||||
if self.state['type']==POPULATION:
|
||||
self.state['fstatus'] = POPNE
|
||||
elif self.state['type'] == HAVEN:
|
||||
self.state['fstatus'] = HAVNE
|
||||
|
||||
elif neighbor.state['type'] == LEADERS:
|
||||
|
||||
if neighbor.state['id'] == NEUTRAL or neighbor.state['id'] == RADICAL:
|
||||
self.state['rad'] = self.state['rad'] + (self.influence + self.additional_influence) * self.information_spread_intensity
|
||||
if self.state['rad'] >= 0.3 and self.state['rad'] <= 0.59:
|
||||
self.state['id'] = NEUTRAL
|
||||
if self.state['type']==POPULATION:
|
||||
self.state['fstatus'] = POPNE
|
||||
elif self.state['type'] == HAVEN:
|
||||
self.state['fstatus'] = HAVNE
|
||||
elif self.state['rad'] > 0.59:
|
||||
self.state['rad'] = 0.59
|
||||
self.state['id'] = NEUTRAL
|
||||
if self.state['type']==POPULATION:
|
||||
self.state['fstatus'] = POPNE
|
||||
elif self.state['type'] == HAVEN:
|
||||
self.state['fstatus'] = HAVNE
|
||||
|
||||
|
||||
def neutral_behaviour(self):
|
||||
neighbors = self.get_neighboring_agents()
|
||||
for neighbor in neighbors:
|
||||
if neighbor.state['type'] == POPULATION:
|
||||
if neighbor.state['id'] == RADICAL:
|
||||
self.state['rad'] = self.state['rad'] + self.influence * self.information_spread_intensity
|
||||
if self.state['rad'] >= 0.6:
|
||||
self.state['id'] = RADICAL
|
||||
if self.state['type'] != HAVEN:
|
||||
if self.state['rad'] >= 0.62:
|
||||
if create_leader(self):
|
||||
self.state['type'] = LEADERS
|
||||
|
||||
self.state['fstatus'] = LEADER
|
||||
# elif self.state['type'] == LEADERS:
|
||||
# self.state['type'] = POPULATION
|
||||
# self.state['fstatus'] = POPRAD
|
||||
elif neighbor.state['type'] == POPULATION:
|
||||
self.state['fstatus'] = POPRAD
|
||||
elif self.state['type'] == HAVEN:
|
||||
self.state['fstatus'] = HAVRAD
|
||||
|
||||
|
||||
elif neighbor.state['type'] == LEADERS:
|
||||
if neighbor.state['id'] == RADICAL:
|
||||
self.state['rad'] = self.state['rad'] + (self.influence + self.additional_influence) * self.information_spread_intensity
|
||||
if self.state['rad'] >= 0.6:
|
||||
self.state['id'] = RADICAL
|
||||
if self.state['type'] != HAVEN:
|
||||
if self.state['rad'] >= 0.62:
|
||||
if create_leader(self):
|
||||
self.state['type'] = LEADERS
|
||||
self.state['fstatus'] = LEADER
|
||||
# elif self.state['type'] == LEADERS:
|
||||
# self.state['type'] = POPULATION
|
||||
# self.state['fstatus'] = POPRAD
|
||||
elif neighbor.state['type'] == POPULATION:
|
||||
self.state['fstatus'] = POPRAD
|
||||
elif self.state['type'] == HAVEN:
|
||||
self.state['fstatus'] = HAVRAD
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def radical_behaviour(self):
|
||||
neighbors = self.get_neighboring_agents(state_id=RADICAL)
|
||||
|
||||
for neighbor in neighbors:
|
||||
if self.state['rad']< neighbor.state['rad'] and self.state['type']== LEADERS and neighbor.state['type']==LEADERS:
|
||||
self.state['type'] = POPULATION
|
||||
self.state['fstatus'] = POPRAD
|
||||
|
||||
|
||||
def set_radicalism(self):
|
||||
if self.state['id'] == NON_RADICAL:
|
||||
radicalism = random.uniform(0.0, 0.29) * self.relative_inequality
|
||||
return radicalism
|
||||
elif self.state['id'] == NEUTRAL:
|
||||
radicalism = 0.3 + random.uniform(0.3, 0.59) * self.relative_inequality
|
||||
if radicalism >= 0.6:
|
||||
self.state['id'] = RADICAL
|
||||
return radicalism
|
||||
elif self.state['id'] == RADICAL:
|
||||
radicalism = 0.6 + random.uniform(0.6, 1.0) * self.relative_inequality
|
||||
return radicalism
|
||||
|
||||
def get_partition(agent):
|
||||
return settings.partition_param[agent.id]
|
||||
|
||||
def get_centrality(agent):
|
||||
return settings.centrality_param[agent.id]
|
||||
def get_centrality_given_id(id):
|
||||
return settings.centrality_param[id]
|
||||
|
||||
def get_leader(partition):
|
||||
if not bool(settings.leaders) or partition not in settings.leaders.keys():
|
||||
return None
|
||||
return settings.leaders[partition]
|
||||
|
||||
def set_leader(partition, agent):
|
||||
settings.leaders[partition] = agent.id
|
||||
|
||||
def create_leader(agent):
|
||||
my_partition = get_partition(agent)
|
||||
old_leader = get_leader(my_partition)
|
||||
|
||||
if old_leader == None:
|
||||
set_leader(my_partition, agent)
|
||||
return True
|
||||
else:
|
||||
my_centrality = get_centrality(agent)
|
||||
old_leader_centrality = get_centrality_given_id(old_leader)
|
||||
if my_centrality > old_leader_centrality:
|
||||
set_leader(my_partition, agent)
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
|
||||
1
models/TerroristModel/__init__.py
Normal file
1
models/TerroristModel/__init__.py
Normal file
@ -0,0 +1 @@
|
||||
from .TerroristModel import TerroristModel
|
||||
3
models/__init__.py
Executable file
3
models/__init__.py
Executable file
@ -0,0 +1,3 @@
|
||||
from .models import *
|
||||
from .BaseBehaviour import *
|
||||
from .TerroristModel import *
|
||||
7
models/models.py
Executable file
7
models/models.py
Executable file
@ -0,0 +1,7 @@
|
||||
import settings
|
||||
|
||||
networkStatus = {} # Dict that will contain the status of every agent in the network
|
||||
|
||||
# Initialize agent states. Let's assume everyone is normal and all types are population.
|
||||
init_states = [{'id': 0, 'type': 0, 'rad': 0, 'fstatus':0, } for _ in range(settings.network_params["number_of_nodes"])]
|
||||
|
||||
596
models_org.py
596
models_org.py
@ -1,596 +0,0 @@
|
||||
from nxsim import BaseNetworkAgent
|
||||
import numpy as np
|
||||
import random
|
||||
import settings
|
||||
|
||||
settings.init()
|
||||
|
||||
##############################
|
||||
# Variables initialization #
|
||||
##############################
|
||||
def init():
|
||||
global networkStatus
|
||||
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
|
||||
|
||||
|
||||
####################
|
||||
# Available models #
|
||||
####################
|
||||
|
||||
class BaseBehaviour(BaseNetworkAgent):
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
self._attrs = {}
|
||||
|
||||
@property
|
||||
def attrs(self):
|
||||
now = self.env.now
|
||||
if now not in self._attrs:
|
||||
self._attrs[now] = {}
|
||||
return self._attrs[now]
|
||||
|
||||
@attrs.setter
|
||||
def attrs(self, value):
|
||||
self._attrs[self.env.now] = value
|
||||
|
||||
def run(self):
|
||||
while True:
|
||||
self.step(self.env.now)
|
||||
yield self.env.timeout(settings.timeout)
|
||||
|
||||
def step(self, now):
|
||||
networkStatus['agent_%s'% self.id] = self.to_json()
|
||||
|
||||
def to_json(self):
|
||||
final = {}
|
||||
for stamp, attrs in self._attrs.items():
|
||||
for a in attrs:
|
||||
if a not in final:
|
||||
final[a] = {}
|
||||
final[a][stamp] = attrs[a]
|
||||
return final
|
||||
|
||||
class ControlModelM2(BaseBehaviour):
|
||||
#Init infected
|
||||
init_states[random.randint(0,settings.number_of_nodes-1)] = {'id':1}
|
||||
init_states[random.randint(0,settings.number_of_nodes-1)] = {'id':1}
|
||||
|
||||
# Init beacons
|
||||
init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 4}
|
||||
init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 4}
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
|
||||
self.prob_neutral_making_denier = np.random.normal(settings.prob_neutral_making_denier, settings.standard_variance)
|
||||
|
||||
self.prob_infect = np.random.normal(settings.prob_infect, settings.standard_variance)
|
||||
|
||||
self.prob_cured_healing_infected = np.random.normal(settings.prob_cured_healing_infected, settings.standard_variance)
|
||||
self.prob_cured_vaccinate_neutral = np.random.normal(settings.prob_cured_vaccinate_neutral, settings.standard_variance)
|
||||
|
||||
self.prob_vaccinated_healing_infected = np.random.normal(settings.prob_vaccinated_healing_infected, settings.standard_variance)
|
||||
self.prob_vaccinated_vaccinate_neutral = np.random.normal(settings.prob_vaccinated_vaccinate_neutral, settings.standard_variance)
|
||||
self.prob_generate_anti_rumor = np.random.normal(settings.prob_generate_anti_rumor, settings.standard_variance)
|
||||
|
||||
def step(self, now):
|
||||
|
||||
if self.state['id'] == 0: #Neutral
|
||||
self.neutral_behaviour()
|
||||
elif self.state['id'] == 1: #Infected
|
||||
self.infected_behaviour()
|
||||
elif self.state['id'] == 2: #Cured
|
||||
self.cured_behaviour()
|
||||
elif self.state['id'] == 3: #Vaccinated
|
||||
self.vaccinated_behaviour()
|
||||
elif self.state['id'] == 4: #Beacon-off
|
||||
self.beacon_off_behaviour()
|
||||
elif self.state['id'] == 5: #Beacon-on
|
||||
self.beacon_on_behaviour()
|
||||
|
||||
self.attrs['status'] = self.state['id']
|
||||
super().step(now)
|
||||
|
||||
|
||||
def neutral_behaviour(self):
|
||||
|
||||
# Infected
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
if len(infected_neighbors)>0:
|
||||
if random.random() < self.prob_neutral_making_denier:
|
||||
self.state['id'] = 3 # Vaccinated making denier
|
||||
|
||||
def infected_behaviour(self):
|
||||
|
||||
# Neutral
|
||||
neutral_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors:
|
||||
if random.random() < self.prob_infect:
|
||||
neighbor.state['id'] = 1 # Infected
|
||||
|
||||
def cured_behaviour(self):
|
||||
|
||||
# Vaccinate
|
||||
neutral_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors:
|
||||
if random.random() < self.prob_cured_vaccinate_neutral:
|
||||
neighbor.state['id'] = 3 # Vaccinated
|
||||
|
||||
# Cure
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors:
|
||||
if random.random() < self.prob_cured_healing_infected:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
|
||||
|
||||
def vaccinated_behaviour(self):
|
||||
|
||||
# Cure
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors:
|
||||
if random.random() < self.prob_cured_healing_infected:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
|
||||
|
||||
# Vaccinate
|
||||
neutral_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors:
|
||||
if random.random() < self.prob_cured_vaccinate_neutral:
|
||||
neighbor.state['id'] = 3 # Vaccinated
|
||||
|
||||
# Generate anti-rumor
|
||||
infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors_2:
|
||||
if random.random() < self.prob_generate_anti_rumor:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
|
||||
def beacon_off_behaviour(self):
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
if len(infected_neighbors) > 0:
|
||||
self.state['id'] == 5 #Beacon on
|
||||
|
||||
def beacon_on_behaviour(self):
|
||||
|
||||
# Cure (M2 feature added)
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors:
|
||||
if random.random() < self.prob_generate_anti_rumor:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
neutral_neighbors_infected = neighbor.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors_infected:
|
||||
if random.random() < self.prob_generate_anti_rumor:
|
||||
neighbor.state['id'] = 3 # Vaccinated
|
||||
infected_neighbors_infected = neighbor.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors_infected:
|
||||
if random.random() < self.prob_generate_anti_rumor:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
|
||||
|
||||
# Vaccinate
|
||||
neutral_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors:
|
||||
if random.random() < self.prob_cured_vaccinate_neutral:
|
||||
neighbor.state['id'] = 3 # Vaccinated
|
||||
|
||||
|
||||
class SpreadModelM2(BaseBehaviour):
|
||||
init_states[random.randint(0,settings.number_of_nodes)] = {'id':1}
|
||||
init_states[random.randint(0,settings.number_of_nodes)] = {'id':1}
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
|
||||
self.prob_neutral_making_denier = np.random.normal(settings.prob_neutral_making_denier, settings.standard_variance)
|
||||
|
||||
self.prob_infect = np.random.normal(settings.prob_infect, settings.standard_variance)
|
||||
|
||||
self.prob_cured_healing_infected = np.random.normal(settings.prob_cured_healing_infected, settings.standard_variance)
|
||||
self.prob_cured_vaccinate_neutral = np.random.normal(settings.prob_cured_vaccinate_neutral, settings.standard_variance)
|
||||
|
||||
self.prob_vaccinated_healing_infected = np.random.normal(settings.prob_vaccinated_healing_infected, settings.standard_variance)
|
||||
self.prob_vaccinated_vaccinate_neutral = np.random.normal(settings.prob_vaccinated_vaccinate_neutral, settings.standard_variance)
|
||||
self.prob_generate_anti_rumor = np.random.normal(settings.prob_generate_anti_rumor, settings.standard_variance)
|
||||
|
||||
def step(self, now):
|
||||
|
||||
if self.state['id'] == 0: #Neutral
|
||||
self.neutral_behaviour()
|
||||
elif self.state['id'] == 1: #Infected
|
||||
self.infected_behaviour()
|
||||
elif self.state['id'] == 2: #Cured
|
||||
self.cured_behaviour()
|
||||
elif self.state['id'] == 3: #Vaccinated
|
||||
self.vaccinated_behaviour()
|
||||
|
||||
self.attrs['status'] = self.state['id']
|
||||
super().step(now)
|
||||
|
||||
|
||||
def neutral_behaviour(self):
|
||||
|
||||
# Infected
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
if len(infected_neighbors)>0:
|
||||
if random.random() < self.prob_neutral_making_denier:
|
||||
self.state['id'] = 3 # Vaccinated making denier
|
||||
|
||||
def infected_behaviour(self):
|
||||
|
||||
# Neutral
|
||||
neutral_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors:
|
||||
if random.random() < self.prob_infect:
|
||||
neighbor.state['id'] = 1 # Infected
|
||||
|
||||
def cured_behaviour(self):
|
||||
|
||||
# Vaccinate
|
||||
neutral_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors:
|
||||
if random.random() < self.prob_cured_vaccinate_neutral:
|
||||
neighbor.state['id'] = 3 # Vaccinated
|
||||
|
||||
# Cure
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors:
|
||||
if random.random() < self.prob_cured_healing_infected:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
|
||||
|
||||
def vaccinated_behaviour(self):
|
||||
|
||||
# Cure
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors:
|
||||
if random.random() < self.prob_cured_healing_infected:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
|
||||
|
||||
# Vaccinate
|
||||
neutral_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors:
|
||||
if random.random() < self.prob_cured_vaccinate_neutral:
|
||||
neighbor.state['id'] = 3 # Vaccinated
|
||||
|
||||
# Generate anti-rumor
|
||||
infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors_2:
|
||||
if random.random() < self.prob_generate_anti_rumor:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
|
||||
|
||||
class SISaModel(BaseBehaviour):
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
|
||||
self.neutral_discontent_spon_prob = np.random.normal(settings.neutral_discontent_spon_prob, settings.standard_variance)
|
||||
self.neutral_discontent_infected_prob = np.random.normal(settings.neutral_discontent_infected_prob,settings.standard_variance)
|
||||
self.neutral_content_spon_prob = np.random.normal(settings.neutral_content_spon_prob,settings.standard_variance)
|
||||
self.neutral_content_infected_prob = np.random.normal(settings.neutral_content_infected_prob,settings.standard_variance)
|
||||
|
||||
self.discontent_neutral = np.random.normal(settings.discontent_neutral,settings.standard_variance)
|
||||
self.discontent_content = np.random.normal(settings.discontent_content,settings.variance_d_c)
|
||||
|
||||
self.content_discontent = np.random.normal(settings.content_discontent,settings.variance_c_d)
|
||||
self.content_neutral = np.random.normal(settings.content_neutral,settings.standard_variance)
|
||||
|
||||
def step(self, now):
|
||||
|
||||
if self.state['id'] == 0:
|
||||
self.neutral_behaviour()
|
||||
if self.state['id'] == 1:
|
||||
self.discontent_behaviour()
|
||||
if self.state['id'] == 2:
|
||||
self.content_behaviour()
|
||||
|
||||
self.attrs['status'] = self.state['id']
|
||||
super().step(now)
|
||||
|
||||
|
||||
def neutral_behaviour(self):
|
||||
|
||||
#Spontaneus effects
|
||||
if random.random() < self.neutral_discontent_spon_prob:
|
||||
self.state['id'] = 1
|
||||
if random.random() < self.neutral_content_spon_prob:
|
||||
self.state['id'] = 2
|
||||
|
||||
#Infected
|
||||
discontent_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
if random.random() < len(discontent_neighbors)*self.neutral_discontent_infected_prob:
|
||||
self.state['id'] = 1
|
||||
content_neighbors = self.get_neighboring_agents(state_id=2)
|
||||
if random.random() < len(content_neighbors)*self.neutral_content_infected_prob:
|
||||
self.state['id'] = 2
|
||||
|
||||
def discontent_behaviour(self):
|
||||
|
||||
#Healing
|
||||
if random.random() < self.discontent_neutral:
|
||||
self.state['id'] = 0
|
||||
|
||||
#Superinfected
|
||||
content_neighbors = self.get_neighboring_agents(state_id=2)
|
||||
if random.random() < len(content_neighbors)*self.discontent_content:
|
||||
self.state['id'] = 2
|
||||
|
||||
def content_behaviour(self):
|
||||
|
||||
#Healing
|
||||
if random.random() < self.content_neutral:
|
||||
self.state['id'] = 0
|
||||
|
||||
#Superinfected
|
||||
discontent_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
if random.random() < len(discontent_neighbors)*self.content_discontent:
|
||||
self.state['id'] = 1
|
||||
|
||||
|
||||
class BigMarketModel(BaseBehaviour):
|
||||
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
self.enterprises = settings.enterprises
|
||||
self.type = ""
|
||||
self.number_of_enterprises = len(settings.enterprises)
|
||||
|
||||
if self.id < self.number_of_enterprises: #Enterprises
|
||||
self.state['id']=self.id
|
||||
self.type="Enterprise"
|
||||
self.tweet_probability = settings.tweet_probability_enterprises[self.id]
|
||||
else: #normal users
|
||||
self.state['id']=self.number_of_enterprises
|
||||
self.type="User"
|
||||
self.tweet_probability = settings.tweet_probability_users
|
||||
self.tweet_relevant_probability = settings.tweet_relevant_probability
|
||||
self.tweet_probability_about = settings.tweet_probability_about #List
|
||||
self.sentiment_about = settings.sentiment_about #List
|
||||
|
||||
def step(self, now):
|
||||
|
||||
if(self.id < self.number_of_enterprises): # Ennterprise
|
||||
self.enterpriseBehaviour()
|
||||
else: # Usuario
|
||||
self.userBehaviour()
|
||||
for i in range(self.number_of_enterprises): # So that it never is set to 0 if there are not changes (logs)
|
||||
self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
|
||||
|
||||
super().step(now)
|
||||
|
||||
def enterpriseBehaviour(self):
|
||||
|
||||
if random.random()< self.tweet_probability: #Tweets
|
||||
aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodes neighbour users
|
||||
for x in aware_neighbors:
|
||||
if random.uniform(0,10) < 5:
|
||||
x.sentiment_about[self.id] += 0.1 #Increments for enterprise
|
||||
else:
|
||||
x.sentiment_about[self.id] -= 0.1 #Decrements for enterprise
|
||||
|
||||
# Establecemos limites
|
||||
if x.sentiment_about[self.id] > 1:
|
||||
x.sentiment_about[self.id] = 1
|
||||
if x.sentiment_about[self.id]< -1:
|
||||
x.sentiment_about[self.id] = -1
|
||||
|
||||
x.attrs['sentiment_enterprise_%s'% self.enterprises[self.id]] = x.sentiment_about[self.id]
|
||||
|
||||
|
||||
def userBehaviour(self):
|
||||
|
||||
if random.random() < self.tweet_probability: #Tweets
|
||||
if random.random() < self.tweet_relevant_probability: #Tweets something relevant
|
||||
#Tweet probability per enterprise
|
||||
for i in range(self.number_of_enterprises):
|
||||
random_num = random.random()
|
||||
if random_num < self.tweet_probability_about[i]:
|
||||
#The condition is fulfilled, sentiments are evaluated towards that enterprise
|
||||
if self.sentiment_about[i] < 0:
|
||||
#NEGATIVO
|
||||
self.userTweets("negative",i)
|
||||
elif self.sentiment_about[i] == 0:
|
||||
#NEUTRO
|
||||
pass
|
||||
else:
|
||||
#POSITIVO
|
||||
self.userTweets("positive",i)
|
||||
|
||||
def userTweets(self,sentiment,enterprise):
|
||||
aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodes neighbours users
|
||||
for x in aware_neighbors:
|
||||
if sentiment == "positive":
|
||||
x.sentiment_about[enterprise] +=0.003
|
||||
elif sentiment == "negative":
|
||||
x.sentiment_about[enterprise] -=0.003
|
||||
else:
|
||||
pass
|
||||
|
||||
# Establecemos limites
|
||||
if x.sentiment_about[enterprise] > 1:
|
||||
x.sentiment_about[enterprise] = 1
|
||||
if x.sentiment_about[enterprise] < -1:
|
||||
x.sentiment_about[enterprise] = -1
|
||||
|
||||
x.attrs['sentiment_enterprise_%s'% self.enterprises[enterprise]] = x.sentiment_about[enterprise]
|
||||
|
||||
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 = settings.outside_effects_prob
|
||||
self.anger_prob = settings.anger_prob
|
||||
self.joy_prob = settings.joy_prob
|
||||
self.sadness_prob = settings.sadness_prob
|
||||
self.disgust_prob = settings.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()
|
||||
super().step(now)
|
||||
|
||||
def behaviour(self):
|
||||
|
||||
angry_neighbors_1_time_step=[]
|
||||
joyful_neighbors_1_time_step=[]
|
||||
sad_neighbors_1_time_step=[]
|
||||
disgusted_neighbors_1_time_step=[]
|
||||
|
||||
|
||||
angry_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for x in angry_neighbors:
|
||||
if x.time_awareness[0] > (self.env.now-500):
|
||||
angry_neighbors_1_time_step.append(x)
|
||||
num_neighbors_angry = len(angry_neighbors_1_time_step)
|
||||
|
||||
|
||||
joyful_neighbors = self.get_neighboring_agents(state_id=2)
|
||||
for x in joyful_neighbors:
|
||||
if x.time_awareness[1] > (self.env.now-500):
|
||||
joyful_neighbors_1_time_step.append(x)
|
||||
num_neighbors_joyful = len(joyful_neighbors_1_time_step)
|
||||
|
||||
|
||||
sad_neighbors = self.get_neighboring_agents(state_id=3)
|
||||
for x in sad_neighbors:
|
||||
if x.time_awareness[2] > (self.env.now-500):
|
||||
sad_neighbors_1_time_step.append(x)
|
||||
num_neighbors_sad = len(sad_neighbors_1_time_step)
|
||||
|
||||
|
||||
disgusted_neighbors = self.get_neighboring_agents(state_id=4)
|
||||
for x in disgusted_neighbors:
|
||||
if x.time_awareness[3] > (self.env.now-500):
|
||||
disgusted_neighbors_1_time_step.append(x)
|
||||
num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
|
||||
|
||||
|
||||
anger_prob= settings.anger_prob+(len(angry_neighbors_1_time_step)*settings.anger_prob)
|
||||
joy_prob= settings.joy_prob+(len(joyful_neighbors_1_time_step)*settings.joy_prob)
|
||||
sadness_prob = settings.sadness_prob+(len(sad_neighbors_1_time_step)*settings.sadness_prob)
|
||||
disgust_prob = settings.disgust_prob+(len(disgusted_neighbors_1_time_step)*settings.disgust_prob)
|
||||
outside_effects_prob= settings.outside_effects_prob
|
||||
|
||||
|
||||
num = random.random()
|
||||
|
||||
|
||||
if(num<outside_effects_prob):
|
||||
self.state['id'] = random.randint(1,4)
|
||||
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=self.state['id'] #It is stored when it has been infected for the dynamic network
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
self.attrs['sentiment'] = self.state['id']
|
||||
|
||||
|
||||
|
||||
if(num<anger_prob):
|
||||
|
||||
self.state['id'] = 1
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=1
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
elif (num<joy_prob+anger_prob and num>anger_prob):
|
||||
|
||||
self.state['id'] = 2
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=2
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
|
||||
|
||||
|
||||
self.state['id'] = 3
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=3
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
elif (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_prob+anger_prob+joy_prob):
|
||||
|
||||
|
||||
self.state['id'] = 4
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=4
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
|
||||
self.attrs['sentiment'] = self.state['id']
|
||||
|
||||
|
||||
class BassModel(BaseBehaviour):
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
self.innovation_prob = settings.innovation_prob
|
||||
self.imitation_prob = settings.imitation_prob
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=0
|
||||
|
||||
def step(self, now):
|
||||
self.behaviour()
|
||||
super().step(now)
|
||||
|
||||
def behaviour(self):
|
||||
#Outside effects
|
||||
if random.random() < settings.innovation_prob:
|
||||
if self.state['id'] == 0:
|
||||
self.state['id'] = 1
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=1
|
||||
else:
|
||||
pass
|
||||
|
||||
self.attrs['status'] = self.state['id']
|
||||
return
|
||||
|
||||
#Imitation effects
|
||||
if self.state['id'] == 0:
|
||||
aware_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
num_neighbors_aware = len(aware_neighbors)
|
||||
if random.random() < (settings.imitation_prob*num_neighbors_aware):
|
||||
self.state['id'] = 1
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=1
|
||||
|
||||
else:
|
||||
pass
|
||||
self.attrs['status'] = self.state['id']
|
||||
|
||||
|
||||
class IndependentCascadeModel(BaseBehaviour):
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
self.innovation_prob = settings.innovation_prob
|
||||
self.imitation_prob = settings.imitation_prob
|
||||
self.time_awareness = 0
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=0
|
||||
|
||||
def step(self,now):
|
||||
self.behaviour()
|
||||
super().step(now)
|
||||
|
||||
def behaviour(self):
|
||||
aware_neighbors_1_time_step=[]
|
||||
#Outside effects
|
||||
if random.random() < settings.innovation_prob:
|
||||
if self.state['id'] == 0:
|
||||
self.state['id'] = 1
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=1
|
||||
self.time_awareness = self.env.now #To know when they have been infected
|
||||
else:
|
||||
pass
|
||||
|
||||
self.attrs['status'] = self.state['id']
|
||||
return
|
||||
|
||||
#Imitation effects
|
||||
if self.state['id'] == 0:
|
||||
aware_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for x in aware_neighbors:
|
||||
if x.time_awareness == (self.env.now-1):
|
||||
aware_neighbors_1_time_step.append(x)
|
||||
num_neighbors_aware = len(aware_neighbors_1_time_step)
|
||||
if random.random() < (settings.imitation_prob*num_neighbors_aware):
|
||||
self.state['id'] = 1
|
||||
sentimentCorrelationNodeArray[self.id][self.env.now]=1
|
||||
else:
|
||||
pass
|
||||
|
||||
self.attrs['status'] = self.state['id']
|
||||
return
|
||||
File diff suppressed because one or more lines are too long
1
requirements.txt
Normal file → Executable file
1
requirements.txt
Normal file → Executable file
@ -3,4 +3,3 @@ simpy
|
||||
networkx
|
||||
numpy
|
||||
matplotlib
|
||||
pyyaml
|
||||
|
||||
23
settings.json
Executable file
23
settings.json
Executable file
@ -0,0 +1,23 @@
|
||||
[
|
||||
{
|
||||
"network_type": 0,
|
||||
"number_of_nodes": 80,
|
||||
"max_time": 50,
|
||||
"num_trials": 1,
|
||||
"timeout": 2
|
||||
},
|
||||
|
||||
{
|
||||
"agent": ["TerroristModel"],
|
||||
|
||||
"initial_population": 0.85,
|
||||
"initial_havens": 0.1,
|
||||
"initial_training_enviroments": 0.05,
|
||||
|
||||
"initial_radicalism": 0.12,
|
||||
"relative_inequality": 0.33,
|
||||
"information_spread_intensity": 0.1,
|
||||
"influence": 0.4,
|
||||
"additional_influence": 0.1
|
||||
}
|
||||
]
|
||||
13
settings.py
Executable file
13
settings.py
Executable file
@ -0,0 +1,13 @@
|
||||
# General configuration
|
||||
import json
|
||||
|
||||
with open('settings.json', 'r') as f:
|
||||
settings = json.load(f)
|
||||
|
||||
network_params = settings[0]
|
||||
environment_params = settings[1]
|
||||
|
||||
centrality_param = {}
|
||||
partition_param={}
|
||||
leaders={}
|
||||
|
||||
39
setup.py
39
setup.py
@ -1,39 +0,0 @@
|
||||
import pip
|
||||
from setuptools import setup
|
||||
# parse_requirements() returns generator of pip.req.InstallRequirement objects
|
||||
from pip.req import parse_requirements
|
||||
from soil import __version__
|
||||
|
||||
try:
|
||||
install_reqs = parse_requirements(
|
||||
"requirements.txt", session=pip.download.PipSession())
|
||||
test_reqs = parse_requirements(
|
||||
"test-requirements.txt", session=pip.download.PipSession())
|
||||
except AttributeError:
|
||||
install_reqs = parse_requirements("requirements.txt")
|
||||
test_reqs = parse_requirements("test-requirements.txt")
|
||||
|
||||
install_reqs = [str(ir.req) for ir in install_reqs]
|
||||
test_reqs = [str(ir.req) for ir in test_reqs]
|
||||
|
||||
|
||||
setup(
|
||||
name='soil',
|
||||
packages=['soil'], # this must be the same as the name above
|
||||
version=__version__,
|
||||
description=('An Agent-Based Social Simulator for Social Networks'),
|
||||
author='J. Fernando Sanchez',
|
||||
author_email='jf.sanchez@upm.es',
|
||||
url='https://github.com/gsi-upm/soil', # use the URL to the github repo
|
||||
download_url='https://github.com/gsi-upm/soil/archive/{}.tar.gz'.format(
|
||||
__version__),
|
||||
keywords=['agent', 'social', 'simulator'],
|
||||
classifiers=[],
|
||||
install_requires=install_reqs,
|
||||
tests_require=test_reqs,
|
||||
setup_requires=['pytest-runner', ],
|
||||
include_package_data=True,
|
||||
entry_points={
|
||||
'console_scripts':
|
||||
['soil = soil.__init__:main']
|
||||
})
|
||||
BIN
sim_01/log.0.state.pickled
Executable file
BIN
sim_01/log.0.state.pickled
Executable file
Binary file not shown.
BIN
sim_01/log.1.state.pickled
Normal file
BIN
sim_01/log.1.state.pickled
Normal file
Binary file not shown.
@ -1,63 +0,0 @@
|
||||
---
|
||||
name: ControlModelM2_sim
|
||||
max_time: 50
|
||||
num_trials: 1
|
||||
timeout: 2
|
||||
network_params:
|
||||
generator: barabasi_albert_graph
|
||||
n: 100
|
||||
m: 2
|
||||
agent_distribution:
|
||||
- agent_type: ControlModelM2
|
||||
weight: 0.1
|
||||
state:
|
||||
id: 1
|
||||
- agent_type: ControlModelM2
|
||||
weight: 0.9
|
||||
state:
|
||||
id: 0
|
||||
environment_params:
|
||||
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
|
||||
standard_variance: 0.055
|
||||
---
|
||||
name: SISA_sm
|
||||
max_time: 50
|
||||
num_trials: 2
|
||||
timeout: 2
|
||||
network_params:
|
||||
generator: erdos_renyi_graph
|
||||
n: 10000
|
||||
p: 0.05
|
||||
#other_agents:
|
||||
# - agent_type: DrawingAgent
|
||||
agent_distribution:
|
||||
- agent_type: SISaModel
|
||||
weight: 1
|
||||
state:
|
||||
id: content
|
||||
- agent_type: SISaModel
|
||||
weight: 1
|
||||
state:
|
||||
id: neutral
|
||||
- agent_type: SISaModel
|
||||
weight: 1
|
||||
state:
|
||||
id: discontent
|
||||
environment_params:
|
||||
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
|
||||
215
soil.py
Executable file
215
soil.py
Executable file
@ -0,0 +1,215 @@
|
||||
from models import *
|
||||
from nxsim import NetworkSimulation
|
||||
# import numpy
|
||||
from matplotlib import pyplot as plt
|
||||
import networkx as nx
|
||||
import settings
|
||||
import models
|
||||
import math
|
||||
import json
|
||||
import operator
|
||||
import community
|
||||
|
||||
|
||||
|
||||
POPULATION = 0
|
||||
LEADERS = 1
|
||||
HAVEN = 2
|
||||
TRAINING = 3
|
||||
|
||||
NON_RADICAL = 0
|
||||
NEUTRAL = 1
|
||||
RADICAL = 2
|
||||
#################
|
||||
# Visualization #
|
||||
#################
|
||||
|
||||
def visualization(graph_name):
|
||||
|
||||
for x in range(0, settings.network_params["number_of_nodes"]):
|
||||
attributes = {}
|
||||
spells = []
|
||||
for attribute in models.networkStatus["agent_%s" % x]:
|
||||
if attribute == 'visible':
|
||||
lastvisible = False
|
||||
laststep = 0
|
||||
for t_step in models.networkStatus["agent_%s" % x][attribute]:
|
||||
nowvisible = models.networkStatus["agent_%s" % x][attribute][t_step]
|
||||
if nowvisible and not lastvisible:
|
||||
laststep = t_step
|
||||
if not nowvisible and lastvisible:
|
||||
spells.append((laststep, t_step))
|
||||
|
||||
lastvisible = nowvisible
|
||||
if lastvisible:
|
||||
spells.append((laststep, None))
|
||||
else:
|
||||
emotionStatusAux = []
|
||||
for t_step in models.networkStatus["agent_%s" % x][attribute]:
|
||||
prec = 2
|
||||
output = math.floor(models.networkStatus["agent_%s" % x][attribute][t_step] * (10 ** prec)) / (10 ** prec) # 2 decimals
|
||||
emotionStatusAux.append((output, t_step, t_step + settings.network_params["timeout"]))
|
||||
attributes[attribute] = emotionStatusAux
|
||||
if spells:
|
||||
G.add_node(x, attributes, spells=spells)
|
||||
else:
|
||||
G.add_node(x, attributes)
|
||||
|
||||
print("Done!")
|
||||
|
||||
|
||||
with open('data.txt', 'w') as outfile:
|
||||
json.dump(models.networkStatus, outfile, sort_keys=True, indent=4, separators=(',', ': '))
|
||||
|
||||
for node in range(settings.network_params["number_of_nodes"]):
|
||||
G.node[node]['x'] = G.node[node]['pos'][0]
|
||||
G.node[node]['y'] = G.node[node]['pos'][1]
|
||||
G.node[node]['viz'] = {"position": {"x": G.node[node]['pos'][0], "y": G.node[node]['pos'][1], "z": 0.0}}
|
||||
del (G.node[node]['pos'])
|
||||
|
||||
nx.write_gexf(G, graph_name+".gexf", version="1.2draft")
|
||||
|
||||
###########
|
||||
# Results #
|
||||
###########
|
||||
|
||||
def results(model_name):
|
||||
x_values = []
|
||||
neutral_values = []
|
||||
non_radical_values = []
|
||||
radical_values = []
|
||||
|
||||
attribute_plot = 'status'
|
||||
for time in range(0, settings.network_params["max_time"]):
|
||||
value_neutral = 0
|
||||
value_non_radical = 0
|
||||
value_radical = 0
|
||||
real_time = time * settings.network_params["timeout"]
|
||||
activity = False
|
||||
for x in range(0, settings.network_params["number_of_nodes"]):
|
||||
if attribute_plot in models.networkStatus["agent_%s" % x]:
|
||||
if real_time in models.networkStatus["agent_%s" % x][attribute_plot]:
|
||||
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == NON_RADICAL:
|
||||
value_non_radical += 1
|
||||
activity = True
|
||||
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == NEUTRAL:
|
||||
value_neutral += 1
|
||||
activity = True
|
||||
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == RADICAL:
|
||||
value_radical += 1
|
||||
activity = True
|
||||
|
||||
|
||||
if activity:
|
||||
x_values.append(real_time)
|
||||
neutral_values.append(value_neutral)
|
||||
non_radical_values.append(value_non_radical)
|
||||
radical_values.append(value_radical)
|
||||
activity = False
|
||||
|
||||
fig1 = plt.figure()
|
||||
ax1 = fig1.add_subplot(111)
|
||||
|
||||
non_radical_line = ax1.plot(x_values, non_radical_values, label='Non radical')
|
||||
neutral_line = ax1.plot(x_values, neutral_values, label='Neutral')
|
||||
radical_line = ax1.plot(x_values, radical_values, label='Radical')
|
||||
ax1.legend()
|
||||
fig1.savefig(model_name+'.png')
|
||||
plt.show()
|
||||
|
||||
###########
|
||||
# Results #
|
||||
###########
|
||||
|
||||
def resultadosTipo(model_name):
|
||||
x_values = []
|
||||
population_values = []
|
||||
leaders_values = []
|
||||
havens_values = []
|
||||
training_enviroments_values = []
|
||||
|
||||
attribute_plot = 'type'
|
||||
for time in range(0, settings.network_params["max_time"]):
|
||||
value_population = 0
|
||||
value_leaders = 0
|
||||
value_havens = 0
|
||||
value_training_enviroments = 0
|
||||
real_time = time * settings.network_params["timeout"]
|
||||
activity = False
|
||||
for x in range(0, settings.network_params["number_of_nodes"]):
|
||||
if attribute_plot in models.networkStatus["agent_%s" % x]:
|
||||
if real_time in models.networkStatus["agent_%s" % x][attribute_plot]:
|
||||
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == POPULATION:
|
||||
value_population += 1
|
||||
activity = True
|
||||
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == LEADERS:
|
||||
value_leaders += 1
|
||||
activity = True
|
||||
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == HAVEN:
|
||||
value_havens += 1
|
||||
activity = True
|
||||
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == TRAINING:
|
||||
value_training_enviroments += 1
|
||||
activity = True
|
||||
if activity:
|
||||
x_values.append(real_time)
|
||||
population_values.append(value_population)
|
||||
leaders_values.append(value_leaders)
|
||||
havens_values.append(value_havens)
|
||||
training_enviroments_values.append(value_training_enviroments)
|
||||
activity = False
|
||||
|
||||
fig2 = plt.figure()
|
||||
ax2 = fig2.add_subplot(111)
|
||||
|
||||
population_line = ax2.plot(x_values, population_values, label='Population')
|
||||
leaders_line = ax2.plot(x_values, leaders_values, label='Leader')
|
||||
havens_line = ax2.plot(x_values, havens_values, label='Havens')
|
||||
training_enviroments_line = ax2.plot(x_values, training_enviroments_values, label='Training Enviroments')
|
||||
ax2.legend()
|
||||
fig2.savefig(model_name+'_type'+'.png')
|
||||
plt.show()
|
||||
|
||||
####################
|
||||
# Network creation #
|
||||
####################
|
||||
|
||||
# nx.degree_centrality(G);
|
||||
|
||||
if settings.network_params["network_type"] == 0:
|
||||
G = nx.random_geometric_graph(settings.network_params["number_of_nodes"], 0.2)
|
||||
|
||||
settings.partition_param = community.best_partition(G)
|
||||
settings.centrality_param = nx.betweenness_centrality(G).copy()
|
||||
|
||||
|
||||
# print(settings.centrality_param)
|
||||
# print(settings.partition_param)
|
||||
# More types of networks can be added here
|
||||
|
||||
##############
|
||||
# Simulation #
|
||||
##############
|
||||
|
||||
agents = settings.environment_params['agent']
|
||||
|
||||
print("Using Agent(s): {agents}".format(agents=agents))
|
||||
|
||||
if len(agents) > 1:
|
||||
for agent in agents:
|
||||
sim = NetworkSimulation(topology=G, states=init_states, agent_type=locals()[agent], max_time=settings.network_params["max_time"],
|
||||
num_trials=settings.network_params["num_trials"], logging_interval=1.0, **settings.environment_params)
|
||||
sim.run_simulation()
|
||||
print(str(agent))
|
||||
results(str(agent))
|
||||
resultadosTipo(str(agent))
|
||||
visualization(str(agent))
|
||||
else:
|
||||
agent = agents[0]
|
||||
sim = NetworkSimulation(topology=G, states=init_states, agent_type=locals()[agent], max_time=settings.network_params["max_time"],
|
||||
num_trials=settings.network_params["num_trials"], logging_interval=1.0, **settings.environment_params)
|
||||
sim.run_simulation()
|
||||
results(str(agent))
|
||||
resultadosTipo(str(agent))
|
||||
|
||||
visualization(str(agent))
|
||||
394
soil.py~
Executable file
394
soil.py~
Executable file
@ -0,0 +1,394 @@
|
||||
from nxsim import NetworkSimulation
|
||||
from nxsim import BaseNetworkAgent
|
||||
from nxsim import BaseLoggingAgent
|
||||
from random import randint
|
||||
from matplotlib import pyplot as plt
|
||||
import random
|
||||
import numpy as np
|
||||
import networkx as nx
|
||||
import settings
|
||||
|
||||
|
||||
settings.init()
|
||||
|
||||
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,3)
|
||||
if settings.network_type == 2:
|
||||
G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
|
||||
|
||||
|
||||
myList=[]
|
||||
networkStatus=[]
|
||||
for x in range(0, settings.number_of_nodes):
|
||||
networkStatus.append({'id':x})
|
||||
|
||||
|
||||
|
||||
# # Just like subclassing a process in SimPy
|
||||
# class MyAgent(BaseNetworkAgent):
|
||||
# def __init__(self, environment=None, agent_id=0, state=()): # Make sure to have these three keyword arguments
|
||||
# super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
# # Add your own attributes here
|
||||
|
||||
# def run(self):
|
||||
# # Add your behaviors here
|
||||
|
||||
|
||||
|
||||
|
||||
class SentimentCorrelationModel(BaseNetworkAgent):
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
self.outside_effects_prob = settings.outside_effects_prob
|
||||
self.anger_prob = settings.anger_prob
|
||||
self.joy_prob = settings.joy_prob
|
||||
self.sadness_prob = settings.sadness_prob
|
||||
self.disgust_prob = settings.disgust_prob
|
||||
self.time_awareness=[]
|
||||
for i in range(4):
|
||||
self.time_awareness.append(0) #0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
|
||||
networkStatus[self.id][self.env.now]=0
|
||||
|
||||
|
||||
def run(self):
|
||||
while True:
|
||||
if self.env.now > 10:
|
||||
G.add_node(205)
|
||||
G.add_edge(205,0)
|
||||
angry_neighbors_1_time_step=[]
|
||||
joyful_neighbors_1_time_step=[]
|
||||
sad_neighbors_1_time_step=[]
|
||||
disgusted_neighbors_1_time_step=[]
|
||||
|
||||
|
||||
angry_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for x in angry_neighbors:
|
||||
if x.time_awareness[0] > (self.env.now-500):
|
||||
angry_neighbors_1_time_step.append(x)
|
||||
num_neighbors_angry = len(angry_neighbors_1_time_step)
|
||||
|
||||
|
||||
joyful_neighbors = self.get_neighboring_agents(state_id=2)
|
||||
for x in joyful_neighbors:
|
||||
if x.time_awareness[1] > (self.env.now-500):
|
||||
joyful_neighbors_1_time_step.append(x)
|
||||
num_neighbors_joyful = len(joyful_neighbors_1_time_step)
|
||||
|
||||
|
||||
sad_neighbors = self.get_neighboring_agents(state_id=3)
|
||||
for x in sad_neighbors:
|
||||
if x.time_awareness[2] > (self.env.now-500):
|
||||
sad_neighbors_1_time_step.append(x)
|
||||
num_neighbors_sad = len(sad_neighbors_1_time_step)
|
||||
|
||||
|
||||
disgusted_neighbors = self.get_neighboring_agents(state_id=4)
|
||||
for x in disgusted_neighbors:
|
||||
if x.time_awareness[3] > (self.env.now-500):
|
||||
disgusted_neighbors_1_time_step.append(x)
|
||||
num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
|
||||
|
||||
# #Outside effects. Asignamos un estado aleatorio
|
||||
# if random.random() < settings.outside_effects_prob:
|
||||
# if self.state['id'] == 0:
|
||||
# self.state['id'] = random.randint(1,4)
|
||||
# myList.append(self.id)
|
||||
# networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
|
||||
# self.time_awareness = self.env.now #Para saber cuando se han contagiado
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
# else:
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
|
||||
|
||||
# #Imitation effects-Joy
|
||||
|
||||
# if random.random() < (settings.joy_prob*(num_neighbors_joyful)/10):
|
||||
# myList.append(self.id)
|
||||
# self.state['id'] = 2
|
||||
# networkStatus[self.id][self.env.now]=2
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
|
||||
|
||||
# #Imitation effects-Sadness
|
||||
|
||||
# if random.random() < (settings.sadness_prob*(num_neighbors_sad)/10):
|
||||
# myList.append(self.id)
|
||||
# self.state['id'] = 3
|
||||
# networkStatus[self.id][self.env.now]=3
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
|
||||
|
||||
# #Imitation effects-Disgust
|
||||
|
||||
# if random.random() < (settings.disgust_prob*(num_neighbors_disgusted)/10):
|
||||
# myList.append(self.id)
|
||||
# self.state['id'] = 4
|
||||
# networkStatus[self.id][self.env.now]=4
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
|
||||
# #Imitation effects-Anger
|
||||
|
||||
# if random.random() < (settings.anger_prob*(num_neighbors_angry)/10):
|
||||
# myList.append(self.id)
|
||||
# self.state['id'] = 1
|
||||
# networkStatus[self.id][self.env.now]=1
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
|
||||
###########################################
|
||||
|
||||
|
||||
anger_prob= settings.anger_prob+(len(angry_neighbors_1_time_step)*settings.anger_prob)
|
||||
print("anger_prob " + str(anger_prob))
|
||||
joy_prob= settings.joy_prob+(len(joyful_neighbors_1_time_step)*settings.joy_prob)
|
||||
print("joy_prob " + str(joy_prob))
|
||||
sadness_prob = settings.sadness_prob+(len(sad_neighbors_1_time_step)*settings.sadness_prob)
|
||||
print("sadness_prob "+ str(sadness_prob))
|
||||
disgust_prob = settings.disgust_prob+(len(disgusted_neighbors_1_time_step)*settings.disgust_prob)
|
||||
print("disgust_prob " + str(disgust_prob))
|
||||
outside_effects_prob= settings.outside_effects_prob
|
||||
print("outside_effects_prob " + str(outside_effects_prob))
|
||||
|
||||
|
||||
num = random.random()
|
||||
|
||||
|
||||
if(num<outside_effects_prob):
|
||||
self.state['id'] = random.randint(1,4)
|
||||
myList.append(self.id)
|
||||
networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
yield self.env.timeout(settings.timeout)
|
||||
|
||||
|
||||
if(num<anger_prob):
|
||||
|
||||
myList.append(self.id)
|
||||
self.state['id'] = 1
|
||||
networkStatus[self.id][self.env.now]=1
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
elif (num<joy_prob+anger_prob and num>anger_prob):
|
||||
|
||||
myList.append(self.id)
|
||||
self.state['id'] = 2
|
||||
networkStatus[self.id][self.env.now]=2
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
|
||||
|
||||
myList.append(self.id)
|
||||
self.state['id'] = 3
|
||||
networkStatus[self.id][self.env.now]=3
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
elif (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_prob+anger_prob+joy_prob):
|
||||
|
||||
myList.append(self.id)
|
||||
self.state['id'] = 4
|
||||
networkStatus[self.id][self.env.now]=4
|
||||
self.time_awareness[self.state['id']-1] = self.env.now
|
||||
|
||||
yield self.env.timeout(settings.timeout)
|
||||
|
||||
|
||||
# anger_propagation = settings.anger_prob*num_neighbors_angry/10
|
||||
# joy_propagation = anger_propagation + (settings.joy_prob*num_neighbors_joyful/10)
|
||||
# sadness_propagation = joy_propagation + (settings.sadness_prob*num_neighbors_sad/10)
|
||||
# disgust_propagation = sadness_propagation + (settings.disgust_prob*num_neighbors_disgusted/10)
|
||||
# outside_effects_propagation = disgust_propagation + settings.outside_effects_prob
|
||||
|
||||
# if (num<anger_propagation):
|
||||
# if(self.state['id'] !=0):
|
||||
# myList.append(self.id)
|
||||
# self.state['id'] = 1
|
||||
# networkStatus[self.id][self.env.now]=1
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
# if (num<joy_propagation):
|
||||
# if(self.state['id'] !=0):
|
||||
# myList.append(self.id)
|
||||
# self.state['id'] = 2
|
||||
# networkStatus[self.id][self.env.now]=2
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
# if(num<sadness_propagation):
|
||||
# if(self.state['id'] !=0):
|
||||
# myList.append(self.id)
|
||||
# self.state['id'] = 3
|
||||
# networkStatus[self.id][self.env.now]=3
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
# # if(num<disgust_propagation):
|
||||
# # if(self.state['id'] !=0):
|
||||
# # myList.append(self.id)
|
||||
# # self.state['id'] = 4
|
||||
# # networkStatus[self.id][self.env.now]=4
|
||||
# # yield self.env.timeout(settings.timeout)
|
||||
# if(num <outside_effects_propagation):
|
||||
# if self.state['id'] == 0:
|
||||
# self.state['id'] = random.randint(1,4)
|
||||
# myList.append(self.id)
|
||||
# networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
|
||||
# self.time_awareness = self.env.now #Para saber cuando se han contagiado
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
# else:
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
# else:
|
||||
# yield self.env.timeout(settings.timeout)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
class BassModel(BaseNetworkAgent):
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
self.innovation_prob = settings.innovation_prob
|
||||
self.imitation_prob = settings.imitation_prob
|
||||
networkStatus[self.id][self.env.now]=0
|
||||
|
||||
def run(self):
|
||||
while True:
|
||||
|
||||
|
||||
#Outside effects
|
||||
if random.random() < settings.innovation_prob:
|
||||
if self.state['id'] == 0:
|
||||
self.state['id'] = 1
|
||||
myList.append(self.id)
|
||||
networkStatus[self.id][self.env.now]=1
|
||||
yield self.env.timeout(settings.timeout)
|
||||
else:
|
||||
yield self.env.timeout(settings.timeout)
|
||||
|
||||
#Imitation effects
|
||||
if self.state['id'] == 0:
|
||||
aware_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
num_neighbors_aware = len(aware_neighbors)
|
||||
if random.random() < (settings.imitation_prob*num_neighbors_aware):
|
||||
myList.append(self.id)
|
||||
self.state['id'] = 1
|
||||
networkStatus[self.id][self.env.now]=1
|
||||
yield self.env.timeout(settings.timeout)
|
||||
else:
|
||||
yield self.env.timeout(settings.timeout)
|
||||
|
||||
class IndependentCascadeModel(BaseNetworkAgent):
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
self.innovation_prob = settings.innovation_prob
|
||||
self.imitation_prob = settings.imitation_prob
|
||||
self.time_awareness = 0
|
||||
networkStatus[self.id][self.env.now]=0
|
||||
|
||||
def run(self):
|
||||
while True:
|
||||
aware_neighbors_1_time_step=[]
|
||||
#Outside effects
|
||||
if random.random() < settings.innovation_prob:
|
||||
if self.state['id'] == 0:
|
||||
self.state['id'] = 1
|
||||
myList.append(self.id)
|
||||
networkStatus[self.id][self.env.now]=1
|
||||
self.time_awareness = self.env.now #Para saber cuando se han contagiado
|
||||
yield self.env.timeout(settings.timeout)
|
||||
else:
|
||||
yield self.env.timeout(settings.timeout)
|
||||
|
||||
#Imitation effects
|
||||
if self.state['id'] == 0:
|
||||
aware_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for x in aware_neighbors:
|
||||
if x.time_awareness == (self.env.now-1):
|
||||
aware_neighbors_1_time_step.append(x)
|
||||
num_neighbors_aware = len(aware_neighbors_1_time_step)
|
||||
if random.random() < (settings.imitation_prob*num_neighbors_aware):
|
||||
myList.append(self.id)
|
||||
self.state['id'] = 1
|
||||
networkStatus[self.id][self.env.now]=1
|
||||
yield self.env.timeout(settings.timeout)
|
||||
else:
|
||||
yield self.env.timeout(settings.timeout)
|
||||
|
||||
|
||||
class ZombieOutbreak(BaseNetworkAgent):
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
self.bite_prob = settings.bite_prob
|
||||
networkStatus[self.id][self.env.now]=0
|
||||
|
||||
|
||||
def run(self):
|
||||
while True:
|
||||
if random.random() < settings.heal_prob:
|
||||
if self.state['id'] == 1:
|
||||
self.zombify()
|
||||
yield self.env.timeout(settings.timeout)
|
||||
else:
|
||||
yield self.env.timeout(settings.timeout)
|
||||
else:
|
||||
if self.state['id'] == 1:
|
||||
print("Soy el zombie " + str(self.id) + " y me voy a curar porque el num aleatorio ha sido " + str(num))
|
||||
networkStatus[self.id][self.env.now]=0
|
||||
if self.id in myList:
|
||||
myList.remove(self.id)
|
||||
self.state['id'] = 0
|
||||
yield self.env.timeout(settings.timeout)
|
||||
else:
|
||||
yield self.env.timeout(settings.timeout)
|
||||
|
||||
|
||||
def zombify(self):
|
||||
normal_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in normal_neighbors:
|
||||
if random.random() < self.bite_prob:
|
||||
print("Soy el zombie " + str(self.id) + " y voy a contagiar a " + str(neighbor.id))
|
||||
neighbor.state['id'] = 1 # zombie
|
||||
myList.append(neighbor.id)
|
||||
networkStatus[self.id][self.env.now]=1
|
||||
networkStatus[neighbor.id][self.env.now]=1
|
||||
print(self.env.now, "Soy el zombie: "+ str(self.id), "Mi vecino es: "+ str(neighbor.id), sep='\t')
|
||||
break
|
||||
|
||||
|
||||
# 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
|
||||
|
||||
# Seed a zombie
|
||||
#init_states[5] = {'id': 1}
|
||||
#init_states[3] = {'id': 1}
|
||||
|
||||
sim = NetworkSimulation(topology=G, states=init_states, agent_type=SentimentCorrelationModel,
|
||||
max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)
|
||||
|
||||
|
||||
sim.run_simulation()
|
||||
|
||||
myList = sorted(myList, key=int)
|
||||
#print("Los zombies son: " + str(myList))
|
||||
|
||||
trial = BaseLoggingAgent.open_trial_state_history(dir_path='sim_01', trial_id=0)
|
||||
zombie_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) for t,g in trial.items()]
|
||||
|
||||
#for x in range(len(myList)):
|
||||
# G.node[myList[x]]['viz'] = {'color': {'r': 255, 'g': 0, 'b': 0, 'a': 0}}
|
||||
|
||||
#G.node[1]['viz'] = {'color': {'r': 255, 'g': 0, 'b': 0, 'a': 0}}
|
||||
|
||||
#lista = nx.nodes(G)
|
||||
#print('Nodos: ' + str(lista))
|
||||
for x in range(0, settings.number_of_nodes):
|
||||
networkStatusAux=[]
|
||||
for tiempo in networkStatus[x]:
|
||||
if tiempo != 'id':
|
||||
networkStatusAux.append((networkStatus[x][tiempo],tiempo,None))
|
||||
G.add_node(x, zombie= networkStatusAux)
|
||||
#print(networkStatus)
|
||||
|
||||
|
||||
nx.write_gexf(G,"test.gexf", version="1.2draft")
|
||||
plt.plot(zombie_census)
|
||||
plt.draw() # pyplot draw()
|
||||
plt.savefig("zombie.png")
|
||||
#print(networkStatus)
|
||||
#nx.draw(G)
|
||||
#plt.show()
|
||||
#plt.savefig("path.png")
|
||||
@ -1,42 +0,0 @@
|
||||
import importlib
|
||||
import sys
|
||||
import os
|
||||
|
||||
__version__ = "0.9.2"
|
||||
|
||||
try:
|
||||
basestring
|
||||
except NameError:
|
||||
basestring = str
|
||||
|
||||
from . import agents
|
||||
from . import simulation
|
||||
from . import environment
|
||||
from . import utils
|
||||
from . import settings
|
||||
|
||||
|
||||
def main():
|
||||
import argparse
|
||||
from . import simulation
|
||||
|
||||
parser = argparse.ArgumentParser(description='Run a SOIL simulation')
|
||||
parser.add_argument('file', type=str,
|
||||
nargs="?",
|
||||
default='simulation.yml',
|
||||
help='python module containing the simulation configuration.')
|
||||
parser.add_argument('--module', '-m', type=str,
|
||||
help='file containing the code of any custom agents.')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.module:
|
||||
sys.path.append(os.getcwd())
|
||||
importlib.import_module(args.module)
|
||||
|
||||
print('Loading config file: {}'.format(args.file))
|
||||
simulation.run_from_config(args.file)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -1,123 +0,0 @@
|
||||
import nxsim
|
||||
from collections import OrderedDict
|
||||
from copy import deepcopy
|
||||
import json
|
||||
|
||||
from functools import wraps
|
||||
|
||||
|
||||
class BaseAgent(nxsim.BaseAgent):
|
||||
"""
|
||||
A special simpy BaseAgent that keeps track of its state history.
|
||||
"""
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
self._history = OrderedDict()
|
||||
self._neighbors = None
|
||||
super().__init__(*args, **kwargs)
|
||||
self._history[None] = deepcopy(self.state)
|
||||
|
||||
@property
|
||||
def now(self):
|
||||
try:
|
||||
return self.env.now
|
||||
except AttributeError:
|
||||
# No environment
|
||||
return None
|
||||
|
||||
def run(self):
|
||||
while True:
|
||||
res = self.step()
|
||||
self._history[self.env.now] = deepcopy(self.state)
|
||||
yield res or self.env.timeout(self.env.interval)
|
||||
|
||||
def step(self):
|
||||
pass
|
||||
|
||||
def to_json(self):
|
||||
return json.dumps(self._history)
|
||||
|
||||
class NetworkAgent(BaseAgent, nxsim.BaseNetworkAgent):
|
||||
|
||||
def count_agents(self, state_id=None, limit_neighbors=False):
|
||||
if limit_neighbors:
|
||||
agents = self.global_topology.neighbors(self.id)
|
||||
else:
|
||||
agents = self.global_topology.nodes()
|
||||
count = 0
|
||||
for agent in agents:
|
||||
if state_id and state_id != self.global_topology.node[agent]['agent'].state['id']:
|
||||
continue
|
||||
count += 1
|
||||
return count
|
||||
|
||||
def count_neighboring_agents(self, state_id=None):
|
||||
return self.count_agents(state_id, limit_neighbors=True)
|
||||
|
||||
|
||||
def state(func):
|
||||
|
||||
@wraps(func)
|
||||
def func_wrapper(self):
|
||||
when = None
|
||||
next_state = func(self)
|
||||
try:
|
||||
next_state, when = next_state
|
||||
except TypeError:
|
||||
pass
|
||||
if next_state:
|
||||
try:
|
||||
self.state['id'] = next_state.id
|
||||
except AttributeError:
|
||||
raise NotImplemented('State id %s is not valid.' % next_state)
|
||||
return when
|
||||
|
||||
func_wrapper.id = func.__name__
|
||||
func_wrapper.is_default = False
|
||||
return func_wrapper
|
||||
|
||||
|
||||
def default_state(func):
|
||||
func.is_default = True
|
||||
return func
|
||||
|
||||
|
||||
class MetaFSM(type):
|
||||
def __init__(cls, name, bases, nmspc):
|
||||
super(MetaFSM, cls).__init__(name, bases, nmspc)
|
||||
states = {}
|
||||
# Re-use states from inherited classes
|
||||
default_state = None
|
||||
for i in bases:
|
||||
if isinstance(i, MetaFSM):
|
||||
for state_id, state in i.states.items():
|
||||
if state.is_default:
|
||||
default_state = state
|
||||
states[state_id] = state
|
||||
|
||||
# Add new states
|
||||
for name, func in nmspc.items():
|
||||
if hasattr(func, 'id'):
|
||||
if func.is_default:
|
||||
default_state = func
|
||||
states[func.id] = func
|
||||
cls.default_state = default_state
|
||||
cls.states = states
|
||||
|
||||
|
||||
class FSM(BaseAgent, metaclass=MetaFSM):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super(FSM, self).__init__(*args, **kwargs)
|
||||
if 'id' not in self.state:
|
||||
self.state['id'] = self.default_state.id
|
||||
|
||||
def step(self):
|
||||
if 'id' in self.state:
|
||||
next_state = self.state['id']
|
||||
elif self.default_state:
|
||||
next_state = self.default_state.id
|
||||
else:
|
||||
raise Exception('{} has no valid state id or default state'.format(self))
|
||||
if next_state not in self.states:
|
||||
raise Exception('{} is not a valid id for {}'.format(next_state, self))
|
||||
self.states[next_state](self)
|
||||
@ -1,40 +0,0 @@
|
||||
import random
|
||||
from . import NetworkAgent
|
||||
|
||||
|
||||
class BassModel(NetworkAgent):
|
||||
"""
|
||||
Settings:
|
||||
innovation_prob
|
||||
imitation_prob
|
||||
"""
|
||||
|
||||
def __init__(self, environment, agent_id, state):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
env_params = environment.environment_params
|
||||
self.state['sentimentCorrelation'] = 0
|
||||
|
||||
def step(self):
|
||||
self.behaviour()
|
||||
|
||||
def behaviour(self):
|
||||
# Outside effects
|
||||
if random.random() < self.state_params['innovation_prob']:
|
||||
if self.state['id'] == 0:
|
||||
self.state['id'] = 1
|
||||
self.state['sentimentCorrelation'] = 1
|
||||
else:
|
||||
pass
|
||||
|
||||
return
|
||||
|
||||
# Imitation effects
|
||||
if self.state['id'] == 0:
|
||||
aware_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
num_neighbors_aware = len(aware_neighbors)
|
||||
if random.random() < (self.state_params['imitation_prob']*num_neighbors_aware):
|
||||
self.state['id'] = 1
|
||||
self.state['sentimentCorrelation'] = 1
|
||||
|
||||
else:
|
||||
pass
|
||||
@ -1,102 +0,0 @@
|
||||
import random
|
||||
from . import NetworkAgent
|
||||
|
||||
|
||||
class BigMarketModel(NetworkAgent):
|
||||
"""
|
||||
Settings:
|
||||
Names:
|
||||
enterprises [Array]
|
||||
|
||||
tweet_probability_enterprises [Array]
|
||||
Users:
|
||||
tweet_probability_users
|
||||
|
||||
tweet_relevant_probability
|
||||
|
||||
tweet_probability_about [Array]
|
||||
|
||||
sentiment_about [Array]
|
||||
"""
|
||||
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
self.enterprises = environment.environment_params['enterprises']
|
||||
self.type = ""
|
||||
self.number_of_enterprises = len(environment.environment_params['enterprises'])
|
||||
|
||||
if self.id < self.number_of_enterprises: # Enterprises
|
||||
self.state['id'] = self.id
|
||||
self.type = "Enterprise"
|
||||
self.tweet_probability = environment.environment_params['tweet_probability_enterprises'][self.id]
|
||||
else: # normal users
|
||||
self.state['id'] = self.number_of_enterprises
|
||||
self.type = "User"
|
||||
self.tweet_probability = environment.environment_params['tweet_probability_users']
|
||||
self.tweet_relevant_probability = environment.environment_params['tweet_relevant_probability']
|
||||
self.tweet_probability_about = environment.environment_params['tweet_probability_about'] # List
|
||||
self.sentiment_about = environment.environment_params['sentiment_about'] # List
|
||||
|
||||
def step(self):
|
||||
|
||||
if self.id < self.number_of_enterprises: # Enterprise
|
||||
self.enterpriseBehaviour()
|
||||
else: # Usuario
|
||||
self.userBehaviour()
|
||||
for i in range(self.number_of_enterprises): # So that it never is set to 0 if there are not changes (logs)
|
||||
self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
|
||||
|
||||
def enterpriseBehaviour(self):
|
||||
|
||||
if random.random() < self.tweet_probability: # Tweets
|
||||
aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) # Nodes neighbour users
|
||||
for x in aware_neighbors:
|
||||
if random.uniform(0,10) < 5:
|
||||
x.sentiment_about[self.id] += 0.1 # Increments for enterprise
|
||||
else:
|
||||
x.sentiment_about[self.id] -= 0.1 # Decrements for enterprise
|
||||
|
||||
# Establecemos limites
|
||||
if x.sentiment_about[self.id] > 1:
|
||||
x.sentiment_about[self.id] = 1
|
||||
if x.sentiment_about[self.id]< -1:
|
||||
x.sentiment_about[self.id] = -1
|
||||
|
||||
x.attrs['sentiment_enterprise_%s'% self.enterprises[self.id]] = x.sentiment_about[self.id]
|
||||
|
||||
def userBehaviour(self):
|
||||
|
||||
if random.random() < self.tweet_probability: # Tweets
|
||||
if random.random() < self.tweet_relevant_probability: # Tweets something relevant
|
||||
# Tweet probability per enterprise
|
||||
for i in range(self.number_of_enterprises):
|
||||
random_num = random.random()
|
||||
if random_num < self.tweet_probability_about[i]:
|
||||
# The condition is fulfilled, sentiments are evaluated towards that enterprise
|
||||
if self.sentiment_about[i] < 0:
|
||||
# NEGATIVO
|
||||
self.userTweets("negative",i)
|
||||
elif self.sentiment_about[i] == 0:
|
||||
# NEUTRO
|
||||
pass
|
||||
else:
|
||||
# POSITIVO
|
||||
self.userTweets("positive",i)
|
||||
|
||||
def userTweets(self,sentiment,enterprise):
|
||||
aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) # Nodes neighbours users
|
||||
for x in aware_neighbors:
|
||||
if sentiment == "positive":
|
||||
x.sentiment_about[enterprise] +=0.003
|
||||
elif sentiment == "negative":
|
||||
x.sentiment_about[enterprise] -=0.003
|
||||
else:
|
||||
pass
|
||||
|
||||
# Establecemos limites
|
||||
if x.sentiment_about[enterprise] > 1:
|
||||
x.sentiment_about[enterprise] = 1
|
||||
if x.sentiment_about[enterprise] < -1:
|
||||
x.sentiment_about[enterprise] = -1
|
||||
|
||||
x.attrs['sentiment_enterprise_%s'% self.enterprises[enterprise]] = x.sentiment_about[enterprise]
|
||||
@ -1,31 +0,0 @@
|
||||
from . import NetworkAgent
|
||||
|
||||
|
||||
class CounterModel(NetworkAgent):
|
||||
"""
|
||||
Dummy behaviour. It counts the number of nodes in the network and neighbors
|
||||
in each step and adds it to its state.
|
||||
"""
|
||||
|
||||
def step(self):
|
||||
# Outside effects
|
||||
total = len(self.get_all_agents())
|
||||
neighbors = len(self.get_neighboring_agents())
|
||||
self.state['times'] = self.state.get('times', 0) + 1
|
||||
self.state['neighbors'] = neighbors
|
||||
self.state['total'] = total
|
||||
|
||||
|
||||
class AggregatedCounter(NetworkAgent):
|
||||
"""
|
||||
Dummy behaviour. It counts the number of nodes in the network and neighbors
|
||||
in each step and adds it to its state.
|
||||
"""
|
||||
|
||||
def step(self):
|
||||
# Outside effects
|
||||
total = len(self.get_all_agents())
|
||||
neighbors = len(self.get_neighboring_agents())
|
||||
self.state['times'] = self.state.get('times', 0) + 1
|
||||
self.state['neighbors'] = self.state.get('neighbors', 0) + neighbors
|
||||
self.state['total'] = self.state.get('total', 0) + total
|
||||
@ -1,18 +0,0 @@
|
||||
from . import BaseAgent
|
||||
|
||||
import os.path
|
||||
import matplotlib
|
||||
import matplotlib.pyplot as plt
|
||||
import networkx as nx
|
||||
|
||||
|
||||
class DrawingAgent(BaseAgent):
|
||||
"""
|
||||
Agent that draws the state of the network.
|
||||
"""
|
||||
|
||||
def step(self):
|
||||
# Outside effects
|
||||
f = plt.figure()
|
||||
nx.draw(self.env.G, node_size=10, width=0.2, pos=nx.spring_layout(self.env.G, scale=100), ax=f.add_subplot(111))
|
||||
f.savefig(os.path.join(self.env.sim().dir_path, "graph-"+str(self.env.now)+".png"))
|
||||
@ -1,49 +0,0 @@
|
||||
import random
|
||||
from . import BaseAgent
|
||||
|
||||
|
||||
class IndependentCascadeModel(BaseAgent):
|
||||
"""
|
||||
Settings:
|
||||
innovation_prob
|
||||
|
||||
imitation_prob
|
||||
"""
|
||||
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
self.innovation_prob = environment.environment_params['innovation_prob']
|
||||
self.imitation_prob = environment.environment_params['imitation_prob']
|
||||
self.state['time_awareness'] = 0
|
||||
self.state['sentimentCorrelation'] = 0
|
||||
|
||||
def step(self):
|
||||
self.behaviour()
|
||||
|
||||
def behaviour(self):
|
||||
aware_neighbors_1_time_step = []
|
||||
# Outside effects
|
||||
if random.random() < self.innovation_prob:
|
||||
if self.state['id'] == 0:
|
||||
self.state['id'] = 1
|
||||
self.state['sentimentCorrelation'] = 1
|
||||
self.state['time_awareness'] = self.env.now # To know when they have been infected
|
||||
else:
|
||||
pass
|
||||
|
||||
return
|
||||
|
||||
# Imitation effects
|
||||
if self.state['id'] == 0:
|
||||
aware_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for x in aware_neighbors:
|
||||
if x.state['time_awareness'] == (self.env.now-1):
|
||||
aware_neighbors_1_time_step.append(x)
|
||||
num_neighbors_aware = len(aware_neighbors_1_time_step)
|
||||
if random.random() < (self.imitation_prob*num_neighbors_aware):
|
||||
self.state['id'] = 1
|
||||
self.state['sentimentCorrelation'] = 1
|
||||
else:
|
||||
pass
|
||||
|
||||
return
|
||||
@ -1,242 +0,0 @@
|
||||
import random
|
||||
import numpy as np
|
||||
from . import NetworkAgent
|
||||
|
||||
|
||||
class SpreadModelM2(NetworkAgent):
|
||||
"""
|
||||
Settings:
|
||||
prob_neutral_making_denier
|
||||
|
||||
prob_infect
|
||||
|
||||
prob_cured_healing_infected
|
||||
|
||||
prob_cured_vaccinate_neutral
|
||||
|
||||
prob_vaccinated_healing_infected
|
||||
|
||||
prob_vaccinated_vaccinate_neutral
|
||||
|
||||
prob_generate_anti_rumor
|
||||
"""
|
||||
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
|
||||
self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
|
||||
environment.environment_params['standard_variance'])
|
||||
|
||||
self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
|
||||
environment.environment_params['standard_variance'])
|
||||
|
||||
self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],
|
||||
environment.environment_params['standard_variance'])
|
||||
self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],
|
||||
environment.environment_params['standard_variance'])
|
||||
|
||||
self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
|
||||
environment.environment_params['standard_variance'])
|
||||
self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
|
||||
environment.environment_params['standard_variance'])
|
||||
self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
|
||||
environment.environment_params['standard_variance'])
|
||||
|
||||
def step(self):
|
||||
|
||||
if self.state['id'] == 0: # Neutral
|
||||
self.neutral_behaviour()
|
||||
elif self.state['id'] == 1: # Infected
|
||||
self.infected_behaviour()
|
||||
elif self.state['id'] == 2: # Cured
|
||||
self.cured_behaviour()
|
||||
elif self.state['id'] == 3: # Vaccinated
|
||||
self.vaccinated_behaviour()
|
||||
|
||||
def neutral_behaviour(self):
|
||||
|
||||
# Infected
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
if len(infected_neighbors) > 0:
|
||||
if random.random() < self.prob_neutral_making_denier:
|
||||
self.state['id'] = 3 # Vaccinated making denier
|
||||
|
||||
def infected_behaviour(self):
|
||||
|
||||
# Neutral
|
||||
neutral_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors:
|
||||
if random.random() < self.prob_infect:
|
||||
neighbor.state['id'] = 1 # Infected
|
||||
|
||||
def cured_behaviour(self):
|
||||
|
||||
# Vaccinate
|
||||
neutral_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors:
|
||||
if random.random() < self.prob_cured_vaccinate_neutral:
|
||||
neighbor.state['id'] = 3 # Vaccinated
|
||||
|
||||
# Cure
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors:
|
||||
if random.random() < self.prob_cured_healing_infected:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
|
||||
def vaccinated_behaviour(self):
|
||||
|
||||
# Cure
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors:
|
||||
if random.random() < self.prob_cured_healing_infected:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
|
||||
# Vaccinate
|
||||
neutral_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors:
|
||||
if random.random() < self.prob_cured_vaccinate_neutral:
|
||||
neighbor.state['id'] = 3 # Vaccinated
|
||||
|
||||
# Generate anti-rumor
|
||||
infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors_2:
|
||||
if random.random() < self.prob_generate_anti_rumor:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
|
||||
|
||||
class ControlModelM2(NetworkAgent):
|
||||
"""
|
||||
Settings:
|
||||
prob_neutral_making_denier
|
||||
|
||||
prob_infect
|
||||
|
||||
prob_cured_healing_infected
|
||||
|
||||
prob_cured_vaccinate_neutral
|
||||
|
||||
prob_vaccinated_healing_infected
|
||||
|
||||
prob_vaccinated_vaccinate_neutral
|
||||
|
||||
prob_generate_anti_rumor
|
||||
"""
|
||||
|
||||
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
|
||||
self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
|
||||
environment.environment_params['standard_variance'])
|
||||
|
||||
self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
|
||||
environment.environment_params['standard_variance'])
|
||||
|
||||
self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],
|
||||
environment.environment_params['standard_variance'])
|
||||
self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],
|
||||
environment.environment_params['standard_variance'])
|
||||
|
||||
self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
|
||||
environment.environment_params['standard_variance'])
|
||||
self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
|
||||
environment.environment_params['standard_variance'])
|
||||
self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
|
||||
environment.environment_params['standard_variance'])
|
||||
|
||||
def step(self):
|
||||
|
||||
if self.state['id'] == 0: # Neutral
|
||||
self.neutral_behaviour()
|
||||
elif self.state['id'] == 1: # Infected
|
||||
self.infected_behaviour()
|
||||
elif self.state['id'] == 2: # Cured
|
||||
self.cured_behaviour()
|
||||
elif self.state['id'] == 3: # Vaccinated
|
||||
self.vaccinated_behaviour()
|
||||
elif self.state['id'] == 4: # Beacon-off
|
||||
self.beacon_off_behaviour()
|
||||
elif self.state['id'] == 5: # Beacon-on
|
||||
self.beacon_on_behaviour()
|
||||
|
||||
def neutral_behaviour(self):
|
||||
self.state['visible'] = False
|
||||
|
||||
# Infected
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
if len(infected_neighbors) > 0:
|
||||
if random.random() < self.prob_neutral_making_denier:
|
||||
self.state['id'] = 3 # Vaccinated making denier
|
||||
|
||||
def infected_behaviour(self):
|
||||
|
||||
# Neutral
|
||||
neutral_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors:
|
||||
if random.random() < self.prob_infect:
|
||||
neighbor.state['id'] = 1 # Infected
|
||||
self.state['visible'] = False
|
||||
|
||||
def cured_behaviour(self):
|
||||
|
||||
self.state['visible'] = True
|
||||
# Vaccinate
|
||||
neutral_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors:
|
||||
if random.random() < self.prob_cured_vaccinate_neutral:
|
||||
neighbor.state['id'] = 3 # Vaccinated
|
||||
|
||||
# Cure
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors:
|
||||
if random.random() < self.prob_cured_healing_infected:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
|
||||
def vaccinated_behaviour(self):
|
||||
self.state['visible'] = True
|
||||
|
||||
# Cure
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors:
|
||||
if random.random() < self.prob_cured_healing_infected:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
|
||||
# Vaccinate
|
||||
neutral_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors:
|
||||
if random.random() < self.prob_cured_vaccinate_neutral:
|
||||
neighbor.state['id'] = 3 # Vaccinated
|
||||
|
||||
# Generate anti-rumor
|
||||
infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors_2:
|
||||
if random.random() < self.prob_generate_anti_rumor:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
|
||||
def beacon_off_behaviour(self):
|
||||
self.state['visible'] = False
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
if len(infected_neighbors) > 0:
|
||||
self.state['id'] == 5 # Beacon on
|
||||
|
||||
def beacon_on_behaviour(self):
|
||||
self.state['visible'] = False
|
||||
# Cure (M2 feature added)
|
||||
infected_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors:
|
||||
if random.random() < self.prob_generate_anti_rumor:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
neutral_neighbors_infected = neighbor.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors_infected:
|
||||
if random.random() < self.prob_generate_anti_rumor:
|
||||
neighbor.state['id'] = 3 # Vaccinated
|
||||
infected_neighbors_infected = neighbor.get_neighboring_agents(state_id=1)
|
||||
for neighbor in infected_neighbors_infected:
|
||||
if random.random() < self.prob_generate_anti_rumor:
|
||||
neighbor.state['id'] = 2 # Cured
|
||||
|
||||
# Vaccinate
|
||||
neutral_neighbors = self.get_neighboring_agents(state_id=0)
|
||||
for neighbor in neutral_neighbors:
|
||||
if random.random() < self.prob_cured_vaccinate_neutral:
|
||||
neighbor.state['id'] = 3 # Vaccinated
|
||||
@ -1,93 +0,0 @@
|
||||
import random
|
||||
import numpy as np
|
||||
from . import FSM, state
|
||||
|
||||
|
||||
class SISaModel(FSM):
|
||||
"""
|
||||
Settings:
|
||||
neutral_discontent_spon_prob
|
||||
|
||||
neutral_discontent_infected_prob
|
||||
|
||||
neutral_content_spong_prob
|
||||
|
||||
neutral_content_infected_prob
|
||||
|
||||
discontent_neutral
|
||||
|
||||
discontent_content
|
||||
|
||||
variance_d_c
|
||||
|
||||
content_discontent
|
||||
|
||||
variance_c_d
|
||||
|
||||
content_neutral
|
||||
|
||||
standard_variance
|
||||
"""
|
||||
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
|
||||
self.neutral_discontent_spon_prob = np.random.normal(environment.environment_params['neutral_discontent_spon_prob'],
|
||||
environment.environment_params['standard_variance'])
|
||||
self.neutral_discontent_infected_prob = np.random.normal(environment.environment_params['neutral_discontent_infected_prob'],
|
||||
environment.environment_params['standard_variance'])
|
||||
self.neutral_content_spon_prob = np.random.normal(environment.environment_params['neutral_content_spon_prob'],
|
||||
environment.environment_params['standard_variance'])
|
||||
self.neutral_content_infected_prob = np.random.normal(environment.environment_params['neutral_content_infected_prob'],
|
||||
environment.environment_params['standard_variance'])
|
||||
|
||||
self.discontent_neutral = np.random.normal(environment.environment_params['discontent_neutral'],
|
||||
environment.environment_params['standard_variance'])
|
||||
self.discontent_content = np.random.normal(environment.environment_params['discontent_content'],
|
||||
environment.environment_params['variance_d_c'])
|
||||
|
||||
self.content_discontent = np.random.normal(environment.environment_params['content_discontent'],
|
||||
environment.environment_params['variance_c_d'])
|
||||
self.content_neutral = np.random.normal(environment.environment_params['content_neutral'],
|
||||
environment.environment_params['standard_variance'])
|
||||
|
||||
@state
|
||||
def neutral(self):
|
||||
# Spontaneous effects
|
||||
if random.random() < self.neutral_discontent_spon_prob:
|
||||
return self.discontent
|
||||
if random.random() < self.neutral_content_spon_prob:
|
||||
return self.content
|
||||
|
||||
# Infected
|
||||
discontent_neighbors = self.count_neighboring_agents(state_id=self.discontent)
|
||||
if random.random() < discontent_neighbors * self.neutral_discontent_infected_prob:
|
||||
return self.discontent
|
||||
content_neighbors = self.count_neighboring_agents(state_id=self.content.id)
|
||||
if random.random() < content_neighbors * self.neutral_content_infected_prob:
|
||||
return self.content
|
||||
return self.neutral
|
||||
|
||||
@state
|
||||
def discontent(self):
|
||||
# Healing
|
||||
if random.random() < self.discontent_neutral:
|
||||
return self.neutral
|
||||
|
||||
# Superinfected
|
||||
content_neighbors = self.count_neighboring_agents(state_id=self.content.id)
|
||||
if random.random() < content_neighbors * self.discontent_content:
|
||||
return self.content
|
||||
return self.discontent
|
||||
|
||||
@state
|
||||
def content(self):
|
||||
# Healing
|
||||
if random.random() < self.content_neutral:
|
||||
return self.neutral
|
||||
|
||||
# Superinfected
|
||||
discontent_neighbors = self.count_neighboring_agents(state_id=self.discontent.id)
|
||||
if random.random() < discontent_neighbors * self.content_discontent:
|
||||
self.discontent
|
||||
return self.content
|
||||
@ -1,102 +0,0 @@
|
||||
import random
|
||||
from . import NetworkAgent
|
||||
|
||||
|
||||
class SentimentCorrelationModel(NetworkAgent):
|
||||
"""
|
||||
Settings:
|
||||
outside_effects_prob
|
||||
|
||||
anger_prob
|
||||
|
||||
joy_prob
|
||||
|
||||
sadness_prob
|
||||
|
||||
disgust_prob
|
||||
"""
|
||||
|
||||
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.state['time_awareness'] = []
|
||||
for i in range(4): # In this model we have 4 sentiments
|
||||
self.state['time_awareness'].append(0) # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
|
||||
self.state['sentimentCorrelation'] = 0
|
||||
|
||||
def step(self):
|
||||
self.behaviour()
|
||||
|
||||
def behaviour(self):
|
||||
|
||||
angry_neighbors_1_time_step = []
|
||||
joyful_neighbors_1_time_step = []
|
||||
sad_neighbors_1_time_step = []
|
||||
disgusted_neighbors_1_time_step = []
|
||||
|
||||
angry_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
for x in angry_neighbors:
|
||||
if x.state['time_awareness'][0] > (self.env.now-500):
|
||||
angry_neighbors_1_time_step.append(x)
|
||||
num_neighbors_angry = len(angry_neighbors_1_time_step)
|
||||
|
||||
joyful_neighbors = self.get_neighboring_agents(state_id=2)
|
||||
for x in joyful_neighbors:
|
||||
if x.state['time_awareness'][1] > (self.env.now-500):
|
||||
joyful_neighbors_1_time_step.append(x)
|
||||
num_neighbors_joyful = len(joyful_neighbors_1_time_step)
|
||||
|
||||
sad_neighbors = self.get_neighboring_agents(state_id=3)
|
||||
for x in sad_neighbors:
|
||||
if x.state['time_awareness'][2] > (self.env.now-500):
|
||||
sad_neighbors_1_time_step.append(x)
|
||||
num_neighbors_sad = len(sad_neighbors_1_time_step)
|
||||
|
||||
disgusted_neighbors = self.get_neighboring_agents(state_id=4)
|
||||
for x in disgusted_neighbors:
|
||||
if x.state['time_awareness'][3] > (self.env.now-500):
|
||||
disgusted_neighbors_1_time_step.append(x)
|
||||
num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
|
||||
|
||||
anger_prob = self.anger_prob+(len(angry_neighbors_1_time_step)*self.anger_prob)
|
||||
joy_prob = self.joy_prob+(len(joyful_neighbors_1_time_step)*self.joy_prob)
|
||||
sadness_prob = self.sadness_prob+(len(sad_neighbors_1_time_step)*self.sadness_prob)
|
||||
disgust_prob = self.disgust_prob+(len(disgusted_neighbors_1_time_step)*self.disgust_prob)
|
||||
outside_effects_prob = self.outside_effects_prob
|
||||
|
||||
num = random.random()
|
||||
|
||||
if num<outside_effects_prob:
|
||||
self.state['id'] = random.randint(1, 4)
|
||||
|
||||
self.state['sentimentCorrelation'] = self.state['id'] # It is stored when it has been infected for the dynamic network
|
||||
self.state['time_awareness'][self.state['id']-1] = self.env.now
|
||||
self.state['sentiment'] = self.state['id']
|
||||
|
||||
|
||||
if(num<anger_prob):
|
||||
|
||||
self.state['id'] = 1
|
||||
self.state['sentimentCorrelation'] = 1
|
||||
self.state['time_awareness'][self.state['id']-1] = self.env.now
|
||||
elif (num<joy_prob+anger_prob and num>anger_prob):
|
||||
|
||||
self.state['id'] = 2
|
||||
self.state['sentimentCorrelation'] = 2
|
||||
self.state['time_awareness'][self.state['id']-1] = self.env.now
|
||||
elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
|
||||
|
||||
self.state['id'] = 3
|
||||
self.state['sentimentCorrelation'] = 3
|
||||
self.state['time_awareness'][self.state['id']-1] = self.env.now
|
||||
elif (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_prob+anger_prob+joy_prob):
|
||||
|
||||
self.state['id'] = 4
|
||||
self.state['sentimentCorrelation'] = 4
|
||||
self.state['time_awareness'][self.state['id']-1] = self.env.now
|
||||
|
||||
self.state['sentiment'] = self.state['id']
|
||||
@ -1,166 +0,0 @@
|
||||
# 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.
|
||||
|
||||
|
||||
import nxsim
|
||||
from collections import OrderedDict
|
||||
from copy import deepcopy
|
||||
import json
|
||||
|
||||
from functools import wraps
|
||||
|
||||
|
||||
agent_types = {}
|
||||
|
||||
|
||||
class MetaAgent(type):
|
||||
def __init__(cls, name, bases, nmspc):
|
||||
super(MetaAgent, cls).__init__(name, bases, nmspc)
|
||||
agent_types[name] = cls
|
||||
|
||||
|
||||
class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
|
||||
"""
|
||||
A special simpy BaseAgent that keeps track of its state history.
|
||||
"""
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
self._history = OrderedDict()
|
||||
self._neighbors = None
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
def __getitem__(self, key):
|
||||
if isinstance(key, tuple):
|
||||
k, t_step = key
|
||||
if k is not None:
|
||||
if t_step is not None:
|
||||
return self._history[t_step][k]
|
||||
else:
|
||||
return {tt: tv.get(k, None) for tt, tv in self._history.items()}
|
||||
else:
|
||||
return self._history[t_step]
|
||||
return self.state[key]
|
||||
|
||||
def __setitem__(self, key, value):
|
||||
self.state[key] = value
|
||||
|
||||
def save_state(self):
|
||||
self._history[self.now] = deepcopy(self.state)
|
||||
|
||||
@property
|
||||
def now(self):
|
||||
try:
|
||||
return self.env.now
|
||||
except AttributeError:
|
||||
# No environment
|
||||
return None
|
||||
|
||||
def run(self):
|
||||
while True:
|
||||
res = self.step()
|
||||
yield res or self.env.timeout(self.env.interval)
|
||||
|
||||
def step(self):
|
||||
pass
|
||||
|
||||
def to_json(self):
|
||||
return json.dumps(self._history)
|
||||
|
||||
|
||||
class NetworkAgent(BaseAgent, nxsim.BaseNetworkAgent):
|
||||
|
||||
def count_agents(self, state_id=None, limit_neighbors=False):
|
||||
if limit_neighbors:
|
||||
agents = self.global_topology.neighbors(self.id)
|
||||
else:
|
||||
agents = self.global_topology.nodes()
|
||||
count = 0
|
||||
for agent in agents:
|
||||
if state_id and state_id != self.global_topology.node[agent]['agent'].state['id']:
|
||||
continue
|
||||
count += 1
|
||||
return count
|
||||
|
||||
def count_neighboring_agents(self, state_id=None):
|
||||
return self.count_agents(state_id, limit_neighbors=True)
|
||||
|
||||
|
||||
def state(func):
|
||||
|
||||
@wraps(func)
|
||||
def func_wrapper(self):
|
||||
when = None
|
||||
next_state = func(self)
|
||||
try:
|
||||
next_state, when = next_state
|
||||
except TypeError:
|
||||
pass
|
||||
if next_state:
|
||||
try:
|
||||
self.state['id'] = next_state.id
|
||||
except AttributeError:
|
||||
raise NotImplemented('State id %s is not valid.' % next_state)
|
||||
return when
|
||||
|
||||
func_wrapper.id = func.__name__
|
||||
func_wrapper.is_default = False
|
||||
return func_wrapper
|
||||
|
||||
|
||||
def default_state(func):
|
||||
func.is_default = True
|
||||
return func
|
||||
|
||||
|
||||
class MetaFSM(MetaAgent):
|
||||
def __init__(cls, name, bases, nmspc):
|
||||
super(MetaFSM, cls).__init__(name, bases, nmspc)
|
||||
states = {}
|
||||
# Re-use states from inherited classes
|
||||
default_state = None
|
||||
for i in bases:
|
||||
if isinstance(i, MetaFSM):
|
||||
for state_id, state in i.states.items():
|
||||
if state.is_default:
|
||||
default_state = state
|
||||
states[state_id] = state
|
||||
|
||||
# Add new states
|
||||
for name, func in nmspc.items():
|
||||
if hasattr(func, 'id'):
|
||||
if func.is_default:
|
||||
default_state = func
|
||||
states[func.id] = func
|
||||
cls.default_state = default_state
|
||||
cls.states = states
|
||||
|
||||
|
||||
class FSM(BaseAgent, metaclass=MetaFSM):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super(FSM, self).__init__(*args, **kwargs)
|
||||
if 'id' not in self.state:
|
||||
self.state['id'] = self.default_state.id
|
||||
|
||||
def step(self):
|
||||
if 'id' in self.state:
|
||||
next_state = self.state['id']
|
||||
elif self.default_state:
|
||||
next_state = self.default_state.id
|
||||
else:
|
||||
raise Exception('{} has no valid state id or default state'.format(self))
|
||||
if next_state not in self.states:
|
||||
raise Exception('{} is not a valid id for {}'.format(next_state, self))
|
||||
self.states[next_state](self)
|
||||
|
||||
|
||||
from .BassModel import *
|
||||
from .BigMarketModel import *
|
||||
from .IndependentCascadeModel import *
|
||||
from .ModelM2 import *
|
||||
from .SentimentCorrelationModel import *
|
||||
from .SISaModel import *
|
||||
from .CounterModel import *
|
||||
from .DrawingAgent import *
|
||||
@ -1,23 +0,0 @@
|
||||
import pandas as pd
|
||||
|
||||
import glob
|
||||
import yaml
|
||||
from os.path import join
|
||||
|
||||
|
||||
def get_data(pattern, process=True, attributes=None):
|
||||
for folder in glob.glob(pattern):
|
||||
config_file = glob.glob(join(folder, '*.yml'))[0]
|
||||
config = yaml.load(open(config_file))
|
||||
for trial_data in sorted(glob.glob(join(folder, '*.environment.csv'))):
|
||||
df = pd.read_csv(trial_data)
|
||||
if process:
|
||||
if attributes is not None:
|
||||
df = df[df['attribute'].isin(attributes)]
|
||||
df = df.pivot_table(values='attribute', index='tstep', columns=['value'], aggfunc='count').fillna(0)
|
||||
yield config_file, df, config
|
||||
|
||||
|
||||
def plot_all(*args, **kwargs):
|
||||
for config_file, df, config in sorted(get_data(*args, **kwargs)):
|
||||
df.plot(title=config['name'])
|
||||
@ -1,190 +0,0 @@
|
||||
import os
|
||||
import csv
|
||||
import weakref
|
||||
from random import random
|
||||
from copy import deepcopy
|
||||
|
||||
import networkx as nx
|
||||
import nxsim
|
||||
|
||||
|
||||
class SoilEnvironment(nxsim.NetworkEnvironment):
|
||||
|
||||
def __init__(self, name=None,
|
||||
network_agents=None,
|
||||
environment_agents=None,
|
||||
states=None,
|
||||
default_state=None,
|
||||
interval=1,
|
||||
*args, **kwargs):
|
||||
self.name = name or 'UnnamedEnvironment'
|
||||
self.states = deepcopy(states) or {}
|
||||
self.default_state = deepcopy(default_state) or {}
|
||||
super().__init__(*args, **kwargs)
|
||||
self._env_agents = {}
|
||||
self._history = {}
|
||||
self.interval = interval
|
||||
self.logger = None
|
||||
# Add environment agents first, so their events get
|
||||
# executed before network agents
|
||||
self.environment_agents = environment_agents or []
|
||||
self.network_agents = network_agents or []
|
||||
self.process(self.save_state())
|
||||
|
||||
@property
|
||||
def agents(self):
|
||||
yield from self.environment_agents
|
||||
yield from self.network_agents
|
||||
|
||||
@property
|
||||
def environment_agents(self):
|
||||
for ref in self._env_agents.values():
|
||||
yield ref()
|
||||
|
||||
@environment_agents.setter
|
||||
def environment_agents(self, environment_agents):
|
||||
# Set up environmental agent
|
||||
self._env_agents = {}
|
||||
for item in environment_agents:
|
||||
kwargs = deepcopy(item)
|
||||
atype = kwargs.pop('agent_type')
|
||||
kwargs['agent_id'] = kwargs.get('agent_id', atype.__name__)
|
||||
kwargs['state'] = kwargs.get('state', {})
|
||||
a = atype(**kwargs,
|
||||
environment=self)
|
||||
self._env_agents[a.id] = weakref.ref(a)
|
||||
|
||||
@property
|
||||
def network_agents(self):
|
||||
for i in self.G.nodes():
|
||||
node = self.G.node[i]
|
||||
if 'agent' in node:
|
||||
yield node['agent']
|
||||
|
||||
@network_agents.setter
|
||||
def network_agents(self, network_agents):
|
||||
for ix in self.G.nodes():
|
||||
i = ix
|
||||
node = self.G.node[i]
|
||||
v = random()
|
||||
found = False
|
||||
for d in network_agents:
|
||||
threshold = d['threshold']
|
||||
if v >= threshold[0] and v < threshold[1]:
|
||||
agent = d['agent_type']
|
||||
state = None
|
||||
if 'state' in d:
|
||||
state = deepcopy(d['state'])
|
||||
else:
|
||||
try:
|
||||
state = self.states[i]
|
||||
except (IndexError, KeyError):
|
||||
state = deepcopy(self.default_state)
|
||||
node['agent'] = agent(environment=self,
|
||||
agent_id=i,
|
||||
state=state)
|
||||
found = True
|
||||
break
|
||||
assert found
|
||||
|
||||
def run(self, *args, **kwargs):
|
||||
self._save_state()
|
||||
super().run(*args, **kwargs)
|
||||
self._save_state()
|
||||
|
||||
def _save_state(self):
|
||||
for agent in self.agents:
|
||||
agent.save_state()
|
||||
self._history[self.now] = deepcopy(self.environment_params)
|
||||
|
||||
def save_state(self):
|
||||
while True:
|
||||
ev = self.event()
|
||||
ev._ok = True
|
||||
# Schedule the event with minimum priority so
|
||||
# that it executes after all agents are done
|
||||
self.schedule(ev, -1, self.interval)
|
||||
yield ev
|
||||
self._save_state()
|
||||
|
||||
def __getitem__(self, key):
|
||||
return self.environment_params[key]
|
||||
|
||||
def __setitem__(self, key, value):
|
||||
self.environment_params[key] = value
|
||||
|
||||
def get_path(self, dir_path=None):
|
||||
dir_path = dir_path or self.sim().dir_path
|
||||
if not os.path.exists(dir_path):
|
||||
os.makedirs(dir_path)
|
||||
return dir_path
|
||||
|
||||
def get_agent(self, agent_id):
|
||||
return self.G.node[agent_id]['agent']
|
||||
|
||||
def get_agents(self):
|
||||
return list(self.agents)
|
||||
|
||||
def dump_csv(self, dir_path=None):
|
||||
csv_name = os.path.join(self.get_path(dir_path),
|
||||
'{}.environment.csv'.format(self.name))
|
||||
|
||||
with open(csv_name, 'w') as f:
|
||||
cr = csv.writer(f)
|
||||
cr.writerow(('agent_id', 'tstep', 'attribute', 'value'))
|
||||
for i in self.history_to_tuples():
|
||||
cr.writerow(i)
|
||||
|
||||
def dump_gexf(self, dir_path=None):
|
||||
G = self.history_to_graph()
|
||||
graph_path = os.path.join(self.get_path(dir_path),
|
||||
self.name+".gexf")
|
||||
nx.write_gexf(G, graph_path, version="1.2draft")
|
||||
|
||||
def history_to_tuples(self):
|
||||
for tstep, state in self._history.items():
|
||||
for attribute, value in state.items():
|
||||
yield ('env', tstep, attribute, value)
|
||||
for agent in self.agents:
|
||||
for tstep, state in agent._history.items():
|
||||
for attribute, value in state.items():
|
||||
yield (agent.id, tstep, attribute, value)
|
||||
|
||||
def history_to_graph(self):
|
||||
G = nx.Graph(self.G)
|
||||
|
||||
for agent in self.agents:
|
||||
|
||||
attributes = {'agent': str(agent.__class__)}
|
||||
lastattributes = {}
|
||||
spells = []
|
||||
lastvisible = False
|
||||
laststep = None
|
||||
for t_step, state in reversed(agent._history.items()):
|
||||
for attribute, value in state.items():
|
||||
if attribute == 'visible':
|
||||
nowvisible = state[attribute]
|
||||
if nowvisible and not lastvisible:
|
||||
laststep = t_step
|
||||
if not nowvisible and lastvisible:
|
||||
spells.append((laststep, t_step))
|
||||
|
||||
lastvisible = nowvisible
|
||||
else:
|
||||
if attribute not in lastattributes or lastattributes[attribute][0] != value:
|
||||
laststep = lastattributes.get(attribute,
|
||||
(None, None))[1]
|
||||
value = (state[attribute], t_step, laststep)
|
||||
key = 'attr_' + attribute
|
||||
if key not in attributes:
|
||||
attributes[key] = list()
|
||||
attributes[key].append(value)
|
||||
lastattributes[attribute] = (state[attribute], t_step)
|
||||
if lastvisible:
|
||||
spells.append((laststep, None))
|
||||
if spells:
|
||||
G.add_node(agent.id, attributes, spells=spells)
|
||||
else:
|
||||
G.add_node(agent.id, attributes)
|
||||
|
||||
return G
|
||||
@ -1 +0,0 @@
|
||||
# General configuration
|
||||
@ -1,241 +0,0 @@
|
||||
import weakref
|
||||
import os
|
||||
import csv
|
||||
import time
|
||||
import yaml
|
||||
import networkx as nx
|
||||
from networkx.readwrite import json_graph
|
||||
|
||||
from copy import deepcopy
|
||||
from random import random
|
||||
from matplotlib import pyplot as plt
|
||||
|
||||
import pickle
|
||||
|
||||
from nxsim import NetworkSimulation
|
||||
|
||||
from . import agents, utils, environment, basestring
|
||||
|
||||
|
||||
class SoilSimulation(NetworkSimulation):
|
||||
"""
|
||||
Subclass of nsim.NetworkSimulation with three main differences:
|
||||
1) agent type can be specified by name or by class.
|
||||
2) instead of just one type, an network_agents can be used.
|
||||
The distribution specifies the weight (or probability) of each
|
||||
agent type in the topology. This is an example distribution: ::
|
||||
|
||||
[
|
||||
{'agent_type': 'agent_type_1',
|
||||
'weight': 0.2,
|
||||
'state': {
|
||||
'id': 0
|
||||
}
|
||||
},
|
||||
{'agent_type': 'agent_type_2',
|
||||
'weight': 0.8,
|
||||
'state': {
|
||||
'id': 1
|
||||
}
|
||||
}
|
||||
]
|
||||
|
||||
In this example, 20% of the nodes will be marked as type
|
||||
'agent_type_1'.
|
||||
3) if no initial state is given, each node's state will be set
|
||||
to `{'id': 0}`.
|
||||
"""
|
||||
def __init__(self, name=None, topology=None, network_params=None,
|
||||
network_agents=None, agent_type=None, states=None,
|
||||
default_state=None, interval=1,
|
||||
dir_path=None, num_trials=3, max_time=100,
|
||||
agent_module=None,
|
||||
environment_agents=None, environment_params=None):
|
||||
|
||||
if topology is None:
|
||||
topology = utils.load_network(network_params,
|
||||
dir_path=dir_path)
|
||||
elif isinstance(topology, basestring) or isinstance(topology, dict):
|
||||
topology = json_graph.node_link_graph(topology)
|
||||
|
||||
self.topology = nx.Graph(topology)
|
||||
self.network_params = network_params
|
||||
self.name = name or 'UnnamedSimulation'
|
||||
self.num_trials = num_trials
|
||||
self.max_time = max_time
|
||||
self.default_state = default_state or {}
|
||||
self.dir_path = dir_path or os.getcwd()
|
||||
self.interval = interval
|
||||
self.environment_params = environment_params or {}
|
||||
|
||||
environment_agents = environment_agents or []
|
||||
self.environment_agents = self._convert_agent_types(environment_agents)
|
||||
|
||||
distro = self.calculate_distribution(network_agents,
|
||||
agent_type)
|
||||
self.network_agents = self._convert_agent_types(distro)
|
||||
|
||||
self.states = self.validate_states(states,
|
||||
topology)
|
||||
|
||||
def calculate_distribution(self,
|
||||
network_agents=None,
|
||||
agent_type=None):
|
||||
if network_agents:
|
||||
network_agents = deepcopy(network_agents)
|
||||
elif agent_type:
|
||||
network_agents = [{'agent_type': agent_type}]
|
||||
else:
|
||||
return []
|
||||
|
||||
# Calculate the thresholds
|
||||
total = sum(x.get('weight', 1) for x in network_agents)
|
||||
acc = 0
|
||||
for v in network_agents:
|
||||
upper = acc + (v.get('weight', 1)/total)
|
||||
v['threshold'] = [acc, upper]
|
||||
acc = upper
|
||||
return network_agents
|
||||
|
||||
def serialize_distribution(self):
|
||||
d = self._convert_agent_types(self.network_agents,
|
||||
to_string=True)
|
||||
for v in d:
|
||||
if 'threshold' in v:
|
||||
del v['threshold']
|
||||
return d
|
||||
|
||||
def _convert_agent_types(self, ind, to_string=False):
|
||||
d = deepcopy(ind)
|
||||
for v in d:
|
||||
agent_type = v['agent_type']
|
||||
if to_string and not isinstance(agent_type, str):
|
||||
v['agent_type'] = str(agent_type.__name__)
|
||||
elif not to_string and isinstance(agent_type, str):
|
||||
v['agent_type'] = agents.agent_types[agent_type]
|
||||
return d
|
||||
|
||||
def validate_states(self, states, topology):
|
||||
states = states or []
|
||||
# Validate states to avoid ignoring states during
|
||||
# initialization
|
||||
if isinstance(states, dict):
|
||||
for x in states:
|
||||
assert x in self.topology.node
|
||||
else:
|
||||
assert len(states) <= len(self.topology)
|
||||
return states
|
||||
|
||||
def run_simulation(self):
|
||||
return self.run()
|
||||
|
||||
def run(self):
|
||||
return list(self.run_simulation_gen())
|
||||
|
||||
def run_simulation_gen(self):
|
||||
with utils.timer('simulation'):
|
||||
for i in range(self.num_trials):
|
||||
yield self.run_trial(i)
|
||||
|
||||
def run_trial(self, trial_id=0):
|
||||
"""Run a single trial of the simulation
|
||||
|
||||
Parameters
|
||||
----------
|
||||
trial_id : int
|
||||
"""
|
||||
# Set-up trial environment and graph
|
||||
print('Trial: {}'.format(trial_id))
|
||||
env_name = '{}_trial_{}'.format(self.name, trial_id)
|
||||
env = environment.SoilEnvironment(name=env_name,
|
||||
topology=self.topology.copy(),
|
||||
initial_time=0,
|
||||
interval=self.interval,
|
||||
network_agents=self.network_agents,
|
||||
states=self.states,
|
||||
default_state=self.default_state,
|
||||
environment_agents=self.environment_agents,
|
||||
**self.environment_params)
|
||||
|
||||
env.sim = weakref.ref(self)
|
||||
# Set up agents on nodes
|
||||
print('\tRunning')
|
||||
with utils.timer('trial'):
|
||||
env.run(until=self.max_time)
|
||||
return env
|
||||
|
||||
def to_dict(self):
|
||||
return self.__getstate__()
|
||||
|
||||
def to_yaml(self):
|
||||
return yaml.dump(self.to_dict())
|
||||
|
||||
def dump_yaml(self, dir_path=None, file_name=None):
|
||||
dir_path = dir_path or self.dir_path
|
||||
if not os.path.exists(dir_path):
|
||||
os.makedirs(dir_path)
|
||||
if not file_name:
|
||||
file_name = os.path.join(dir_path,
|
||||
'{}.dumped.yml'.format(self.name))
|
||||
with open(file_name, 'w') as f:
|
||||
f.write(self.to_yaml())
|
||||
|
||||
def dump_pickle(self, dir_path=None, pickle_name=None):
|
||||
dir_path = dir_path or self.dir_path
|
||||
if not os.path.exists(dir_path):
|
||||
os.makedirs(dir_path)
|
||||
if not pickle_name:
|
||||
pickle_name = os.path.join(dir_path,
|
||||
'{}.simulation.pickle'.format(self.name))
|
||||
with open(pickle_name, 'wb') as f:
|
||||
pickle.dump(self, f)
|
||||
|
||||
def __getstate__(self):
|
||||
state = self.__dict__.copy()
|
||||
state['topology'] = json_graph.node_link_data(self.topology)
|
||||
state['network_agents'] = self.serialize_distribution()
|
||||
state['environment_agents'] = self._convert_agent_types(self.environment_agents,
|
||||
to_string=True)
|
||||
return state
|
||||
|
||||
def __setstate__(self, state):
|
||||
self.__dict__ = state
|
||||
self.topology = json_graph.node_link_graph(state['topology'])
|
||||
self.network_agents = self._convert_agent_types(self.network_agents)
|
||||
self.environment_agents = self._convert_agent_types(self.environment_agents)
|
||||
return state
|
||||
|
||||
|
||||
def from_config(config, G=None):
|
||||
config = list(utils.load_config(config))
|
||||
if len(config) > 1:
|
||||
raise AttributeError('Provide only one configuration')
|
||||
config = config[0][0]
|
||||
sim = SoilSimulation(**config)
|
||||
return sim
|
||||
|
||||
|
||||
def run_from_config(*configs, dump=True, results_dir=None, timestamp=False):
|
||||
if not results_dir:
|
||||
results_dir = 'soil_output'
|
||||
for config_def in configs:
|
||||
for config, cpath in utils.load_config(config_def):
|
||||
name = config.get('name', 'unnamed')
|
||||
print("Using config(s): {name}".format(name=name))
|
||||
|
||||
sim = SoilSimulation(**config)
|
||||
if timestamp:
|
||||
sim_folder = '{}_{}'.format(sim.name,
|
||||
time.strftime("%Y-%m-%d_%H:%M:%S"))
|
||||
else:
|
||||
sim_folder = sim.name
|
||||
dir_path = os.path.join(results_dir,
|
||||
sim_folder)
|
||||
results = sim.run_simulation()
|
||||
|
||||
if dump:
|
||||
sim.dump_pickle(dir_path)
|
||||
sim.dump_yaml(dir_path)
|
||||
for env in results:
|
||||
env.dump_gexf(dir_path)
|
||||
env.dump_csv(dir_path)
|
||||
@ -1,61 +0,0 @@
|
||||
import os
|
||||
import yaml
|
||||
from time import time
|
||||
from glob import glob
|
||||
|
||||
import networkx as nx
|
||||
|
||||
from contextlib import contextmanager
|
||||
|
||||
|
||||
def load_network(network_params, dir_path=None):
|
||||
path = network_params.get('path', None)
|
||||
if path:
|
||||
if dir_path and not os.path.isabs(path):
|
||||
path = os.path.join(dir_path, path)
|
||||
extension = os.path.splitext(path)[1][1:]
|
||||
kwargs = {}
|
||||
if extension == 'gexf':
|
||||
kwargs['version'] = '1.2draft'
|
||||
kwargs['node_type'] = int
|
||||
try:
|
||||
method = getattr(nx.readwrite, 'read_' + extension)
|
||||
except AttributeError:
|
||||
raise AttributeError('Unknown format')
|
||||
return method(path, **kwargs)
|
||||
|
||||
net_args = network_params.copy()
|
||||
net_type = net_args.pop('generator')
|
||||
|
||||
method = getattr(nx.generators, net_type)
|
||||
return method(**net_args)
|
||||
|
||||
|
||||
def load_file(infile):
|
||||
with open(infile, 'r') as f:
|
||||
return list(yaml.load_all(f))
|
||||
|
||||
|
||||
def load_files(*patterns):
|
||||
for pattern in patterns:
|
||||
for i in glob(pattern):
|
||||
for config in load_file(i):
|
||||
yield config, os.path.abspath(i)
|
||||
|
||||
|
||||
def load_config(config):
|
||||
if isinstance(config, dict):
|
||||
yield config, None
|
||||
else:
|
||||
yield from load_files(config)
|
||||
|
||||
|
||||
@contextmanager
|
||||
def timer(name='task', pre="", function=print, to_object=None):
|
||||
start = time()
|
||||
yield start
|
||||
end = time()
|
||||
function('{}Finished {} in {} seconds'.format(pre, name, str(end-start)))
|
||||
if to_object:
|
||||
to_object.start = start
|
||||
to_object.end = end
|
||||
913
soil_tutorial.ipynb
Executable file
913
soil_tutorial.ipynb
Executable file
@ -0,0 +1,913 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<img src=\"./logo_gsi.png\" alt=\"Grupo de Sistemas Inteligentes\" width=\"100px\">"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# SOIL Tutorial "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This notebook contains a tutorial to learn how to use the SOcial network sImuLator (SOIL) written in Python. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Introduction"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"SOIL is based in 2 main files:\n",
|
||||
"* __soil.py__: It's the main file of SOIL. The network creation, simulation and visualization are done in this file.\n",
|
||||
"+ __settings.json__: This file contains every variable needed in the simulation in order to be modified easily.\n",
|
||||
"- __models__: All the spread models already implemented are stored in this directory as modules."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Requirements"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"SOIL requires to install:\n",
|
||||
"* **Python 3** - you can use the Conda distribution\n",
|
||||
"* **NetworkX** - install with conda install networkx or pip install networkx\n",
|
||||
"* **simpy** - install with pip install simpy\n",
|
||||
"* **nxsim** - install with pip install nxsim\n",
|
||||
"* **Gephi** - Available at https://gephi.org"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Soil.py"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Imports and data initialization"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"First of all, you need to make all the imports. This simulator is based on [nxsim](https://pypi.python.org/pypi/nxsim), using [networkx](https://networkx.github.io/) for network management. We will also include the models and settings files where the spread models and initialization variables are stored."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from models import *\n",
|
||||
"from nxsim import NetworkSimulation\n",
|
||||
"# import numpy\n",
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"import networkx as nx\n",
|
||||
"import settings\n",
|
||||
"import models\n",
|
||||
"import math\n",
|
||||
"import json"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Network creation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Using a parameter provided in the settings file, we can choose what type of network we want to create, as well as the number of nodes and some other parameters. More types of networks can be implemented using [networkx](https://networkx.github.io/)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if settings.network_params[\"network_type\"] == 0:\n",
|
||||
" G = nx.complete_graph(settings.network_params[\"number_of_nodes\"])\n",
|
||||
"if settings.network_params[\"network_type\"] == 1:\n",
|
||||
" G = nx.barabasi_albert_graph(settings.network_params[\"number_of_nodes\"], 10)\n",
|
||||
"if settings.network_params[\"network_type\"] == 2:\n",
|
||||
" G = nx.margulis_gabber_galil_graph(settings.network_params[\"number_of_nodes\"], None)\n",
|
||||
"# More types of networks can be added here"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Visualization"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"In order to analyse the results of the simulation. We include them in the topology and a .gexf file is generated. This allows the user to picture the network in [Gephi](https://gephi.org/). A JSON file is also generated to permit further analysis.\n",
|
||||
"\n",
|
||||
"The JSON file follows this schema. The file has three depth levels. In the first one we can find the identifier of each agent in the network. Secondly, inside every agent we can observe every attribute that the creator of the model wanted to make visible. In the deepest level the different values of each attribute are\n",
|
||||
"visible.\n",
|
||||
"\n",
|
||||
"\t{\n",
|
||||
"\t\t\"agent_0\": {\n",
|
||||
"\t\t\t\"attribute_X\": {\n",
|
||||
"\t\t\t\t\"0\": 0,\n",
|
||||
"\t\t\t\t\"2\": 0,\n",
|
||||
"\t\t\t\t\"4\": 1,\n",
|
||||
"\t\t\t\t\"6\": 2,\n",
|
||||
"\t\t\t\t...\n",
|
||||
"\t\t\t}\n",
|
||||
"\t\t},\n",
|
||||
"\t\t\"agent_1\": {\n",
|
||||
"\t\t\t\"attribute_X\": {\n",
|
||||
"\t\t\t\t\"0\": 0,\n",
|
||||
"\t\t\t\t\"2\": 3,\n",
|
||||
"\t\t\t\t...\n",
|
||||
"\t\t\t}\n",
|
||||
"\t\t},\n",
|
||||
"\t\t...\t\t\n",
|
||||
"\t}\n",
|
||||
"\n",
|
||||
"This is done with the following code:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def visualization(graph_name):\n",
|
||||
"\n",
|
||||
" for x in range(0, settings.network_params[\"number_of_nodes\"]):\n",
|
||||
" for attribute in models.networkStatus[\"agent_%s\" % x]:\n",
|
||||
" emotionStatusAux = []\n",
|
||||
" for t_step in models.networkStatus[\"agent_%s\" % x][attribute]:\n",
|
||||
" prec = 2\n",
|
||||
" output = math.floor(models.networkStatus[\"agent_%s\" % x][attribute][t_step] * (10 ** prec)) / (10 ** prec) # 2 decimals\n",
|
||||
" emotionStatusAux.append((output, t_step, t_step + settings.network_params[\"timeout\"]))\n",
|
||||
" attributes = {}\n",
|
||||
" attributes[attribute] = emotionStatusAux\n",
|
||||
" G.add_node(x, attributes)\n",
|
||||
"\n",
|
||||
" print(\"Done!\")\n",
|
||||
"\n",
|
||||
" with open('data.txt', 'w') as outfile:\n",
|
||||
" json.dump(models.networkStatus, outfile, sort_keys=True, indent=4, separators=(',', ': '))\n",
|
||||
"\n",
|
||||
" nx.write_gexf(G, graph_name+\".gexf\", version=\"1.2draft\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"That's only the basic visualization. Everything you need can be implemented as well. For example:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def results(model_name):\n",
|
||||
" x_values = []\n",
|
||||
" infected_values = []\n",
|
||||
" neutral_values = []\n",
|
||||
" cured_values = []\n",
|
||||
" vaccinated_values = []\n",
|
||||
"\n",
|
||||
" attribute_plot = 'status'\n",
|
||||
" for time in range(0, settings.network_params[\"max_time\"]):\n",
|
||||
" value_infectados = 0\n",
|
||||
" value_neutral = 0\n",
|
||||
" value_cured = 0\n",
|
||||
" value_vaccinated = 0\n",
|
||||
" real_time = time * settings.network_params[\"timeout\"]\n",
|
||||
" activity = False\n",
|
||||
" for x in range(0, settings.network_params[\"number_of_nodes\"]):\n",
|
||||
" if attribute_plot in models.networkStatus[\"agent_%s\" % x]:\n",
|
||||
" if real_time in models.networkStatus[\"agent_%s\" % x][attribute_plot]:\n",
|
||||
" if models.networkStatus[\"agent_%s\" % x][attribute_plot][real_time] == 1: ## Infected\n",
|
||||
" value_infectados += 1\n",
|
||||
" activity = True\n",
|
||||
" if models.networkStatus[\"agent_%s\" % x][attribute_plot][real_time] == 0: ## Neutral\n",
|
||||
" value_neutral += 1\n",
|
||||
" activity = True\n",
|
||||
" if models.networkStatus[\"agent_%s\" % x][attribute_plot][real_time] == 2: ## Cured\n",
|
||||
" value_cured += 1\n",
|
||||
" activity = True\n",
|
||||
" if models.networkStatus[\"agent_%s\" % x][attribute_plot][real_time] == 3: ## Vaccinated\n",
|
||||
" value_vaccinated += 1\n",
|
||||
" activity = True\n",
|
||||
"\n",
|
||||
" if activity:\n",
|
||||
" x_values.append(real_time)\n",
|
||||
" infected_values.append(value_infectados)\n",
|
||||
" neutral_values.append(value_neutral)\n",
|
||||
" cured_values.append(value_cured)\n",
|
||||
" vaccinated_values.append(value_vaccinated)\n",
|
||||
" activity = False\n",
|
||||
"\n",
|
||||
" fig1 = plt.figure()\n",
|
||||
" ax1 = fig1.add_subplot(111)\n",
|
||||
"\n",
|
||||
" infected_line = ax1.plot(x_values, infected_values, label='Infected')\n",
|
||||
" neutral_line = ax1.plot(x_values, neutral_values, label='Neutral')\n",
|
||||
" cured_line = ax1.plot(x_values, cured_values, label='Cured')\n",
|
||||
" vaccinated_line = ax1.plot(x_values, vaccinated_values, label='Vaccinated')\n",
|
||||
" ax1.legend()\n",
|
||||
" fig1.savefig(model_name + '.png')\n",
|
||||
" # plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Simulation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The simulation starts with the following code. The user can provide the network topology, the maximum time of simulation, the spread model to be used as well as other parameters."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"agents = settings.environment_params['agent']\n",
|
||||
"\n",
|
||||
"print(\"Using Agent(s): {agents}\".format(agents=agents))\n",
|
||||
"\n",
|
||||
"if len(agents) > 1:\n",
|
||||
" for agent in agents:\n",
|
||||
" sim = NetworkSimulation(topology=G, states=init_states, agent_type=locals()[agent], max_time=settings.network_params[\"max_time\"],\n",
|
||||
" num_trials=settings.network_params[\"num_trials\"], logging_interval=1.0, **settings.environment_params)\n",
|
||||
" sim.run_simulation()\n",
|
||||
" print(str(agent))\n",
|
||||
" results(str(agent))\n",
|
||||
" visualization(str(agent))\n",
|
||||
"else:\n",
|
||||
" agent = agents[0]\n",
|
||||
" sim = NetworkSimulation(topology=G, states=init_states, agent_type=locals()[agent], max_time=settings.network_params[\"max_time\"],\n",
|
||||
" num_trials=settings.network_params[\"num_trials\"], logging_interval=1.0, **settings.environment_params)\n",
|
||||
" sim.run_simulation()\n",
|
||||
" results(str(agent))\n",
|
||||
" visualization(str(agent))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Models"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Imports and initialization"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import settings\n",
|
||||
"\n",
|
||||
"networkStatus = {} # Dict that will contain the status of every agent in the network\n",
|
||||
"\n",
|
||||
"sentimentCorrelationNodeArray = []\n",
|
||||
"for x in range(0, settings.network_params[\"number_of_nodes\"]):\n",
|
||||
" sentimentCorrelationNodeArray.append({'id': x})\n",
|
||||
"# Initialize agent states. Let's assume everyone is normal.\n",
|
||||
"init_states = [{'id': 0, } for _ in range(settings.network_params[\"number_of_nodes\"])]\n",
|
||||
" # add keys as as necessary, but \"id\" must always refer to that state category"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Base behaviour"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Every spread model used in SOIL should extend the base behaviour class. By doing this the exportation of the attributes values will be automatic. This feature will be explained in the Spread Models section. The class looks like this:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import settings\n",
|
||||
"from nxsim import BaseNetworkAgent\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class BaseBehaviour(BaseNetworkAgent):\n",
|
||||
"\n",
|
||||
" def __init__(self, environment=None, agent_id=0, state=()):\n",
|
||||
" super().__init__(environment=environment, agent_id=agent_id, state=state)\n",
|
||||
" self._attrs = {}\n",
|
||||
"\n",
|
||||
" @property\n",
|
||||
" def attrs(self):\n",
|
||||
" now = self.env.now\n",
|
||||
" if now not in self._attrs:\n",
|
||||
" self._attrs[now] = {}\n",
|
||||
" return self._attrs[now]\n",
|
||||
"\n",
|
||||
" @attrs.setter\n",
|
||||
" def attrs(self, value):\n",
|
||||
" self._attrs[self.env.now] = value\n",
|
||||
"\n",
|
||||
" def run(self):\n",
|
||||
" while True:\n",
|
||||
" self.step(self.env.now)\n",
|
||||
" yield self.env.timeout(settings.network_params[\"timeout\"])\n",
|
||||
"\n",
|
||||
" def step(self, now):\n",
|
||||
" networkStatus['agent_%s'% self.id] = self.to_json()\n",
|
||||
"\n",
|
||||
" def to_json(self):\n",
|
||||
" final = {}\n",
|
||||
" for stamp, attrs in self._attrs.items():\n",
|
||||
" for a in attrs:\n",
|
||||
" if a not in final:\n",
|
||||
" final[a] = {}\n",
|
||||
" final[a][stamp] = attrs[a]\n",
|
||||
" return final"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Spread models"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Every model to be implemented must include an init and a step function. Depending on your model, you would need different attributes. If you want them to be automatic exported for a further analysis, you must name them like this *self.attrs['name_of_attribute']*. Moreover, the last thing you should do inside the step function is call the following method *super().step(now)*. This call will store the values.\n",
|
||||
"\n",
|
||||
"Some other tips:\n",
|
||||
"* __self.state['id']__: To check the id of the current agent/node.\n",
|
||||
"* __self.get_neighboring_agents(state_id=x)__: Returns the neighbours agents/nodes with the id provided\n",
|
||||
"\n",
|
||||
"An example of a spread model already implemented and working:\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import settings\n",
|
||||
"import random\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class ControlModelM2(BaseBehaviour):\n",
|
||||
"\n",
|
||||
" # Init infected\n",
|
||||
" init_states[random.randint(0, settings.network_params[\"number_of_nodes\"]-1)] = {'id': 1}\n",
|
||||
" init_states[random.randint(0, settings.network_params[\"number_of_nodes\"]-1)] = {'id': 1}\n",
|
||||
"\n",
|
||||
" # Init beacons\n",
|
||||
" init_states[random.randint(0, settings.network_params[\"number_of_nodes\"]-1)] = {'id': 4}\n",
|
||||
" init_states[random.randint(0, settings.network_params[\"number_of_nodes\"]-1)] = {'id': 4}\n",
|
||||
"\n",
|
||||
" def __init__(self, environment=None, agent_id=0, state=()):\n",
|
||||
" super().__init__(environment=environment, agent_id=agent_id, state=state)\n",
|
||||
"\n",
|
||||
" self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],\n",
|
||||
" environment.environment_params['standard_variance'])\n",
|
||||
"\n",
|
||||
" self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],\n",
|
||||
" environment.environment_params['standard_variance'])\n",
|
||||
"\n",
|
||||
" self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],\n",
|
||||
" environment.environment_params['standard_variance'])\n",
|
||||
" self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],\n",
|
||||
" environment.environment_params['standard_variance'])\n",
|
||||
"\n",
|
||||
" self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],\n",
|
||||
" environment.environment_params['standard_variance'])\n",
|
||||
" self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],\n",
|
||||
" environment.environment_params['standard_variance'])\n",
|
||||
" self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],\n",
|
||||
" environment.environment_params['standard_variance'])\n",
|
||||
"\n",
|
||||
" def step(self, now):\n",
|
||||
"\n",
|
||||
" if self.state['id'] == 0: # Neutral\n",
|
||||
" self.neutral_behaviour()\n",
|
||||
" elif self.state['id'] == 1: # Infected\n",
|
||||
" self.infected_behaviour()\n",
|
||||
" elif self.state['id'] == 2: # Cured\n",
|
||||
" self.cured_behaviour()\n",
|
||||
" elif self.state['id'] == 3: # Vaccinated\n",
|
||||
" self.vaccinated_behaviour()\n",
|
||||
" elif self.state['id'] == 4: # Beacon-off\n",
|
||||
" self.beacon_off_behaviour()\n",
|
||||
" elif self.state['id'] == 5: # Beacon-on\n",
|
||||
" self.beacon_on_behaviour()\n",
|
||||
"\n",
|
||||
" self.attrs['status'] = self.state['id']\n",
|
||||
" super().step(now)\n",
|
||||
"\n",
|
||||
" def neutral_behaviour(self):\n",
|
||||
"\n",
|
||||
" # Infected\n",
|
||||
" infected_neighbors = self.get_neighboring_agents(state_id=1)\n",
|
||||
" if len(infected_neighbors) > 0:\n",
|
||||
" if random.random() < self.prob_neutral_making_denier:\n",
|
||||
" self.state['id'] = 3 # Vaccinated making denier\n",
|
||||
"\n",
|
||||
" def infected_behaviour(self):\n",
|
||||
"\n",
|
||||
" # Neutral\n",
|
||||
" neutral_neighbors = self.get_neighboring_agents(state_id=0)\n",
|
||||
" for neighbor in neutral_neighbors:\n",
|
||||
" if random.random() < self.prob_infect:\n",
|
||||
" neighbor.state['id'] = 1 # Infected\n",
|
||||
"\n",
|
||||
" def cured_behaviour(self):\n",
|
||||
"\n",
|
||||
" # Vaccinate\n",
|
||||
" neutral_neighbors = self.get_neighboring_agents(state_id=0)\n",
|
||||
" for neighbor in neutral_neighbors:\n",
|
||||
" if random.random() < self.prob_cured_vaccinate_neutral:\n",
|
||||
" neighbor.state['id'] = 3 # Vaccinated\n",
|
||||
"\n",
|
||||
" # Cure\n",
|
||||
" infected_neighbors = self.get_neighboring_agents(state_id=1)\n",
|
||||
" for neighbor in infected_neighbors:\n",
|
||||
" if random.random() < self.prob_cured_healing_infected:\n",
|
||||
" neighbor.state['id'] = 2 # Cured\n",
|
||||
"\n",
|
||||
" def vaccinated_behaviour(self):\n",
|
||||
"\n",
|
||||
" # Cure\n",
|
||||
" infected_neighbors = self.get_neighboring_agents(state_id=1)\n",
|
||||
" for neighbor in infected_neighbors:\n",
|
||||
" if random.random() < self.prob_cured_healing_infected:\n",
|
||||
" neighbor.state['id'] = 2 # Cured\n",
|
||||
"\n",
|
||||
" # Vaccinate\n",
|
||||
" neutral_neighbors = self.get_neighboring_agents(state_id=0)\n",
|
||||
" for neighbor in neutral_neighbors:\n",
|
||||
" if random.random() < self.prob_cured_vaccinate_neutral:\n",
|
||||
" neighbor.state['id'] = 3 # Vaccinated\n",
|
||||
"\n",
|
||||
" # Generate anti-rumor\n",
|
||||
" infected_neighbors_2 = self.get_neighboring_agents(state_id=1)\n",
|
||||
" for neighbor in infected_neighbors_2:\n",
|
||||
" if random.random() < self.prob_generate_anti_rumor:\n",
|
||||
" neighbor.state['id'] = 2 # Cured\n",
|
||||
"\n",
|
||||
" def beacon_off_behaviour(self):\n",
|
||||
" infected_neighbors = self.get_neighboring_agents(state_id=1)\n",
|
||||
" if len(infected_neighbors) > 0:\n",
|
||||
" self.state['id'] == 5 # Beacon on\n",
|
||||
"\n",
|
||||
" def beacon_on_behaviour(self):\n",
|
||||
"\n",
|
||||
" # Cure (M2 feature added)\n",
|
||||
" infected_neighbors = self.get_neighboring_agents(state_id=1)\n",
|
||||
" for neighbor in infected_neighbors:\n",
|
||||
" if random.random() < self.prob_generate_anti_rumor:\n",
|
||||
" neighbor.state['id'] = 2 # Cured\n",
|
||||
" neutral_neighbors_infected = neighbor.get_neighboring_agents(state_id=0)\n",
|
||||
" for neighbor in neutral_neighbors_infected:\n",
|
||||
" if random.random() < self.prob_generate_anti_rumor:\n",
|
||||
" neighbor.state['id'] = 3 # Vaccinated\n",
|
||||
" infected_neighbors_infected = neighbor.get_neighboring_agents(state_id=1)\n",
|
||||
" for neighbor in infected_neighbors_infected:\n",
|
||||
" if random.random() < self.prob_generate_anti_rumor:\n",
|
||||
" neighbor.state['id'] = 2 # Cured\n",
|
||||
"\n",
|
||||
" # Vaccinate\n",
|
||||
" neutral_neighbors = self.get_neighboring_agents(state_id=0)\n",
|
||||
" for neighbor in neutral_neighbors:\n",
|
||||
" if random.random() < self.prob_cured_vaccinate_neutral:\n",
|
||||
" neighbor.state['id'] = 3 # Vaccinated"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Settings.json"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This file contains all the variables that can be modified from the simulation. In case of implementing a new spread model, the new variables should be also included in this file."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"[\n",
|
||||
" {\n",
|
||||
" \"network_type\": 1,\n",
|
||||
" \"number_of_nodes\": 1000,\n",
|
||||
" \"max_time\": 50,\n",
|
||||
" \"num_trials\": 1,\n",
|
||||
" \"timeout\": 2\n",
|
||||
" },\n",
|
||||
"\n",
|
||||
" {\n",
|
||||
" \"agent\": [\"BaseBehaviour\",\"SISaModel\",\"ControlModelM2\"],\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" \"bite_prob\": 0.01,\n",
|
||||
" \"heal_prob\": 0.01,\n",
|
||||
"\n",
|
||||
" \"innovation_prob\": 0.001,\n",
|
||||
" \"imitation_prob\": 0.005,\n",
|
||||
"\n",
|
||||
" \"outside_effects_prob\": 0.2,\n",
|
||||
" \"anger_prob\": 0.06,\n",
|
||||
" \"joy_prob\": 0.05,\n",
|
||||
" \"sadness_prob\": 0.02,\n",
|
||||
" \"disgust_prob\": 0.02,\n",
|
||||
"\n",
|
||||
" \"enterprises\": [\"BBVA\", \"Santander\", \"Bankia\"],\n",
|
||||
"\n",
|
||||
" \"tweet_probability_users\": 0.44,\n",
|
||||
" \"tweet_relevant_probability\": 0.25,\n",
|
||||
" \"tweet_probability_about\": [0.15, 0.15, 0.15],\n",
|
||||
" \"sentiment_about\": [0, 0, 0],\n",
|
||||
"\n",
|
||||
" \"tweet_probability_enterprises\": [0.3, 0.3, 0.3],\n",
|
||||
"\n",
|
||||
" \"neutral_discontent_spon_prob\": 0.04,\n",
|
||||
" \"neutral_discontent_infected_prob\": 0.04,\n",
|
||||
" \"neutral_content_spon_prob\": 0.18,\n",
|
||||
" \"neutral_content_infected_prob\": 0.02,\n",
|
||||
"\n",
|
||||
" \"discontent_neutral\": 0.13,\n",
|
||||
" \"discontent_content\": 0.07,\n",
|
||||
" \"variance_d_c\": 0.02,\n",
|
||||
"\n",
|
||||
" \"content_discontent\": 0.009,\n",
|
||||
" \"variance_c_d\": 0.003,\n",
|
||||
" \"content_neutral\": 0.088,\n",
|
||||
"\n",
|
||||
" \"standard_variance\": 0.055,\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" \"prob_neutral_making_denier\": 0.035,\n",
|
||||
"\n",
|
||||
" \"prob_infect\": 0.075,\n",
|
||||
"\n",
|
||||
" \"prob_cured_healing_infected\": 0.035,\n",
|
||||
" \"prob_cured_vaccinate_neutral\": 0.035,\n",
|
||||
"\n",
|
||||
" \"prob_vaccinated_healing_infected\": 0.035,\n",
|
||||
" \"prob_vaccinated_vaccinate_neutral\": 0.035,\n",
|
||||
" \"prob_generate_anti_rumor\": 0.035\n",
|
||||
" }\n",
|
||||
"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Model Library"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To test this simulator in all the experiments we have used the Albert\n",
|
||||
"Barabasi Graph [34] to automatically generate the network and the con-\n",
|
||||
"nections among the agents due it is one of the most suitable graphs to\n",
|
||||
"recreate social networks.\n",
|
||||
"\n",
|
||||
"Using different human behaviour models we will recreate the different\n",
|
||||
"decisions of each agent.\n",
|
||||
"\n",
|
||||
"Moreover there are some parameters regarding the basic simulation that\n",
|
||||
"have to be settled. In addition, more parameters will be needed depend-\n",
|
||||
"ing on the spread model used for the experiment."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Spread Model M2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This model is based on the New Spread Model\n",
|
||||
"M2 [1] which also refers to the cascade model [2]. Agents, usually Twit-\n",
|
||||
"ter users, have four states regarding a rumour: neutral (initial state),\n",
|
||||
"infected, vaccinated and cured.\n",
|
||||
"\n",
|
||||
"An agent becomes: infected when believes the rumour; vaccinated when is\n",
|
||||
"influenced before being infected by a cured or already vaccinated agent\n",
|
||||
"and cured when after becoming infected the agent is influenced by a\n",
|
||||
"vaccinated/cured user.\n",
|
||||
"\n",
|
||||
"After a certain period of time, a random infected user develops an anti-\n",
|
||||
"rumour and spreads it to its neighbours in order to vaccinate the neutral\n",
|
||||
"and cure the infected ones.\n",
|
||||
"\n",
|
||||
"This model includes the fact that infected users who made a mistake\n",
|
||||
"believing in the rumour will not be in favour of spreading theirs mistakes\n",
|
||||
"through the network. Therefore, only vaccinated users will spread anti-\n",
|
||||
"rumours. The probability of making a denier and becoming vaccinated\n",
|
||||
"when a neutral user has an infected neighbour and the first already had\n",
|
||||
"information about the rumour being false.\n",
|
||||
"\n",
|
||||
"* [1] E. Serrano and C. A. Iglesias. “Validating viral marketing\n",
|
||||
"strategies in Twitter via agent-based social simulation”. In:\n",
|
||||
"Expert Systems with Applications 50.1 (2016),\n",
|
||||
"* [2] L. Weng et al. “Virality prediction and community structure\n",
|
||||
"in social networks”. In: Scientific Reports 3 (2013)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Control model M2,2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This model is based on the New Control Model\n",
|
||||
"M2,2 [1]. It includes the use of beacons, special agents, that represent\n",
|
||||
"an authority which can work against the rumour once it is detected. It\n",
|
||||
"only has two states: on or off. Beacons will switch to on status when they\n",
|
||||
"detect the misinformation in an infected neighbour agent.\n",
|
||||
"Once the beacon is activated, they will try to cure and vaccinate other\n",
|
||||
"agents starting a anti-rumour. Therefore this model also takes into ac-\n",
|
||||
"count that infected users might not admit a previous mistake.\n",
|
||||
"\n",
|
||||
"* [1] E. Serrano and C. A. Iglesias. “Validating viral marketing\n",
|
||||
"strategies in Twitter via agent-based social simulation”. In:\n",
|
||||
"Expert Systems with Applications 50.1 (2016),"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### SISa Model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The SISa model of infection is already included in the simulator. Its the evolution of the classic disease spread Susceptible-Infective-Susceptible (SIS) model [1, 2].\n",
|
||||
"\n",
|
||||
"The SISa model is proposed by [3] and the main new feature is considering the spontaneous generation process of sentiment. This model has two assumptions: first, a susceptible agent who is close and more exposed to the infected has a higher probability of infection that other agent; second, the number of infected agents does not affect the probability of recovery.\n",
|
||||
"\n",
|
||||
"Based on some recent implementations of the SISa model [3], every agent can be in three states: neutral (initial), content and discontent.\n",
|
||||
"\n",
|
||||
"All the transitions between every different state are allowed depending on customizable probabilities. This model includes the fact that an agent will be more likely to change state as the number of neighbours with this state increases.\n",
|
||||
"\n",
|
||||
"* [1] P. Weng and X.-Q. Zhao. “Spreading speed and traveling waves for a multi-type SIS epidemic model”. In: Journal of Differential Equations 229.1 (2006)\n",
|
||||
"\n",
|
||||
"* [2] P. V. Mieghem. “Epidemic phase transition of the SIS type in networks”. In: A Letters Journal Exploring the Frontiers of Physics 97.4 (2012).\n",
|
||||
"* [3] A. L. Hill et al. “Emotions as infectious diseases in a large social network: the SISa model”. In: Proceedings of the Royal Society of London B: Biological Sciences 277.1701 (2010),"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Big Market Model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"As stated in several papers [2–4], social networks like Twitter are the perfect scenario to study the propagation of ideas, sentiments and marketing strategies. In this scenario several enterprises want to take advantage of social networks to promote their companies and connect with their clients.\n",
|
||||
"\n",
|
||||
"The goal of this model [1] is to recreate the behaviour of several enterprises in a social network. Following the example of HashtKat, we want to measure the effect of different marketing strategies in social networks.\n",
|
||||
"Depending on the sentiment towards an enterprise the user will post positive or negative tweets about these enterprises. The fact that an user can increase its probabilities of posting a relevant tweet about a certain\n",
|
||||
"company depending on its sentiment towards it is also considered.\n",
|
||||
"In this model the number of enterprises as well as tweet rate probabilities of both companies and users can be changed.\n",
|
||||
"\n",
|
||||
"* [1] E. Serrano and C. A. Iglesias. “Validating viral marketing\n",
|
||||
"strategies in Twitter via agent-based social simulation”. In:\n",
|
||||
"Expert Systems with Applications 50.1 (2016)\n",
|
||||
"* [2] B. A. Huberman et al. “Social Networks that Matter: Twitter\n",
|
||||
"Under the Microscope”. In: Social Science Research Network\n",
|
||||
"(2008).\n",
|
||||
"* [3] M. Cha et al. “Measuring User Influence in Twitter: The\n",
|
||||
"Million Follower Fallacy.” In: ICWSM 10.10-17 (2010),\n",
|
||||
"* [4] M. Bulearca and S. Bulearca. “Twitter: a viable marketing\n",
|
||||
"tool for SMEs?” In: Global business and management research\n",
|
||||
"2.4 (2010),"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Sentiment Correlation Model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"With this model we want to study\n",
|
||||
"the influence of different sentiments in a social network. In order to do so, we base our model on the research made by [1]. In this paper the authors found out that in a social network (in this case Weibo) the correlation\n",
|
||||
"of anger is significantly higher than joy and sadness meaning that the anger sentiment would occasionally spread faster than the others.\n",
|
||||
"\n",
|
||||
"They also confirmed some intuitive ideas such as a pair of users who have higher interactions are more likely to be influenced by each other, and that users with more friends would influence their neighbours more than other agents.\n",
|
||||
"\n",
|
||||
"In this simulation we have four emotions: anger, joy, sadness and disgust.\n",
|
||||
"\n",
|
||||
"Using the probabilities extracted from the dataset used by [1] we can visualise the graph and confirm the conclusions of the paper. Anger sentiment propagation rate is much higher than any other. Joy sentiment also spreads easily to the neighbours. However, sadness and disgust propagation rate is really small, few neighbours get affected by them.\n",
|
||||
"\n",
|
||||
"* [1] R. Fan et al. “Anger is More Influential Than Joy: Sentiment\n",
|
||||
"Correlation in Weibo”. In: CoRR abs/1309.2402 (2013)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Bass Model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Even though Bass Model can be applied to many appli-\n",
|
||||
"cations [57–60] it can be used to study the diffusion of information as\n",
|
||||
"well.\n",
|
||||
"This model is based on the implementation proposed by Rand and Wilen-\n",
|
||||
"sky [13]. In this scenario there are only two states: unaware (initial) and\n",
|
||||
"aware. For this simulation we assume that agents can only change status\n",
|
||||
"from advertising (outside effects) and word of mouth (information inside\n",
|
||||
"the network).\n",
|
||||
"The probability of being affected by imitation (word of mouth effect)\n",
|
||||
"increases as a function of the agent aware neighbours. In this model once\n",
|
||||
"the user changes to aware status it remains in this state for the whole\n",
|
||||
"simulation.\n",
|
||||
"\n",
|
||||
"* F. M. Bass. “A New Product Growth for Model ConsumerDurables”. In: Management Science 15.5 (1969),\n",
|
||||
"W. Dodds. “An Application of the Bass Model in Long-TermNew Product Forecasting”. In: Journal of Marketing Research\n",
|
||||
"10.3 (1973),\n",
|
||||
"* F. Douglas Tigert. “The Bass New Product Growth Model: A Sensitivity Analysis for a High Technology Product”. In: Journal of Marketing 45.4 (1981),\n",
|
||||
"* Z. Jiang et al. “Virtual Bass Model and the left-hand data-truncation bias in diffusion of innovation studies”. In: International Journal of Research in Marketing 23.1 (2006), "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Independent Cascade Model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"As stated by Rand and Wilensky [1], the Independent Cascade Model [61] suits better the case we want to\n",
|
||||
"study as it is more appropriate for social networks than the Bass Model.\n",
|
||||
"\n",
|
||||
"In this scenario we also have two states: unaware (initial) and aware. The new feature in this model is that one agent will only get infected once at least one neighbour became aware the previous time step. There is also\n",
|
||||
"a probability of becoming aware by outside effects (innovation).\n",
|
||||
"\n",
|
||||
"This new feature can be explained intuitively, one agent will have more influence on another if the first just infected and wants to spread the new information he acquired.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"* [1] W. Rand and U. Wilensky. An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo. MIT Press, 2015.\n",
|
||||
"* [2] J. Goldenberg et al. “Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth”. In: Marketing Letters 12.3 (2001),\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Copyright"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"SOIL has been developed by the Intelligent Systems Group, Universidad Politécnica de Madrid, 2016-2017.\n",
|
||||
"\n",
|
||||
"@Copyright Universidad Politécnica de Madrid, 2016-2017\n",
|
||||
" <img src=\"./logo_gsi.png\" alt=\"Grupo de Sistemas Inteligentes\" width=\"100px\">"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
@ -1,225 +0,0 @@
|
||||
from unittest import TestCase
|
||||
|
||||
import os
|
||||
import yaml
|
||||
from functools import partial
|
||||
|
||||
from os.path import join
|
||||
from soil import simulation, agents, utils
|
||||
|
||||
|
||||
ROOT = os.path.abspath(os.path.dirname(__file__))
|
||||
|
||||
EXAMPLES = join(ROOT, '..', 'examples')
|
||||
|
||||
|
||||
class TestMain(TestCase):
|
||||
|
||||
def test_load_graph(self):
|
||||
"""
|
||||
Load a graph from file if the extension is known.
|
||||
Raise an exception otherwise.
|
||||
"""
|
||||
config = {
|
||||
'network_params': {
|
||||
'path': join(ROOT, 'test.gexf')
|
||||
}
|
||||
}
|
||||
G = utils.load_network(config['network_params'])
|
||||
assert G
|
||||
assert len(G) == 2
|
||||
with self.assertRaises(AttributeError):
|
||||
config = {
|
||||
'network_params': {
|
||||
'path': join(ROOT, 'unknown.extension')
|
||||
}
|
||||
}
|
||||
G = utils.load_network(config['network_params'])
|
||||
print(G)
|
||||
|
||||
def test_generate_barabasi(self):
|
||||
"""
|
||||
If no path is given, a generator and network parameters
|
||||
should be used to generate a network
|
||||
"""
|
||||
config = {
|
||||
'network_params': {
|
||||
'generator': 'barabasi_albert_graph'
|
||||
}
|
||||
}
|
||||
with self.assertRaises(TypeError):
|
||||
G = utils.load_network(config['network_params'])
|
||||
config['network_params']['n'] = 100
|
||||
config['network_params']['m'] = 10
|
||||
G = utils.load_network(config['network_params'])
|
||||
assert len(G) == 100
|
||||
|
||||
def test_empty_simulation(self):
|
||||
"""A simulation with a base behaviour should do nothing"""
|
||||
config = {
|
||||
'network_params': {
|
||||
'path': join(ROOT, 'test.gexf')
|
||||
},
|
||||
'agent_type': 'NetworkAgent',
|
||||
'environment_params': {
|
||||
}
|
||||
}
|
||||
s = simulation.from_config(config)
|
||||
s.run_simulation()
|
||||
|
||||
def test_counter_agent(self):
|
||||
"""
|
||||
The initial states should be applied to the agent and the
|
||||
agent should be able to update its state."""
|
||||
config = {
|
||||
'network_params': {
|
||||
'path': join(ROOT, 'test.gexf')
|
||||
},
|
||||
'agent_type': 'CounterModel',
|
||||
'states': [{'neighbors': 10}, {'total': 12}],
|
||||
'max_time': 2,
|
||||
'num_trials': 1,
|
||||
'environment_params': {
|
||||
}
|
||||
}
|
||||
s = simulation.from_config(config)
|
||||
env = s.run_simulation()[0]
|
||||
assert env.get_agent(0)['neighbors', 0] == 10
|
||||
assert env.get_agent(0)['neighbors', 1] == 1
|
||||
assert env.get_agent(1)['total', 0] == 12
|
||||
assert env.get_agent(1)['neighbors', 1] == 1
|
||||
|
||||
def test_counter_agent_history(self):
|
||||
"""
|
||||
The evolution of the state should be recorded in the logging agent
|
||||
"""
|
||||
config = {
|
||||
'network_params': {
|
||||
'path': join(ROOT, 'test.gexf')
|
||||
},
|
||||
'network_agents': [{
|
||||
'agent_type': 'AggregatedCounter',
|
||||
'weight': 1,
|
||||
'state': {'id': 0}
|
||||
|
||||
}],
|
||||
'max_time': 10,
|
||||
'environment_params': {
|
||||
}
|
||||
}
|
||||
s = simulation.from_config(config)
|
||||
env = s.run_simulation()[0]
|
||||
for agent in env.network_agents:
|
||||
last = 0
|
||||
assert len(agent._history) == 11
|
||||
for step, total in agent['total', None].items():
|
||||
if step > 0:
|
||||
assert total == last + 2
|
||||
last = total
|
||||
|
||||
def test_custom_agent(self):
|
||||
"""Allow for search of neighbors with a certain state_id"""
|
||||
class CustomAgent(agents.NetworkAgent):
|
||||
def step(self):
|
||||
self.state['neighbors'] = self.count_agents(state_id=0,
|
||||
limit_neighbors=True)
|
||||
config = {
|
||||
'network_params': {
|
||||
'path': join(ROOT, 'test.gexf')
|
||||
},
|
||||
'network_agents': [{
|
||||
'agent_type': CustomAgent,
|
||||
'weight': 1,
|
||||
'state': {'id': 0}
|
||||
|
||||
}],
|
||||
'max_time': 10,
|
||||
'environment_params': {
|
||||
}
|
||||
}
|
||||
s = simulation.from_config(config)
|
||||
env = s.run_simulation()[0]
|
||||
assert env.get_agent(0).state['neighbors'] == 1
|
||||
|
||||
def test_torvalds_example(self):
|
||||
"""A complete example from a documentation should work."""
|
||||
config = utils.load_file(join(EXAMPLES, 'torvalds.yml'))[0]
|
||||
config['network_params']['path'] = join(EXAMPLES,
|
||||
config['network_params']['path'])
|
||||
s = simulation.from_config(config)
|
||||
env = s.run_simulation()[0]
|
||||
for a in env.network_agents:
|
||||
skill_level = a.state['skill_level']
|
||||
if a.id == 'Torvalds':
|
||||
assert skill_level == 'God'
|
||||
assert a.state['total'] == 3
|
||||
assert a.state['neighbors'] == 2
|
||||
elif a.id == 'balkian':
|
||||
assert skill_level == 'developer'
|
||||
assert a.state['total'] == 3
|
||||
assert a.state['neighbors'] == 1
|
||||
else:
|
||||
assert skill_level == 'beginner'
|
||||
assert a.state['total'] == 3
|
||||
assert a.state['neighbors'] == 1
|
||||
|
||||
def test_yaml(self):
|
||||
"""
|
||||
The YAML version of a newly created simulation
|
||||
should be equivalent to the configuration file used
|
||||
"""
|
||||
with utils.timer('loading'):
|
||||
config = utils.load_file(join(EXAMPLES, 'complete.yml'))[0]
|
||||
s = simulation.from_config(config)
|
||||
with utils.timer('serializing'):
|
||||
serial = s.to_yaml()
|
||||
with utils.timer('recovering'):
|
||||
recovered = yaml.load(serial)
|
||||
with utils.timer('deleting'):
|
||||
del recovered['topology']
|
||||
assert config == recovered
|
||||
|
||||
def test_configuration_changes(self):
|
||||
"""
|
||||
The configuration should not change after running
|
||||
the simulation.
|
||||
"""
|
||||
config = utils.load_file('examples/complete.yml')[0]
|
||||
s = simulation.from_config(config)
|
||||
for i in range(5):
|
||||
s.run_simulation()
|
||||
nconfig = s.to_dict()
|
||||
del nconfig['topology']
|
||||
assert config == nconfig
|
||||
|
||||
def test_examples(self):
|
||||
"""
|
||||
Make sure all examples in the examples folder are correct
|
||||
"""
|
||||
|
||||
|
||||
def make_example_test(path, config):
|
||||
def wrapped(self):
|
||||
root = os.getcwd()
|
||||
os.chdir(os.path.dirname(path))
|
||||
s = simulation.from_config(config)
|
||||
envs = s.run_simulation()
|
||||
for env in envs:
|
||||
n = config['network_params'].get('n', None)
|
||||
if n is not None:
|
||||
assert len(env.get_agents()) == n
|
||||
os.chdir(root)
|
||||
return wrapped
|
||||
|
||||
|
||||
def add_example_tests():
|
||||
for config, path in utils.load_config(join(EXAMPLES, '*.yml')):
|
||||
p = make_example_test(path=path, config=config)
|
||||
fname = os.path.basename(path)
|
||||
p.__name__ = 'test_example_file_%s' % fname
|
||||
p.__doc__ = '%s should be a valid configuration' % fname
|
||||
setattr(TestMain, p.__name__, p)
|
||||
del p
|
||||
|
||||
|
||||
add_example_tests()
|
||||
@ -1 +0,0 @@
|
||||
This is an unknown file
|
||||
Loading…
Reference in New Issue
Block a user