soil/docs/configuration.rst

Configuring a simulation
------------------------

There are two ways to configure a simulation: programmatically and with a configuration file.
In both cases, the parameters used are the same.
The advantage of a configuration file is that it is a clean declarative description, and it makes it easier to reproduce.

Simulation configuration files can be formatted in ``json`` or ``yaml`` and they define all the parameters of a simulation.
Here's an example (``example.yml``).

.. code:: yaml
          
    ---
    name: MyExampleSimulation
    max_time: 50
    num_trials: 3
    interval: 2
    network_params:
        generator: barabasi_albert_graph
        n: 100
        m: 2
    network_agents:
        - agent_type: SISaModel
           weight: 1
            state:
            id: content
        - agent_type: SISaModel
            weight: 1
            state:
            id: discontent
        - agent_type: SISaModel
            weight: 8
            state:
            id: neutral
    environment_params:
        prob_infect: 0.075


This example configuration will run three trials (``num_trials``) of a simulation containing a randomly generated network (``network_params``).
The 100 nodes in the network will be SISaModel agents (``network_agents.agent_type``), which is an agent behavior that is included in Soil.
10% of the agents (``weight=1``) will start in the content state, 10% in the discontent state, and the remaining 80% (``weight=8``) in the neutral state.
All agents will have access to the environment (``environment_params``), which only contains one variable, ``prob_infected``.
The state of the agents will be updated every 2 seconds (``interval``).

Now run the simulation with the command line tool:

.. code:: bash

   soil example.yml

Once the simulation finishes, its results will be stored in a folder named ``MyExampleSimulation``.
Three types of objects are saved by default: a pickle of the simulation; a ``YAML`` representation of the simulation (which can be used to re-launch it); and for every trial, a ``sqlite`` file with the content of the state of every network node and the environment parameters at every step of the simulation.


.. code::

    soil_output
    └── MyExampleSimulation
        ├── MyExampleSimulation.dumped.yml
        ├── MyExampleSimulation.simulation.pickle
        ├── MyExampleSimulation_trial_0.db.sqlite
        ├── MyExampleSimulation_trial_1.db.sqlite
        └── MyExampleSimulation_trial_2.db.sqlite


You may also ask soil to export the states in a ``csv`` file, and the network in gephi format (``gexf``).

Network
=======

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.

Loading a network
#################

To load an existing network, specify its path in the configuration:

.. code:: yaml

   ---
   network_params:
      path: /tmp/mynetwork.gexf

Soil will try to guess what networkx method to use to read the file based on its extension.
However, we only test using ``gexf`` files.

For simple networks, you may also include them in the configuration itself using , using the ``topology`` parameter like so:

.. code:: yaml

   ---
   topology:
       nodes:
          - id: First
          - id: Second
       links:
          - source: First
            target: Second


Generating a random network
###########################

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(n=100)`:

.. code:: yaml

    network_params:
        generator: complete_graph
        n: 100

Environment
============
The environment is the place where the shared state of the simulation is stored.
For instance, the probability of disease outbreak.
The configuration file may specify the initial value of the environment parameters:

.. code:: yaml

    environment_params:
        daily_probability_of_earthquake: 0.001
        number_of_earthquakes: 0

All agents have access to the environment parameters.

In some scenarios, it is useful to have a custom environment, to provide additional methods or to control the way agents update environment state.
For example, if our agents play the lottery, the environment could provide a method to decide whether the agent wins, instead of leaving it to the agent.


Agents
======
Agents are a way of modelling behavior.
Agents can be characterized with two variables: agent type (``agent_type``) and state.
Only one agent is executed at a time (generally, every ``interval`` 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 and environment agent.

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 be associated to an 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 (using the ``weight`` property).
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

    network_agents:
          - agent_type: SISaModel
            weight: 1
          - agent_type: CounterModel
            weight: 5

The third option is to specify the type of agent on the node itself, e.g.:


.. code:: yaml

   topology:
       nodes:
           - id: first
   agent_type: BaseAgent
   states:
       first:
         agent_type: SISaModel
          

This would also work with a randomly generated network:


.. code:: yaml

   network:
       generator: complete
       n: 5
   agent_type: BaseAgent
   states:
       - agent_type: SISaModel

              

In addition to agent type, you may 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

    network_agents:
        - 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:
      generator: 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 agents 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


You may use environment agents to model events that a normal agent cannot control, such as natural disasters or chance.
They are also useful to add behavior that has little to do with the network and the interactions within that network.