mirror of
https://github.com/gsi-upm/soil
synced 2024-11-13 23:12:28 +00:00
112 lines
3.8 KiB
ReStructuredText
112 lines
3.8 KiB
ReStructuredText
Developing new models
|
|
---------------------
|
|
This document describes how to develop a new analysis model.
|
|
|
|
What is a model?
|
|
================
|
|
|
|
A model defines the behaviour of the agents with a view to assessing their effects on the system as a whole.
|
|
In practice, a model consists of at least two parts:
|
|
|
|
* Python module: the actual code that describes the behaviour.
|
|
* Setting up the variables in the Settings JSON file.
|
|
|
|
This separation allows us to run the simulation with different agents.
|
|
|
|
Models Code
|
|
===========
|
|
|
|
All the models are imported to the main file. The initialization look like this:
|
|
|
|
.. code:: python
|
|
|
|
import settings
|
|
|
|
networkStatus = {} # Dict that will contain the status of every agent in the network
|
|
|
|
sentimentCorrelationNodeArray = []
|
|
for x in range(0, settings.network_params["number_of_nodes"]):
|
|
sentimentCorrelationNodeArray.append({'id': x})
|
|
# Initialize agent states. Let's assume everyone is normal.
|
|
init_states = [{'id': 0, } for _ in range(settings.network_params["number_of_nodes"])]
|
|
# add keys as as necessary, but "id" must always refer to that state category
|
|
|
|
A new model have to inherit the BaseBehaviour class which is in the same module.
|
|
There are two basics methods:
|
|
|
|
* __init__
|
|
* step: used to define the behaviour over time.
|
|
|
|
Variable Initialization
|
|
=======================
|
|
|
|
The different parameters of the model have to be initialize in the Simulation Settings JSON file which will be
|
|
passed as a parameter to the simulation.
|
|
|
|
.. code:: json
|
|
|
|
{
|
|
"agent": ["SISaModel","ControlModelM2"],
|
|
|
|
"neutral_discontent_spon_prob": 0.04,
|
|
"neutral_discontent_infected_prob": 0.04,
|
|
"neutral_content_spon_prob": 0.18,
|
|
"neutral_content_infected_prob": 0.02,
|
|
|
|
"discontent_neutral": 0.13,
|
|
"discontent_content": 0.07,
|
|
"variance_d_c": 0.02,
|
|
|
|
"content_discontent": 0.009,
|
|
"variance_c_d": 0.003,
|
|
"content_neutral": 0.088,
|
|
|
|
"standard_variance": 0.055,
|
|
|
|
|
|
"prob_neutral_making_denier": 0.035,
|
|
|
|
"prob_infect": 0.075,
|
|
|
|
"prob_cured_healing_infected": 0.035,
|
|
"prob_cured_vaccinate_neutral": 0.035,
|
|
|
|
"prob_vaccinated_healing_infected": 0.035,
|
|
"prob_vaccinated_vaccinate_neutral": 0.035,
|
|
"prob_generate_anti_rumor": 0.035
|
|
}
|
|
|
|
In this file you will also define the models you are going to simulate. You can simulate as many models as you want.
|
|
The simulation returns one result for each model, executing each model separately. For the usage, see :doc:`usage`.
|
|
|
|
Example Model
|
|
=============
|
|
|
|
In this section, we will implement a Sentiment Correlation Model.
|
|
|
|
The class would look like this:
|
|
|
|
.. code:: python
|
|
|
|
from ..BaseBehaviour import *
|
|
from .. import sentimentCorrelationNodeArray
|
|
|
|
class SentimentCorrelationModel(BaseBehaviour):
|
|
|
|
def __init__(self, environment=None, agent_id=0, state=()):
|
|
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
|
self.outside_effects_prob = environment.environment_params['outside_effects_prob']
|
|
self.anger_prob = environment.environment_params['anger_prob']
|
|
self.joy_prob = environment.environment_params['joy_prob']
|
|
self.sadness_prob = environment.environment_params['sadness_prob']
|
|
self.disgust_prob = environment.environment_params['disgust_prob']
|
|
self.time_awareness = []
|
|
for i in range(4): # In this model we have 4 sentiments
|
|
self.time_awareness.append(0) # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
|
|
sentimentCorrelationNodeArray[self.id][self.env.now] = 0
|
|
|
|
def step(self, now):
|
|
self.behaviour() # Method which define the behaviour
|
|
super().step(now)
|
|
|
|
The variables will be modified by the user, so you have to include them in the Simulation Settings JSON file. |