1
0
mirror of https://github.com/gsi-upm/soil synced 2025-10-20 10:18:29 +00:00

Compare commits

..

23 Commits

Author SHA1 Message Date
J. Fernando Sánchez
a3ea434f23 0.13.8 2019-02-19 21:17:19 +01:00
J. Fernando Sánchez
65f6aa72f3 fix timeout in FSM. Improve logs 2019-02-01 19:05:07 +01:00
J. Fernando Sánchez
09e14c6e84 Add generator and programmatic examples 2018-12-20 19:25:33 +01:00
J. Fernando Sánchez
8593ac999d Swap test and build in CI. Remove tests in tags 2018-12-20 17:56:33 +01:00
J. Fernando Sánchez
90338c3549 skip-tls-verify in kaniko 2018-12-20 17:48:58 +01:00
J. Fernando Sánchez
1d532dacfe Remove entrypoint build stage 2018-12-20 15:14:58 +01:00
J. Fernando Sánchez
a1f8d8c9c5 Change entrypoint build stage 2018-12-20 15:07:45 +01:00
J. Fernando Sánchez
de326eb331 Remove CI global image 2018-12-20 15:05:45 +01:00
J. Fernando Sánchez
04b4380c61 Fix wrong import soil.web 2018-12-20 14:06:18 +01:00
J. Fernando Sánchez
d70a0c865c limit ci jobs to docker runners 2018-12-09 17:22:40 +01:00
J. Fernando Sánchez
625c28e4ee Fix CI syntax 2018-12-09 17:09:31 +01:00
J. Fernando Sánchez
9749f4ca14 Fix multithreading
Multithreading needs pickling to work.
Pickling/unpickling didn't work in some situations, like when the
environment_agents parameter was left blank.
This was due to two reasons:

1) agents and history didn't have a setstate method, and some of their
attributes cannot be pickled (generators, sqlite connection)
2) the environment was adding generators (agents) to its state.

This fixes the situation by restricting the keys that the environment exports
when it pickles, and by adding the set/getstate methods in agents.

The resulting pickles should contain enough information to inspect
them (history, state values, etc), but very limited.
2018-12-09 16:58:49 +01:00
J. Fernando Sánchez
3526fa29d7 Fix bug parallel 2018-12-09 14:06:50 +01:00
J. Fernando Sánchez
53604c1e66 Fix quickstart.rst markdown code 2018-12-09 13:10:00 +01:00
J. Fernando Sánchez
01cc8e9128 Merge branch 'refactor-imports'
* remove leftover import in example
* Update quickstart tutorial
* Add gitlab-ci
* Added missing gexf for tests
* Upgrade to python3.7 and pandas 0.3.4 because pandas has dropped support for
  python 3.4 -> There are some API changes in pandas, and I've updated the code
  accordingly.
* Set pytest as the default test runner
* Update dockerignore
* Skip testing long examples (>1000 steps)
2018-12-09 12:55:12 +01:00
J. Fernando Sánchez
a47ffa815b Fix CI. Skip testing long examples 2018-12-08 20:49:34 +01:00
J. Fernando Sánchez
b41927d7bf remove leftover import in example 2018-12-08 20:35:02 +01:00
J. Fernando Sánchez
70d033b3a9 Update dockerignore 2018-12-08 19:13:56 +01:00
J. Fernando Sánchez
3afed06656 Add gitlab-ci 2018-12-08 19:08:47 +01:00
J. Fernando Sánchez
0a7ef27844 Added missing gexf for tests 2018-12-08 18:53:12 +01:00
J. Fernando Sánchez
2e28b36f6e Python3.7, testing and bug fixes
* Upgrade to python3.7 and pandas 0.3.4 because pandas has dropped support for
python 3.4 -> There are some API changes in pandas, and I've update the code
accordingly.
* Set pytest as the default test runner
2018-12-08 18:53:06 +01:00
J. Fernando Sánchez
bd4700567e Update quickstart tutorial 2018-12-08 18:17:25 +01:00
J. Fernando Sánchez
ff1df62eec All tests pass 2018-12-08 18:17:21 +01:00
50 changed files with 82833 additions and 777 deletions

2
.dockerignore Normal file
View File

@@ -0,0 +1,2 @@
**/soil_output
.*

28
.gitlab-ci.yml Normal file
View File

@@ -0,0 +1,28 @@
stages:
- test
- build
build:
stage: build
image:
name: gcr.io/kaniko-project/executor:debug
entrypoint: [""]
tags:
- docker
script:
- echo "{\"auths\":{\"$CI_REGISTRY\":{\"username\":\"$CI_REGISTRY_USER\",\"password\":\"$CI_REGISTRY_PASSWORD\"}}}" > /kaniko/.docker/config.json
# The skip-tls-verify flag is there because our registry certificate is self signed
- /kaniko/executor --context $CI_PROJECT_DIR --skip-tls-verify --dockerfile $CI_PROJECT_DIR/Dockerfile --destination $CI_REGISTRY_IMAGE:$CI_COMMIT_TAG
only:
- tags
test:
except:
- tags # Avoid running tests for tags, because they are already run for the branch
tags:
- docker
image: python:3.7
stage: test
script:
- python setup.py test

27
CHANGELOG.md Normal file
View File

@@ -0,0 +1,27 @@
# Changelog
All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [0.13.8]
### Changed
* Moved TerroristNetworkModel to examples
### Added
* `get_agents` and `count_agents` methods now accept lists as inputs. They can be used to retrieve agents from node ids
* `subgraph` in BaseAgent
* `agents.select` method, to filter out agents
* `skip_test` property in yaml definitions, to force skipping some examples
* `agents.Geo`, with a search function based on postition
* `BaseAgent.ego_search` to get nodes from the ego network of a node
* `BaseAgent.degree` and `BaseAgent.betweenness`
### Fixed
## [0.13.7]
### Changed
* History now defaults to not backing up! This makes it more intuitive to load the history for examination, at the expense of rewriting something. That should not happen because History is only created in the Environment, and that has `backup=True`.
### Added
* Agent names are assigned based on their agent types
* Agent logging uses the agent name.
* FSM agents can now return a timeout in addition to a new state. e.g. `return self.idle, self.env.timeout(2)` will execute the *different_state* in 2 *units of time* (`t_step=now+2`).
* Example of using timeouts in FSM (custom_timeouts)
* `network_agents` entries may include an `ids` entry. If set, it should be a list of node ids that should be assigned that agent type. This complements the previous behavior of setting agent type with `weights`.

View File

@@ -1,4 +1,11 @@
FROM python:3.4-onbuild
FROM python:3.7
WORKDIR /usr/src/app
COPY test-requirements.txt requirements.txt /usr/src/app/
RUN pip install --no-cache-dir -r test-requirements.txt -r requirements.txt
COPY ./ /usr/src/app
RUN pip install '.[web]'

View File

@@ -1,4 +1,7 @@
include requirements.txt
include test-requirements.txt
include README.rst
graft soil
graft soil
global-exclude __pycache__
global-exclude soil_output
global-exclude *.py[co]

4
Makefile Normal file
View File

@@ -0,0 +1,4 @@
test:
docker-compose exec dev python -m pytest -s -v
.PHONY: test

View File

@@ -2,6 +2,8 @@ version: '3'
services:
dev:
build: .
environment:
PYTHONDONTWRITEBYTECODE: 1
volumes:
- .:/usr/src/app
tty: true

244
docs/configuration.rst Normal file
View File

@@ -0,0 +1,244 @@
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.

View File

@@ -6,7 +6,7 @@
Welcome to Soil's documentation!
================================
Soil is an Agent-based Social Simulator in Python for modelling and simulation of Social Networks.
Soil is an Agent-based Social Simulator in Python focused on Social Networks.
If you use Soil in your research, do not forget to cite this paper:
@@ -39,6 +39,7 @@ If you use Soil in your research, do not forget to cite this paper:
installation
quickstart
configuration
Tutorial <soil_tutorial>
..

View File

@@ -1,197 +1,93 @@
Quickstart
----------
This section shows how to run simulations from simulation configuration files.
First of all, you need to install the package (See :doc:`installation`)
This section shows how to run your first simulation with Soil.
For installation instructions, see :doc:`installation`.
Simulation configuration files are ``json`` or ``yaml`` files that 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:
network_type: barabasi_albert_graph
n: 100
m: 2
agent_distribution:
- 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
There are mainly two parts in a simulation: agent classes and simulation configuration.
An agent class defines how the agent will behave throughout the simulation.
The configuration includes things such as number of agents to use and their type, network topology to use, etc.
This example configuration will run three trials of a simulation containing a randomly generated network.
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.
All agents will have access to the environment, 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``.
Four 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 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``).
.. image:: soil.png
:width: 80%
:align: center
.. code::
Soil includes several agent classes in the ``soil.agents`` module, and we will use them in this quickstart.
If you are interested in developing your own agents classes, see :doc:`soil_tutorial`.
soil_output
├── Sim_prob_0
│   ├── Sim_prob_0.dumped.yml
│   ├── Sim_prob_0.simulation.pickle
│   ├── Sim_prob_0_trial_0.environment.csv
│   └── Sim_prob_0_trial_0.gexf
Configuration
=============
To get you started, we will use this configuration (:download:`download the file <quickstart.yml>` directly):
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.
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(100)`:
.. code:: yaml
network_params:
network_type: 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
Any agent has unrestricted access to the environment.
However, for the sake of simplicity, we recommend limiting environment updates to environment agents.
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 ``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
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
.. literalinclude:: quickstart.yml
:language: yaml
The agent type used, SISa, is a very simple model.
It only has three states (neutral, content and discontent),
Its parameters are the probabilities to change from one state to another, either spontaneously or because of contagion from neighboring agents.
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.
Running the simulation
======================
To see the simulation in action, simply point soil to the configuration, and tell it to store the graph and the history of agent states and environment parameters at every point.
.. code::
environment_agents:
- agent_type: MyAgent
state:
mood: happy
- agent_type: DummyAgent
soil --graph --csv quickstart.yml [13:35:29]
INFO:soil:Using config(s): quickstart
INFO:soil:Dumping results to soil_output/quickstart : ['csv', 'gexf']
INFO:soil:Starting simulation quickstart at 13:35:30.
INFO:soil:Starting Simulation quickstart trial 0 at 13:35:30.
INFO:soil:Finished Simulation quickstart trial 0 at 13:35:49 in 19.43677067756653 seconds
INFO:soil:Starting Dumping simulation quickstart trial 0 at 13:35:49.
INFO:soil:Finished Dumping simulation quickstart trial 0 at 13:35:51 in 1.7733407020568848 seconds
INFO:soil:Dumping results to soil_output/quickstart
INFO:soil:Finished simulation quickstart at 13:35:51 in 21.29862952232361 seconds
Visualizing the results
=======================
The ``CSV`` file should look like this:
The simulation will return a dynamic graph .gexf file which could be visualized with
.. code::
agent_id,t_step,key,value
env,0,neutral_discontent_spon_prob,0.05
env,0,neutral_discontent_infected_prob,0.1
env,0,neutral_content_spon_prob,0.2
env,0,neutral_content_infected_prob,0.4
env,0,discontent_neutral,0.2
env,0,discontent_content,0.05
env,0,content_discontent,0.05
env,0,variance_d_c,0.05
env,0,variance_c_d,0.1
Results and visualization
=========================
The environment variables are marked as ``agent_id`` env.
Th exported values are only stored when they change.
To find out how to get every key and value at every point in the simulation, check out the :doc:`soil_tutorial`.
The dynamic graph is exported as a .gexf file which could be visualized with
`Gephi <https://gephi.org/users/download/>`__.
Now it is your turn to experiment with the simulation.
Change some of the parameters, such as the number of agents, the probability of becoming content, or the type of network, and see how the results change.
Soil also includes a web server that allows you to upload your simulations, change parameters, and visualize the results, including a timeline of the network.
To make it work, you have to install soil like this:
.. code::
pip install soil[web]
Once installed, the soil web UI can be run in two ways:
.. code::
soil-web
# OR
python -m soil.web

30
docs/quickstart.yml Normal file
View File

@@ -0,0 +1,30 @@
---
name: quickstart
num_trials: 1
max_time: 1000
network_agents:
- agent_type: SISaModel
state:
id: neutral
weight: 1
- agent_type: SISaModel
state:
id: content
weight: 2
network_params:
n: 100
k: 5
p: 0.2
generator: newman_watts_strogatz_graph
environment_params:
neutral_discontent_spon_prob: 0.05
neutral_discontent_infected_prob: 0.1
neutral_content_spon_prob: 0.2
neutral_content_infected_prob: 0.4
discontent_neutral: 0.2
discontent_content: 0.05
content_discontent: 0.05
variance_d_c: 0.05
variance_c_d: 0.1
content_neutral: 0.1
standard_variance: 0.1

BIN
docs/soil.png Normal file

Binary file not shown.

After

Width:  |  Height:  |  Size: 43 KiB

View File

@@ -26,7 +26,7 @@ But before that, let's import the soil module and networkx.
%autoreload 2
%pylab inline
# To display plots in the notebooed_
# To display plots in the notebook_
.. parsed-literal::
@@ -214,7 +214,7 @@ nodes in that network. Notice how node 0 is the only one with a TV.
MAX_TIME = 100
EVENT_TIME = 10
sim = soil.simulation.SoilSimulation(topology=G,
sim = soil.Simulation(topology=G,
num_trials=1,
max_time=MAX_TIME,
environment_agents=[{'agent_type': NewsEnvironmentAgent,
@@ -2531,7 +2531,7 @@ Dealing with bigger data
.. parsed-literal::
267M ../rabbits/soil_output/rabbits_example/
267M ../rabbits/soil_output/rabbits_example/
If we tried to load the entire history, we would probably run out of

View File

@@ -2,14 +2,22 @@
"cells": [
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"start_time": "2017-11-02T09:48:41.843Z"
},
"scrolled": false
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"import soil\n",
"import networkx as nx\n",
@@ -39,26 +47,216 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total 288K\r\n",
"drwxr-xr-x 7 j users 4.0K May 23 12:48 .\r\n",
"drwxr-xr-x 15 j users 20K May 7 18:59 ..\r\n",
"-rw-r--r-- 1 j users 451 Oct 17 2017 complete.yml\r\n",
"drwxr-xr-x 2 j users 4.0K Feb 18 11:22 .ipynb_checkpoints\r\n",
"drwxr-xr-x 2 j users 4.0K Oct 17 2017 long_running\r\n",
"-rw-r--r-- 1 j users 1.2K May 23 12:49 .nbgrader.log\r\n",
"drwxr-xr-x 4 j users 4.0K May 4 11:23 newsspread\r\n",
"-rw-r--r-- 1 j users 225K May 4 11:23 NewsSpread.ipynb\r\n",
"drwxr-xr-x 4 j users 4.0K May 4 11:21 rabbits\r\n",
"-rw-r--r-- 1 j users 42 Jul 3 2017 torvalds.edgelist\r\n",
"-rw-r--r-- 1 j users 245 Oct 13 2017 torvalds.yml\r\n",
"drwxr-xr-x 4 j users 4.0K May 4 11:23 tutorial\r\n"
]
}
],
"source": [
"!ls "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"start_time": "2017-11-02T09:48:43.440Z"
}
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"---\r\n",
"default_state: {}\r\n",
"load_module: newsspread\r\n",
"environment_agents: []\r\n",
"environment_params:\r\n",
" prob_neighbor_spread: 0.0\r\n",
" prob_tv_spread: 0.01\r\n",
"interval: 1\r\n",
"max_time: 30\r\n",
"name: Sim_all_dumb\r\n",
"network_agents:\r\n",
"- agent_type: DumbViewer\r\n",
" state:\r\n",
" has_tv: false\r\n",
" weight: 1\r\n",
"- agent_type: DumbViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" weight: 1\r\n",
"network_params:\r\n",
" generator: barabasi_albert_graph\r\n",
" n: 500\r\n",
" m: 5\r\n",
"num_trials: 50\r\n",
"---\r\n",
"default_state: {}\r\n",
"load_module: newsspread\r\n",
"environment_agents: []\r\n",
"environment_params:\r\n",
" prob_neighbor_spread: 0.0\r\n",
" prob_tv_spread: 0.01\r\n",
"interval: 1\r\n",
"max_time: 30\r\n",
"name: Sim_half_herd\r\n",
"network_agents:\r\n",
"- agent_type: DumbViewer\r\n",
" state:\r\n",
" has_tv: false\r\n",
" weight: 1\r\n",
"- agent_type: DumbViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" weight: 1\r\n",
"- agent_type: HerdViewer\r\n",
" state:\r\n",
" has_tv: false\r\n",
" weight: 1\r\n",
"- agent_type: HerdViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" weight: 1\r\n",
"network_params:\r\n",
" generator: barabasi_albert_graph\r\n",
" n: 500\r\n",
" m: 5\r\n",
"num_trials: 50\r\n",
"---\r\n",
"default_state: {}\r\n",
"load_module: newsspread\r\n",
"environment_agents: []\r\n",
"environment_params:\r\n",
" prob_neighbor_spread: 0.0\r\n",
" prob_tv_spread: 0.01\r\n",
"interval: 1\r\n",
"max_time: 30\r\n",
"name: Sim_all_herd\r\n",
"network_agents:\r\n",
"- agent_type: HerdViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" id: neutral\r\n",
" weight: 1\r\n",
"- agent_type: HerdViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" id: neutral\r\n",
" weight: 1\r\n",
"network_params:\r\n",
" generator: barabasi_albert_graph\r\n",
" n: 500\r\n",
" m: 5\r\n",
"num_trials: 50\r\n",
"---\r\n",
"default_state: {}\r\n",
"load_module: newsspread\r\n",
"environment_agents: []\r\n",
"environment_params:\r\n",
" prob_neighbor_spread: 0.0\r\n",
" prob_tv_spread: 0.01\r\n",
" prob_neighbor_cure: 0.1\r\n",
"interval: 1\r\n",
"max_time: 30\r\n",
"name: Sim_wise_herd\r\n",
"network_agents:\r\n",
"- agent_type: HerdViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" id: neutral\r\n",
" weight: 1\r\n",
"- agent_type: WiseViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" weight: 1\r\n",
"network_params:\r\n",
" generator: barabasi_albert_graph\r\n",
" n: 500\r\n",
" m: 5\r\n",
"num_trials: 50\r\n",
"---\r\n",
"default_state: {}\r\n",
"load_module: newsspread\r\n",
"environment_agents: []\r\n",
"environment_params:\r\n",
" prob_neighbor_spread: 0.0\r\n",
" prob_tv_spread: 0.01\r\n",
" prob_neighbor_cure: 0.1\r\n",
"interval: 1\r\n",
"max_time: 30\r\n",
"name: Sim_all_wise\r\n",
"network_agents:\r\n",
"- agent_type: WiseViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" id: neutral\r\n",
" weight: 1\r\n",
"- agent_type: WiseViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" weight: 1\r\n",
"network_params:\r\n",
" generator: barabasi_albert_graph\r\n",
" n: 500\r\n",
" m: 5\r\n",
"network_params:\r\n",
" generator: barabasi_albert_graph\r\n",
" n: 500\r\n",
" m: 5\r\n",
"num_trials: 50\r\n"
]
}
],
"source": [
"!cat NewsSpread.yml"
"!cat newsspread/NewsSpread.yml"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"start_time": "2017-11-02T09:48:43.879Z"
}
},
"outputs": [],
"outputs": [
{
"ename": "ValueError",
"evalue": "No objects to concatenate",
"output_type": "error",
"traceback": [
"\u001b[0;31m----------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-bae848826594>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevodumb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manalysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'soil_output/Sim_all_dumb/'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0manalysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/git/lab.gsi/soil/soil/soil/analysis.py\u001b[0m in \u001b[0;36mread_data\u001b[0;34m(group, *args, **kwargs)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0miterable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mgroup_trials\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/lab.gsi/soil/soil/soil/analysis.py\u001b[0m in \u001b[0;36mgroup_trials\u001b[0;34m(trials, aggfunc)\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0mtrials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 160\u001b[0m \u001b[0mtrials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maggfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreorder_levels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.6/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverify_integrity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m copy=copy)\n\u001b[0m\u001b[1;32m 213\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.6/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'No objects to concatenate'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: No objects to concatenate"
]
}
],
"source": [
"evodumb = analysis.read_data('soil_output/Sim_all_dumb/', group=True, process=analysis.get_count, keys=['id']);"
]
@@ -302,7 +500,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
"version": "3.6.5"
},
"toc": {
"colors": {

80808
examples/Untitled.ipynb Normal file

File diff suppressed because it is too large Load Diff

View File

@@ -2,6 +2,7 @@
name: simple
dir_path: "/tmp/"
num_trials: 3
dry_run: True
max_time: 100
interval: 1
seed: "CompleteSeed!"
@@ -17,6 +18,7 @@ network_agents:
- agent_type: AggregatedCounter
weight: 0.2
environment_agents: []
environment_class: Environment
environment_params:
am_i_complete: true
default_state:

View File

@@ -0,0 +1,17 @@
---
name: custom-generator
description: Using a custom generator for the network
num_trials: 3
dry_run: True
max_time: 100
interval: 1
network_params:
generator: mymodule.mygenerator
# These are custom parameters
n: 10
n_edges: 5
network_agents:
- agent_type: CounterModel
weight: 1
state:
id: 0

View File

@@ -0,0 +1,27 @@
from networkx import Graph
import networkx as nx
from random import choice
def mygenerator(n=5, n_edges=5):
'''
Just a simple generator that creates a network with n nodes and
n_edges edges. Edges are assigned randomly, only avoiding self loops.
'''
G = nx.Graph()
for i in range(n):
G.add_node(i)
for i in range(n_edges):
nodes = list(G.nodes)
n_in = choice(nodes)
nodes.remove(n_in) # Avoid loops
n_out = choice(nodes)
G.add_edge(n_in, n_out)
return G

View File

@@ -0,0 +1,36 @@
from soil.agents import FSM, state, default_state
class Fibonacci(FSM):
'''Agent that only executes in t_steps that are Fibonacci numbers'''
defaults = {
'prev': 1
}
@default_state
@state
def counting(self):
self.log('Stopping at {}'.format(self.now))
prev, self['prev'] = self['prev'], max([self.now, self['prev']])
return None, self.env.timeout(prev)
class Odds(FSM):
'''Agent that only executes in odd t_steps'''
@default_state
@state
def odds(self):
self.log('Stopping at {}'.format(self.now))
return None, self.env.timeout(1+self.now%2)
if __name__ == '__main__':
import logging
logging.basicConfig(level=logging.INFO)
from soil import Simulation
s = Simulation(network_agents=[{'ids': [0], 'agent_type': Fibonacci},
{'ids': [1], 'agent_type': Odds}],
dry_run=True,
network_params={"generator": "complete_graph", "n": 2},
max_time=100,
)
s.run()

View File

@@ -6,7 +6,7 @@ environment_params:
prob_neighbor_spread: 0.0
prob_tv_spread: 0.01
interval: 1
max_time: 30
max_time: 300
name: Sim_all_dumb
network_agents:
- agent_type: DumbViewer
@@ -30,7 +30,7 @@ environment_params:
prob_neighbor_spread: 0.0
prob_tv_spread: 0.01
interval: 1
max_time: 30
max_time: 300
name: Sim_half_herd
network_agents:
- agent_type: DumbViewer
@@ -62,7 +62,7 @@ environment_params:
prob_neighbor_spread: 0.0
prob_tv_spread: 0.01
interval: 1
max_time: 30
max_time: 300
name: Sim_all_herd
network_agents:
- agent_type: HerdViewer
@@ -89,7 +89,7 @@ environment_params:
prob_tv_spread: 0.01
prob_neighbor_cure: 0.1
interval: 1
max_time: 30
max_time: 300
name: Sim_wise_herd
network_agents:
- agent_type: HerdViewer
@@ -115,7 +115,7 @@ environment_params:
prob_tv_spread: 0.01
prob_neighbor_cure: 0.1
interval: 1
max_time: 30
max_time: 300
name: Sim_all_wise
network_agents:
- agent_type: WiseViewer

1
examples/programmatic/.gitignore vendored Normal file
View File

@@ -0,0 +1 @@
Programmatic*

View File

@@ -0,0 +1,38 @@
'''
Example of a fully programmatic simulation, without definition files.
'''
from soil import Simulation, agents
from networkx import Graph
import logging
def mygenerator():
# Add only a node
G = Graph()
G.add_node(1)
return G
class MyAgent(agents.FSM):
@agents.default_state
@agents.state
def neutral(self):
self.info('I am running')
s = Simulation(name='Programmatic',
network_params={'generator': mygenerator},
num_trials=1,
max_time=100,
agent_type=MyAgent,
dry_run=True)
logging.basicConfig(level=logging.INFO)
envs = s.run()
s.dump_yaml()
for env in envs:
env.dump_csv()

View File

@@ -0,0 +1,10 @@
Simulation of pubs and drinking pals that go from pub to pub.
Th custom environment includes a list of pubs and methods to allow agents to discover and enter pubs.
There are two types of agents:
* Patron. A patron will do three things, in this order:
* Look for other patrons to drink with
* Look for a pub where the agent and other agents in the same group can get in.
* While in the pub, patrons only drink, until they get drunk and taken home.
* Police. There is only one police agent that will take any drunk patrons home (kick them out of the pub).

View File

@@ -0,0 +1,174 @@
from soil.agents import FSM, state, default_state
from soil import Environment
from random import random, shuffle
from itertools import islice
import logging
class CityPubs(Environment):
'''Environment with Pubs'''
level = logging.INFO
def __init__(self, *args, number_of_pubs=3, pub_capacity=10, **kwargs):
super(CityPubs, self).__init__(*args, **kwargs)
pubs = {}
for i in range(number_of_pubs):
newpub = {
'name': 'The awesome pub #{}'.format(i),
'open': True,
'capacity': pub_capacity,
'occupancy': 0,
}
pubs[newpub['name']] = newpub
self['pubs'] = pubs
def enter(self, pub_id, *nodes):
'''Agents will try to enter. The pub checks if it is possible'''
try:
pub = self['pubs'][pub_id]
except KeyError:
raise ValueError('Pub {} is not available'.format(pub_id))
if not pub['open'] or (pub['capacity'] < (len(nodes) + pub['occupancy'])):
return False
pub['occupancy'] += len(nodes)
for node in nodes:
node['pub'] = pub_id
return True
def available_pubs(self):
for pub in self['pubs'].values():
if pub['open'] and (pub['occupancy'] < pub['capacity']):
yield pub['name']
def exit(self, pub_id, *node_ids):
'''Agents will notify the pub they want to leave'''
try:
pub = self['pubs'][pub_id]
except KeyError:
raise ValueError('Pub {} is not available'.format(pub_id))
for node_id in node_ids:
node = self.get_agent(node_id)
if pub_id == node['pub']:
del node['pub']
pub['occupancy'] -= 1
class Patron(FSM):
'''Agent that looks for friends to drink with. It will do three things:
1) Look for other patrons to drink with
2) Look for a bar where the agent and other agents in the same group can get in.
3) While in the bar, patrons only drink, until they get drunk and taken home.
'''
level = logging.INFO
defaults = {
'pub': None,
'drunk': False,
'pints': 0,
'max_pints': 3,
}
@default_state
@state
def looking_for_friends(self):
'''Look for friends to drink with'''
self.info('I am looking for friends')
available_friends = list(self.get_agents(drunk=False,
pub=None,
state_id=self.looking_for_friends.id))
if not available_friends:
self.info('Life sucks and I\'m alone!')
return self.at_home
befriended = self.try_friends(available_friends)
if befriended:
return self.looking_for_pub
@state
def looking_for_pub(self):
'''Look for a pub that accepts me and my friends'''
if self['pub'] != None:
return self.sober_in_pub
self.debug('I am looking for a pub')
group = list(self.get_neighboring_agents())
for pub in self.env.available_pubs():
self.debug('We\'re trying to get into {}: total: {}'.format(pub, len(group)))
if self.env.enter(pub, self, *group):
self.info('We\'re all {} getting in {}!'.format(len(group), pub))
return self.sober_in_pub
@state
def sober_in_pub(self):
'''Drink up.'''
self.drink()
if self['pints'] > self['max_pints']:
return self.drunk_in_pub
@state
def drunk_in_pub(self):
'''I'm out. Take me home!'''
self.info('I\'m so drunk. Take me home!')
self['drunk'] = True
pass # out drunk
@state
def at_home(self):
'''The end'''
self.debug('Life sucks. I\'m home!')
def drink(self):
self['pints'] += 1
self.debug('Cheers to that')
def kick_out(self):
self.set_state(self.at_home)
def befriend(self, other_agent, force=False):
'''
Try to become friends with another agent. The chances of
success depend on both agents' openness.
'''
if force or self['openness'] > random():
self.env.add_edge(self, other_agent)
self.info('Made some friend {}'.format(other_agent))
return True
return False
def try_friends(self, others):
''' Look for random agents around me and try to befriend them'''
befriended = False
k = int(10*self['openness'])
shuffle(others)
for friend in islice(others, k): # random.choice >= 3.7
if friend == self:
continue
if friend.befriend(self):
self.befriend(friend, force=True)
self.debug('Hooray! new friend: {}'.format(friend.id))
befriended = True
else:
self.debug('{} does not want to be friends'.format(friend.id))
return befriended
class Police(FSM):
'''Simple agent to take drunk people out of pubs.'''
level = logging.INFO
@default_state
@state
def patrol(self):
drunksters = list(self.get_agents(drunk=True,
state_id=Patron.drunk_in_pub.id))
for drunk in drunksters:
self.info('Kicking out the trash: {}'.format(drunk.id))
drunk.kick_out()
else:
self.info('No trash to take out. Too bad.')
if __name__ == '__main__':
from soil import simulation
simulation.run_from_config('pubcrawl.yml',
dry_run=True,
dump=None,
parallel=False)

View File

@@ -0,0 +1,26 @@
---
name: pubcrawl
num_trials: 3
max_time: 10
dump: false
network_params:
# Generate 100 empty nodes. They will be assigned a network agent
generator: empty_graph
n: 30
network_agents:
- agent_type: pubcrawl.Patron
description: Extroverted patron
state:
openness: 1.0
weight: 9
- agent_type: pubcrawl.Patron
description: Introverted patron
state:
openness: 0.1
weight: 1
environment_agents:
- agent_type: pubcrawl.Police
environment_class: pubcrawl.CityPubs
environment_params:
altercations: 0
number_of_pubs: 3

View File

@@ -1,7 +1,7 @@
---
load_module: rabbit_agents
name: rabbits_example
max_time: 1200
max_time: 500
interval: 1
seed: MySeed
agent_type: RabbitModel

View File

@@ -0,0 +1,208 @@
import random
import networkx as nx
from soil.agents import Geo, NetworkAgent, FSM, state, default_state
from soil import Environment
class TerroristSpreadModel(FSM, Geo):
"""
Settings:
information_spread_intensity
terrorist_additional_influence
min_vulnerability (optional else zero)
max_vulnerability
prob_interaction
"""
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.information_spread_intensity = environment.environment_params['information_spread_intensity']
self.terrorist_additional_influence = environment.environment_params['terrorist_additional_influence']
self.prob_interaction = environment.environment_params['prob_interaction']
if self['id'] == self.civilian.id: # Civilian
self.mean_belief = random.uniform(0.00, 0.5)
elif self['id'] == self.terrorist.id: # Terrorist
self.mean_belief = random.uniform(0.8, 1.00)
elif self['id'] == self.leader.id: # Leader
self.mean_belief = 1.00
else:
raise Exception('Invalid state id: {}'.format(self['id']))
if 'min_vulnerability' in environment.environment_params:
self.vulnerability = random.uniform( environment.environment_params['min_vulnerability'], environment.environment_params['max_vulnerability'] )
else :
self.vulnerability = random.uniform( 0, environment.environment_params['max_vulnerability'] )
@state
def civilian(self):
neighbours = list(self.get_neighboring_agents(agent_type=TerroristSpreadModel))
if len(neighbours) > 0:
# Only interact with some of the neighbors
interactions = list(n for n in neighbours if random.random() <= self.prob_interaction)
influence = sum( self.degree(i) for i in interactions )
mean_belief = sum( i.mean_belief * self.degree(i) / influence for i in interactions )
mean_belief = mean_belief * self.information_spread_intensity + self.mean_belief * ( 1 - self.information_spread_intensity )
self.mean_belief = mean_belief * self.vulnerability + self.mean_belief * ( 1 - self.vulnerability )
if self.mean_belief >= 0.8:
return self.terrorist
@state
def leader(self):
self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
for neighbour in self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]):
if self.betweenness(neighbour) > self.betweenness(self):
return self.terrorist
@state
def terrorist(self):
neighbours = self.get_agents(state_id=[self.terrorist.id, self.leader.id],
agent_type=TerroristSpreadModel,
limit_neighbors=True)
if len(neighbours) > 0:
influence = sum( self.degree(n) for n in neighbours )
mean_belief = sum( n.mean_belief * self.degree(n) / influence for n in neighbours )
mean_belief = mean_belief * self.vulnerability + self.mean_belief * ( 1 - self.vulnerability )
self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
# Check if there are any leaders in the group
leaders = list(filter(lambda x: x.state.id == self.leader.id, neighbours))
if not leaders:
# Check if this is the potential leader
# Stop once it's found. Otherwise, set self as leader
for neighbour in neighbours:
if self.betweenness(self) < self.betweenness(neighbour):
return
return self.leader
class TrainingAreaModel(FSM, Geo):
"""
Settings:
training_influence
min_vulnerability
Requires TerroristSpreadModel.
"""
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.training_influence = environment.environment_params['training_influence']
if 'min_vulnerability' in environment.environment_params:
self.min_vulnerability = environment.environment_params['min_vulnerability']
else: self.min_vulnerability = 0
@default_state
@state
def terrorist(self):
for neighbour in self.get_neighboring_agents(agent_type=TerroristSpreadModel):
if neighbour.vulnerability > self.min_vulnerability:
neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.training_influence )
class HavenModel(FSM, Geo):
"""
Settings:
haven_influence
min_vulnerability
max_vulnerability
Requires TerroristSpreadModel.
"""
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.haven_influence = environment.environment_params['haven_influence']
if 'min_vulnerability' in environment.environment_params:
self.min_vulnerability = environment.environment_params['min_vulnerability']
else: self.min_vulnerability = 0
self.max_vulnerability = environment.environment_params['max_vulnerability']
def get_occupants(self, **kwargs):
return self.get_neighboring_agents(agent_type=TerroristSpreadModel, **kwargs)
@state
def civilian(self):
civilians = self.get_occupants(state_id=self.civilian.id)
if not civilians:
return self.terrorist
for neighbour in self.get_occupants():
if neighbour.vulnerability > self.min_vulnerability:
neighbour.vulnerability = neighbour.vulnerability * ( 1 - self.haven_influence )
return self.civilian
@state
def terrorist(self):
for neighbour in self.get_occupants():
if neighbour.vulnerability < self.max_vulnerability:
neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.haven_influence )
return self.terrorist
class TerroristNetworkModel(TerroristSpreadModel):
"""
Settings:
sphere_influence
vision_range
weight_social_distance
weight_link_distance
"""
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.vision_range = environment.environment_params['vision_range']
self.sphere_influence = environment.environment_params['sphere_influence']
self.weight_social_distance = environment.environment_params['weight_social_distance']
self.weight_link_distance = environment.environment_params['weight_link_distance']
@state
def terrorist(self):
self.update_relationships()
return super().terrorist()
@state
def leader(self):
self.update_relationships()
return super().leader()
def update_relationships(self):
if self.count_neighboring_agents(state_id=self.civilian.id) == 0:
close_ups = set(self.geo_search(radius=self.vision_range, agent_type=TerroristNetworkModel))
step_neighbours = set(self.ego_search(self.sphere_influence, agent_type=TerroristNetworkModel, center=False))
neighbours = set(agent.id for agent in self.get_neighboring_agents(agent_type=TerroristNetworkModel))
search = (close_ups | step_neighbours) - neighbours
for agent in self.get_agents(search):
social_distance = 1 / self.shortest_path_length(agent.id)
spatial_proximity = ( 1 - self.get_distance(agent.id) )
prob_new_interaction = self.weight_social_distance * social_distance + self.weight_link_distance * spatial_proximity
if agent['id'] == agent.civilian.id and random.random() < prob_new_interaction:
self.add_edge(agent)
break
def get_distance(self, target):
source_x, source_y = nx.get_node_attributes(self.global_topology, 'pos')[self.id]
target_x, target_y = nx.get_node_attributes(self.global_topology, 'pos')[target]
dx = abs( source_x - target_x )
dy = abs( source_y - target_y )
return ( dx ** 2 + dy ** 2 ) ** ( 1 / 2 )
def shortest_path_length(self, target):
try:
return nx.shortest_path_length(self.global_topology, self.id, target)
except nx.NetworkXNoPath:
return float('inf')

View File

@@ -60,3 +60,4 @@ visualization_params:
background_image: 'map_4800x2860.jpg'
background_opacity: '0.9'
background_filter_color: 'blue'
skip_test: true # This simulation takes too long for automated tests.

View File

@@ -12327,7 +12327,7 @@ Notice how node 0 is the only one with a TV.</p>
<span class="n">MAX_TIME</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">EVENT_TIME</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">sim</span> <span class="o">=</span> <span class="n">soil</span><span class="o">.</span><span class="n">simulation</span><span class="o">.</span><span class="n">SoilSimulation</span><span class="p">(</span><span class="n">topology</span><span class="o">=</span><span class="n">G</span><span class="p">,</span>
<span class="n">sim</span> <span class="o">=</span> <span class="n">soil</span><span class="o">.</span><span class="n">Simulation</span><span class="p">(</span><span class="n">topology</span><span class="o">=</span><span class="n">G</span><span class="p">,</span>
<span class="n">num_trials</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">max_time</span><span class="o">=</span><span class="n">MAX_TIME</span><span class="p">,</span>
<span class="n">environment_agents</span><span class="o">=</span><span class="p">[{</span><span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsEnvironmentAgent</span><span class="p">,</span>
@@ -21883,7 +21883,7 @@ bgAAAABJRU5ErkJggg==
<div class="output_subarea output_stream output_stdout output_text">
<pre>267M ../rabbits/soil_output/rabbits_example/
<pre>267M ../rabbits/soil_output/rabbits_example/
</pre>
</div>
</div>

View File

@@ -426,7 +426,7 @@
"MAX_TIME = 100\n",
"EVENT_TIME = 10\n",
"\n",
"sim = soil.simulation.SoilSimulation(topology=G,\n",
"sim = soil.Simulation(topology=G,\n",
" num_trials=1,\n",
" max_time=MAX_TIME,\n",
" environment_agents=[{'agent_type': NewsEnvironmentAgent,\n",

View File

@@ -5,3 +5,4 @@ numpy
matplotlib
pyyaml
pandas
scipy

4
setup.cfg Normal file
View File

@@ -0,0 +1,4 @@
[aliases]
test=pytest
[tool:pytest]
addopts = --verbose

View File

@@ -1 +1 @@
0.12.0
0.13.8

View File

@@ -11,19 +11,20 @@ try:
except NameError:
basestring = str
logging.basicConfig()
from . import agents
from . import simulation
from . import environment
from .simulation import *
from .environment import Environment
from .history import History
from . import utils
from . import analysis
def main():
import argparse
from . import simulation
logging.basicConfig(level=logging.INFO)
logging.info('Running SOIL version: {}'.format(__version__))
parser = argparse.ArgumentParser(description='Run a SOIL simulation')
parser.add_argument('file', type=str,
nargs="?",
@@ -46,11 +47,12 @@ def main():
args = parser.parse_args()
if args.module:
if os.getcwd() not in sys.path:
sys.path.append(os.getcwd())
if args.module:
importlib.import_module(args.module)
logging.info('Loading config file: {}'.format(args.file, args.output))
logging.info('Loading config file: {}'.format(args.file))
try:
dump = []
@@ -62,9 +64,9 @@ def main():
simulation.run_from_config(args.file,
dry_run=args.dry_run,
dump=dump,
parallel=(not args.synchronous and not args.pdb),
parallel=(not args.synchronous),
results_dir=args.output)
except Exception as ex:
except Exception:
if args.pdb:
pdb.post_mortem()
else:

View File

@@ -22,11 +22,17 @@ class AggregatedCounter(BaseAgent):
in each step and adds it to its state.
"""
defaults = {
'times': 0,
'neighbors': 0,
'total': 0
}
def step(self):
# Outside effects
total = len(list(self.get_all_agents()))
self['times'] += 1
neighbors = len(list(self.get_neighboring_agents()))
self['times'] = self.get('times', 0) + 1
self['neighbors'] = self.get('neighbors', 0) + neighbors
self['total'] = total = self.get('total', 0) + total
self['neighbors'] += neighbors
total = len(list(self.get_all_agents()))
self['total'] += total
self.debug('Running for step: {}. Total: {}'.format(self.now, total))

View File

@@ -10,7 +10,7 @@ class SISaModel(FSM):
neutral_discontent_infected_prob
neutral_content_spong_prob
neutral_content_spon_prob
neutral_content_infected_prob
@@ -29,27 +29,27 @@ class SISaModel(FSM):
standard_variance
"""
def __init__(self, environment=None, agent_id=0, state=()):
def __init__(self, environment, 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.neutral_discontent_spon_prob = np.random.normal(self.env['neutral_discontent_spon_prob'],
self.env['standard_variance'])
self.neutral_discontent_infected_prob = np.random.normal(self.env['neutral_discontent_infected_prob'],
self.env['standard_variance'])
self.neutral_content_spon_prob = np.random.normal(self.env['neutral_content_spon_prob'],
self.env['standard_variance'])
self.neutral_content_infected_prob = np.random.normal(self.env['neutral_content_infected_prob'],
self.env['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.discontent_neutral = np.random.normal(self.env['discontent_neutral'],
self.env['standard_variance'])
self.discontent_content = np.random.normal(self.env['discontent_content'],
self.env['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'])
self.content_discontent = np.random.normal(self.env['content_discontent'],
self.env['variance_c_d'])
self.content_neutral = np.random.normal(self.env['content_neutral'],
self.env['standard_variance'])
@state
def neutral(self):

View File

@@ -16,7 +16,7 @@ class SentimentCorrelationModel(BaseAgent):
disgust_prob
"""
def __init__(self, environment=None, agent_id=0, state=()):
def __init__(self, environment, 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']

View File

@@ -10,42 +10,37 @@ import logging
from collections import OrderedDict
from copy import deepcopy
from functools import partial
from scipy.spatial import cKDTree as KDTree
import json
from functools import wraps
from .. import utils, history
agent_types = {}
def as_node(agent):
if isinstance(agent, BaseAgent):
return agent.id
return agent
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):
class BaseAgent(nxsim.BaseAgent):
"""
A special simpy BaseAgent that keeps track of its state history.
"""
defaults = {}
def __init__(self, environment=None, agent_id=None, state=None,
name='network_process', interval=None, **state_params):
def __init__(self, environment, agent_id, state=None,
name=None, interval=None, **state_params):
# Check for REQUIRED arguments
assert environment is not None, TypeError('__init__ missing 1 required keyword argument: \'environment\'. '
'Cannot be NoneType.')
# Initialize agent parameters
self.id = agent_id
self.name = name
self.name = name or '{}[{}]'.format(type(self).__name__, self.id)
self.state_params = state_params
# Global parameters
self.global_topology = environment.G
self.environment_params = environment.environment_params
# Register agent to environment
self.env = environment
@@ -58,8 +53,7 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
if not hasattr(self, 'level'):
self.level = logging.DEBUG
self.logger = logging.getLogger('{}-Agent-{}'.format(self.env.name,
self.id))
self.logger = logging.getLogger(self.env.name)
self.logger.setLevel(self.level)
# initialize every time an instance of the agent is created
@@ -81,6 +75,18 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
for k, v in value.items():
self[k] = v
@property
def global_topology(self):
return self.env.G
@property
def environment_params(self):
return self.env.environment_params
@environment_params.setter
def environment_params(self, value):
self.env.environment_params = value
def __getitem__(self, key):
if isinstance(key, tuple):
key, t_step = key
@@ -134,46 +140,25 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
def step(self):
pass
def to_json(self):
return json.dumps(self.state)
def count_agents(self, **kwargs):
return len(list(self.get_agents(**kwargs)))
def count_agents(self, state_id=None, limit_neighbors=False):
def count_neighboring_agents(self, state_id=None, **kwargs):
return len(super().get_neighboring_agents(state_id=state_id, **kwargs))
def get_neighboring_agents(self, state_id=None, **kwargs):
return self.get_agents(limit_neighbors=True, state_id=state_id, **kwargs)
def get_agents(self, agents=None, limit_neighbors=False, **kwargs):
if limit_neighbors:
agents = self.global_topology.neighbors(self.id)
agents = super().get_agents(limit_neighbors=limit_neighbors)
else:
agents = self.global_topology.nodes()
count = 0
for agent in agents:
if state_id and state_id != self.global_topology.node[agent]['agent']['id']:
continue
count += 1
return count
def count_neighboring_agents(self, state_id=None):
return len(super().get_agents(state_id, limit_neighbors=True))
def get_agents(self, state_id=None, limit_neighbors=False, iterator=False, **kwargs):
if limit_neighbors:
agents = super().get_agents(state_id, limit_neighbors)
else:
agents = filter(lambda x: state_id is None or x.state.get('id', None) == state_id,
self.env.agents)
def matches_all(agent):
state = agent.state
for k, v in kwargs.items():
if state.get(k, None) != v:
return False
return True
f = filter(matches_all, agents)
if iterator:
return f
return list(f)
agents = self.env.get_agents(agents)
return select(agents, **kwargs)
def log(self, message, *args, level=logging.INFO, **kwargs):
message = message + " ".join(str(i) for i in args)
message = "\t@{:>5}:\t{}".format(self.now, message)
message = "\t{:10}@{:>5}:\t{}".format(self.name, self.now, message)
for k, v in kwargs:
message += " {k}={v} ".format(k, v)
extra = {}
@@ -186,32 +171,97 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
def info(self, *args, **kwargs):
return self.log(*args, level=logging.INFO, **kwargs)
def __getstate__(self):
'''
Serializing an agent will lose all its running information (you cannot
serialize an iterator), but it keeps the state and link to the environment,
so it can be used for inspection and dumping to a file
'''
state = {}
state['id'] = self.id
state['environment'] = self.env
state['_state'] = self._state
return state
def __setstate__(self, state):
'''
Get back a serialized agent and try to re-compose it
'''
self.id = state['id']
self._state = state['_state']
self.env = state['environment']
def add_edge(self, node1, node2, **attrs):
node1 = as_node(node1)
node2 = as_node(node2)
for n in [node1, node2]:
if n not in self.global_topology.nodes(data=False):
raise ValueError('"{}" not in the graph'.format(n))
return self.global_topology.add_edge(node1, node2, **attrs)
def subgraph(self, center=True, **kwargs):
include = [self] if center else []
return self.global_topology.subgraph(n.id for n in self.get_agents(**kwargs)+include)
def state(func):
'''
A state function should return either a state id, or a tuple (state_id, when)
The default value for state_id is the current state id.
The default value for when is the interval defined in the nevironment.
'''
class NetworkAgent(BaseAgent):
@wraps(func)
def func_wrapper(self):
next_state = func(self)
when = None
if next_state is None:
def add_edge(self, other, **kwargs):
return super(NetworkAgent, self).add_edge(node1=self.id, node2=other, **kwargs)
def ego_search(self, steps=1, center=False, node=None, **kwargs):
'''Get a list of nodes in the ego network of *node* of radius *steps*'''
node = as_node(node if node is not None else self)
G = self.subgraph(**kwargs)
return nx.ego_graph(G, node, center=center, radius=steps).nodes()
def degree(self, node, force=False):
node = as_node(node)
if force or (not hasattr(self.env, '_degree')) or getattr(self.env, '_last_step', 0) < self.now:
self.env._degree = nx.degree_centrality(self.global_topology)
self.env._last_step = self.now
return self.env._degree[node]
def betweenness(self, node, force=False):
node = as_node(node)
if force or (not hasattr(self.env, '_betweenness')) or getattr(self.env, '_last_step', 0) < self.now:
self.env._betweenness = nx.betweenness_centrality(self.global_topology)
self.env._last_step = self.now
return self.env._betweenness[node]
def state(name=None):
def decorator(func, name=None):
'''
A state function should return either a state id, or a tuple (state_id, when)
The default value for state_id is the current state id.
The default value for when is the interval defined in the environment.
'''
@wraps(func)
def func_wrapper(self):
next_state = func(self)
when = None
if next_state is None:
return when
try:
next_state, when = next_state
except (ValueError, TypeError):
pass
if next_state:
self.set_state(next_state)
return when
try:
next_state, when = next_state
except (ValueError, TypeError):
pass
if next_state:
self.set_state(next_state)
return when
func_wrapper.id = func.__name__
func_wrapper.is_default = False
return func_wrapper
func_wrapper.id = name or func.__name__
func_wrapper.is_default = False
return func_wrapper
if callable(name):
return decorator(name)
else:
return partial(decorator, name=name)
def default_state(func):
@@ -219,7 +269,7 @@ def default_state(func):
return func
class MetaFSM(MetaAgent):
class MetaFSM(type):
def __init__(cls, name, bases, nmspc):
super(MetaFSM, cls).__init__(name, bases, nmspc)
states = {}
@@ -259,7 +309,7 @@ class FSM(BaseAgent, metaclass=MetaFSM):
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)
return self.states[next_state](self)
def set_state(self, state):
if hasattr(state, 'id'):
@@ -285,6 +335,9 @@ def prob(prob=1):
return r < prob
STATIC_THRESHOLD = (-1, -1)
def calculate_distribution(network_agents=None,
agent_type=None):
'''
@@ -316,28 +369,54 @@ def calculate_distribution(network_agents=None,
elif agent_type:
network_agents = [{'agent_type': agent_type}]
else:
return []
raise ValueError('Specify a distribution or a default agent type')
# Calculate the thresholds
total = sum(x.get('weight', 1) for x in network_agents)
acc = 0
for v in network_agents:
if 'ids' in v:
v['threshold'] = STATIC_THRESHOLD
continue
upper = acc + (v.get('weight', 1)/total)
v['threshold'] = [acc, upper]
acc = upper
return network_agents
def _serialize_distribution(network_agents):
d = _convert_agent_types(network_agents,
to_string=True)
def serialize_type(agent_type, known_modules=[], **kwargs):
if isinstance(agent_type, str):
return agent_type
known_modules += ['soil.agents']
return utils.serialize(agent_type, known_modules=known_modules, **kwargs)[1] # Get the name of the class
def serialize_distribution(network_agents, known_modules=[]):
'''
When serializing an agent distribution, remove the thresholds, in order
to avoid cluttering the YAML definition file.
'''
d = deepcopy(list(network_agents))
for v in d:
if 'threshold' in v:
del v['threshold']
v['agent_type'] = serialize_type(v['agent_type'],
known_modules=known_modules)
return d
def deserialize_type(agent_type, known_modules=[]):
if not isinstance(agent_type, str):
return agent_type
known = known_modules + ['soil.agents', 'soil.agents.custom' ]
agent_type = utils.deserializer(agent_type, known_modules=known)
return agent_type
def deserialize_distribution(ind, **kwargs):
d = deepcopy(ind)
for v in d:
v['agent_type'] = deserialize_type(v['agent_type'], **kwargs)
return d
@@ -352,33 +431,83 @@ def _validate_states(states, topology):
return states
def _convert_agent_types(ind, to_string=False):
def _convert_agent_types(ind, to_string=False, **kwargs):
'''Convenience method to allow specifying agents by class or class name.'''
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'] = agent_types[agent_type]
return d
if to_string:
return serialize_distribution(ind, **kwargs)
return deserialize_distribution(ind, **kwargs)
def _agent_from_distribution(distribution, value=-1):
def _agent_from_distribution(distribution, value=-1, agent_id=None):
"""Used in the initialization of agents given an agent distribution."""
if value < 0:
value = random.random()
for d in distribution:
for d in sorted(distribution, key=lambda x: x['threshold']):
threshold = d['threshold']
if value >= threshold[0] and value < threshold[1]:
state = {}
if 'state' in d:
state = deepcopy(d['state'])
return d['agent_type'], state
# Check if the definition matches by id (first) or by threshold
if not ((agent_id is not None and threshold == STATIC_THRESHOLD and agent_id in d['ids']) or \
(value >= threshold[0] and value < threshold[1])):
continue
state = {}
if 'state' in d:
state = deepcopy(d['state'])
return d['agent_type'], state
raise Exception('Distribution for value {} not found in: {}'.format(value, distribution))
class Geo(NetworkAgent):
'''In this type of network, nodes have a "pos" attribute.'''
def geo_search(self, radius, node=None, center=False, **kwargs):
'''Get a list of nodes whose coordinates are closer than *radius* to *node*.'''
node = as_node(node if node is not None else self)
G = self.subgraph(**kwargs)
pos = nx.get_node_attributes(G, 'pos')
if not pos:
return []
nodes, coords = list(zip(*pos.items()))
kdtree = KDTree(coords) # Cannot provide generator.
indices = kdtree.query_ball_point(pos[node], radius)
return [nodes[i] for i in indices if center or (nodes[i] != node)]
def select(agents, state_id=None, agent_type=None, ignore=None, iterator=False, **kwargs):
if state_id is not None:
try:
state_id = tuple(state_id)
except TypeError:
state_id = tuple([state_id])
if agent_type is not None:
try:
agent_type = tuple(agent_type)
except TypeError:
agent_type = tuple([agent_type])
def matches_all(agent):
if state_id is not None:
if agent.state.get('id', None) not in state_id:
return False
if agent_type is not None:
if not isinstance(agent, agent_type):
return False
state = agent.state
for k, v in kwargs.items():
if state.get(k, None) != v:
return False
return True
f = filter(matches_all, agents)
if ignore:
f = filter(lambda x: x not in ignore, f)
if iterator:
return f
return list(f)
from .BassModel import *
from .BigMarketModel import *
from .IndependentCascadeModel import *

View File

@@ -56,7 +56,7 @@ def read_csv(filename, keys=None, convert_types=False, **kwargs):
def convert_row(row):
row['value'] = utils.convert(row['value'], row['value_type'])
row['value'] = utils.deserialize(row['value_type'], row['value'])
return row
@@ -123,7 +123,7 @@ def get_count(df, *keys):
df = df[list(keys)]
counts = pd.DataFrame()
for key in df.columns.levels[0]:
g = df[key].apply(pd.Series.value_counts, axis=1).fillna(0)
g = df[[key]].apply(pd.Series.value_counts, axis=1).fillna(0)
for value, series in g.iteritems():
counts[key, value] = series
counts.columns = pd.MultiIndex.from_tuples(counts.columns)

View File

@@ -4,9 +4,11 @@ import time
import csv
import random
import simpy
import yaml
import tempfile
import pandas as pd
from copy import deepcopy
from collections import Counter
from networkx.readwrite import json_graph
import networkx as nx
@@ -14,8 +16,16 @@ import nxsim
from . import utils, agents, analysis, history
# These properties will be copied when pickling/unpickling the environment
_CONFIG_PROPS = [ 'name',
'states',
'default_state',
'interval',
'dry_run',
'dir_path',
]
class SoilEnvironment(nxsim.NetworkEnvironment):
class Environment(nxsim.NetworkEnvironment):
"""
The environment is key in a simulation. It contains the network topology,
a reference to network and environment agents, as well as the environment
@@ -23,7 +33,7 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
The environment parameters and the state of every agent can be accessed
both by using the environment as a dictionary or with the environment's
:meth:`soil.environment.SoilEnvironment.get` method.
:meth:`soil.environment.Environment.get` method.
"""
def __init__(self, name=None,
@@ -49,9 +59,11 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
self.dry_run = dry_run
self.interval = interval
self.dir_path = dir_path or tempfile.mkdtemp('soil-env')
self.get_path()
if not dry_run:
self.get_path()
self._history = history.History(name=self.name if not dry_run else None,
dir_path=self.dir_path)
dir_path=self.dir_path,
backup=True)
# Add environment agents first, so their events get
# executed before network agents
self.environment_agents = environment_agents or []
@@ -90,23 +102,45 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
@network_agents.setter
def network_agents(self, network_agents):
if not network_agents:
return
self._network_agents = network_agents
for ix in self.G.nodes():
agent, state = agents._agent_from_distribution(network_agents)
self.set_agent(ix, agent_type=agent, state=state)
self.init_agent(ix, agent_distribution=network_agents)
def init_agent(self, agent_id, agent_distribution):
node = self.G.nodes[agent_id]
init = False
state = dict(node)
agent_type = None
if 'agent_type' in self.states.get(agent_id, {}):
agent_type = self.states[agent_id]['agent_type']
elif 'agent_type' in node:
agent_type = node['agent_type']
elif 'agent_type' in self.default_state:
agent_type = self.default_state['agent_type']
if agent_type:
agent_type = agents.deserialize_type(agent_type)
elif agent_distribution:
agent_type, state = agents._agent_from_distribution(agent_distribution, agent_id=agent_id)
else:
utils.logger.debug('Skipping node {}'.format(agent_id))
return
return self.set_agent(agent_id, agent_type, state)
def set_agent(self, agent_id, agent_type, state=None):
node = self.G.nodes[agent_id]
defstate = deepcopy(self.default_state)
defstate = deepcopy(self.default_state) or {}
defstate.update(self.states.get(agent_id, {}))
defstate.update(node.get('state', {}))
if state:
defstate.update(state)
state = defstate
state.update(node.get('state', {}))
a = agent_type(environment=self,
agent_id=agent_id,
state=state)
a = None
if agent_type:
state = defstate
a = agent_type(environment=self,
agent_id=agent_id,
state=state)
node['agent'] = a
return a
@@ -117,13 +151,20 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
a['visible'] = True
return a
def add_edge(self, agent1, agent2, attrs=None):
return self.G.add_edge(agent1, agent2)
def add_edge(self, agent1, agent2, start=None, **attrs):
if hasattr(agent1, 'id'):
agent1 = agent1.id
if hasattr(agent2, 'id'):
agent2 = agent2.id
start = start or self.now
return self.G.add_edge(agent1, agent2, **attrs)
def run(self, *args, **kwargs):
self._save_state()
self.log_stats()
super().run(*args, **kwargs)
self._history.flush_cache()
self.log_stats()
def _save_state(self, now=None):
# for agent in self.agents:
@@ -193,8 +234,10 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
def get_agent(self, agent_id):
return self.G.node[agent_id]['agent']
def get_agents(self):
return list(self.agents)
def get_agents(self, nodes=None):
if nodes is None:
return list(self.agents)
return [self.G.node[i]['agent'] for i in nodes]
def dump_csv(self, dir_path=None):
csv_name = os.path.join(self.get_path(dir_path),
@@ -202,7 +245,7 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
with open(csv_name, 'w') as f:
cr = csv.writer(f)
cr.writerow(('agent_id', 't_step', 'key', 'value', 'value_type'))
cr.writerow(('agent_id', 't_step', 'key', 'value'))
for i in self.history_to_tuples():
cr.writerow(i)
@@ -295,19 +338,41 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
G.add_node(agent.id, **attributes)
return G
def stats(self):
stats = {}
stats['network'] = {}
stats['network']['n_nodes'] = self.G.number_of_nodes()
stats['network']['n_edges'] = self.G.number_of_edges()
c = Counter()
c.update(a.__class__.__name__ for a in self.network_agents)
stats['agents'] = {}
stats['agents']['model_count'] = dict(c)
c2 = Counter()
c2.update(a['id'] for a in self.network_agents)
stats['agents']['state_count'] = dict(c2)
stats['params'] = self.environment_params
return stats
def log_stats(self):
stats = self.stats()
utils.logger.info('Environment stats: \n{}'.format(yaml.dump(stats, default_flow_style=False)))
def __getstate__(self):
state = self.__dict__.copy()
state = {}
for prop in _CONFIG_PROPS:
state[prop] = self.__dict__[prop]
state['G'] = json_graph.node_link_data(self.G)
state['network_agents'] = agents._serialize_distribution(self.network_agents)
state['environment_agents'] = agents._convert_agent_types(self.environment_agents,
to_string=True)
del state['_queue']
state['environment_agents'] = self._env_agents
state['history'] = self._history
return state
def __setstate__(self, state):
self.__dict__ = state
for prop in _CONFIG_PROPS:
self.__dict__[prop] = state[prop]
self._env_agents = state['environment_agents']
self.G = json_graph.node_link_graph(state['G'])
self.network_agents = self.calculate_distribution(self._convert_agent_types(self.network_agents))
self.environment_agents = self._convert_agent_types(self.environment_agents)
return state
self._history = state['history']
SoilEnvironment = Environment

View File

@@ -3,7 +3,11 @@ import os
import pandas as pd
import sqlite3
import copy
from collections import UserDict, Iterable, namedtuple
import logging
logger = logging.getLogger(__name__)
from collections import UserDict, namedtuple
from . import utils
@@ -13,38 +17,33 @@ class History:
Store and retrieve values from a sqlite database.
"""
def __init__(self, db_path=None, name=None, dir_path=None, backup=True):
def __init__(self, db_path=None, name=None, dir_path=None, backup=False):
if db_path is None and name:
db_path = os.path.join(dir_path or os.getcwd(),
'{}.db.sqlite'.format(name))
if db_path is None:
db_path = ":memory:"
else:
if db_path:
if backup and os.path.exists(db_path):
newname = db_path + '.backup{}.sqlite'.format(time.time())
os.rename(db_path, newname)
else:
db_path = ":memory:"
self.db_path = db_path
self.db = db_path
with self.db:
logger.debug('Creating database {}'.format(self.db_path))
self.db.execute('''CREATE TABLE IF NOT EXISTS history (agent_id text, t_step int, key text, value text text)''')
self.db.execute('''CREATE TABLE IF NOT EXISTS value_types (key text, value_type text)''')
self.db.execute('''CREATE UNIQUE INDEX IF NOT EXISTS idx_history ON history (agent_id, t_step, key);''')
self._dtypes = {}
self._tups = []
def conversors(self, key):
"""Get the serializer and deserializer for a given key."""
if key not in self._dtypes:
self.read_types()
return self._dtypes[key]
@property
def db(self):
try:
self._db.cursor()
except sqlite3.ProgrammingError:
except (sqlite3.ProgrammingError, AttributeError):
self.db = None # Reset the database
return self._db
@@ -52,61 +51,96 @@ class History:
def db(self, db_path=None):
db_path = db_path or self.db_path
if isinstance(db_path, str):
logger.debug('Connecting to database {}'.format(db_path))
self._db = sqlite3.connect(db_path)
else:
self._db = db_path
@property
def dtypes(self):
self.read_types()
return {k:v[0] for k, v in self._dtypes.items()}
def save_tuples(self, tuples):
'''
Save a series of tuples, converting them to records if necessary
'''
self.save_records(Record(*tup) for tup in tuples)
def save_records(self, records):
with self.db:
for rec in records:
if not isinstance(rec, Record):
rec = Record(*rec)
if rec.key not in self._dtypes:
name = utils.name(rec.value)
serializer = utils.serializer(name)
deserializer = utils.deserializer(name)
self._dtypes[rec.key] = (name, serializer, deserializer)
self.db.execute("replace into value_types (key, value_type) values (?, ?)", (rec.key, name))
self.db.execute("replace into history(agent_id, t_step, key, value) values (?, ?, ?, ?)", (rec.agent_id, rec.t_step, rec.key, rec.value))
'''
Save a collection of records
'''
for record in records:
if not isinstance(record, Record):
record = Record(*record)
self.save_record(*record)
def save_record(self, *args, **kwargs):
self._tups.append(Record(*args, **kwargs))
def save_record(self, agent_id, t_step, key, value):
'''
Save a collection of records to the database.
Database writes are cached.
'''
value = self.convert(key, value)
self._tups.append(Record(agent_id=agent_id,
t_step=t_step,
key=key,
value=value))
if len(self._tups) > 100:
self.flush_cache()
def convert(self, key, value):
"""Get the serialized value for a given key."""
if key not in self._dtypes:
self.read_types()
if key not in self._dtypes:
name = utils.name(value)
serializer = utils.serializer(name)
deserializer = utils.deserializer(name)
self._dtypes[key] = (name, serializer, deserializer)
with self.db:
self.db.execute("replace into value_types (key, value_type) values (?, ?)", (key, name))
return self._dtypes[key][1](value)
def recover(self, key, value):
"""Get the deserialized value for a given key, and the serialized version."""
if key not in self._dtypes:
self.read_types()
if key not in self._dtypes:
raise ValueError("Unknown datatype for {} and {}".format(key, value))
return self._dtypes[key][2](value)
def flush_cache(self):
'''
Use a cache to save state changes to avoid opening a session for every change.
The cache will be flushed at the end of the simulation, and when history is accessed.
'''
self.save_records(self._tups)
logger.debug('Flushing cache {}'.format(self.db_path))
with self.db:
for rec in self._tups:
self.db.execute("replace into history(agent_id, t_step, key, value) values (?, ?, ?, ?)", (rec.agent_id, rec.t_step, rec.key, rec.value))
self._tups = list()
def to_tuples(self):
self.flush_cache()
with self.db:
res = self.db.execute("select agent_id, t_step, key, value from history ").fetchall()
for r in res:
agent_id, t_step, key, value = r
_, _ , des = self.conversors(key)
yield agent_id, t_step, key, des(value)
self.flush_cache()
with self.db:
res = self.db.execute("select agent_id, t_step, key, value from history ").fetchall()
for r in res:
agent_id, t_step, key, value = r
value = self.recover(key, value)
yield agent_id, t_step, key, value
def read_types(self):
with self.db:
res = self.db.execute("select key, value_type from value_types ").fetchall()
for k, v in res:
serializer = utils.serializer(v)
deserializer = utils.deserializer(v)
self._dtypes[k] = (v, serializer, deserializer)
with self.db:
res = self.db.execute("select key, value_type from value_types ").fetchall()
for k, v in res:
serializer = utils.serializer(v)
deserializer = utils.deserializer(v)
self._dtypes[k] = (v, serializer, deserializer)
def __getitem__(self, key):
self.flush_cache()
key = Key(*key)
agent_ids = [key.agent_id] if key.agent_id is not None else []
t_steps = [key.t_step] if key.t_step is not None else []
@@ -176,10 +210,20 @@ class History:
for k, v in self._dtypes.items():
if k in df_p:
dtype, _, deserial = v
df_p[k] = df_p[k].fillna(method='ffill').fillna(deserial()).astype(dtype)
df_p[k] = df_p[k].fillna(method='ffill').astype(dtype)
if t_steps:
df_p = df_p.reindex(t_steps, method='ffill')
return df_p.ffill()
def __getstate__(self):
state = dict(**self.__dict__)
del state['_db']
del state['_dtypes']
return state
def __setstate__(self, state):
self.__dict__ = state
self._dtypes = {}
class Records():

View File

@@ -1,8 +1,9 @@
import os
import time
import imp
import importlib
import sys
import yaml
import traceback
import networkx as nx
from networkx.readwrite import json_graph
from multiprocessing import Pool
@@ -12,11 +13,12 @@ import pickle
from nxsim import NetworkSimulation
from . import utils, environment, basestring, agents
from . import utils, basestring, agents
from .environment import Environment
from .utils import logger
class SoilSimulation(NetworkSimulation):
class Simulation(NetworkSimulation):
"""
Subclass of nsim.NetworkSimulation with three main differences:
1) agent type can be specified by name or by class.
@@ -43,22 +45,51 @@ class SoilSimulation(NetworkSimulation):
'agent_type_1'.
3) if no initial state is given, each node's state will be set
to `{'id': 0}`.
Parameters
---------
name : str, optional
name of the Simulation
topology : networkx.Graph instance, optional
network_params : dict
parameters used to create a topology with networkx, if no topology is given
network_agents : dict
definition of agents to populate the topology with
agent_type : NetworkAgent subclass, optional
Default type of NetworkAgent to use for nodes not specified in network_agents
states : list, optional
List of initial states corresponding to the nodes in the topology. Basic form is a list of integers
whose value indicates the state
dir_path : str, optional
Directory path where to save pickled objects
seed : str, optional
Seed to use for the random generator
num_trials : int, optional
Number of independent simulation runs
max_time : int, optional
Time how long the simulation should run
environment_params : dict, optional
Dictionary of globally-shared environmental parameters
environment_agents: dict, optional
Similar to network_agents. Distribution of Agents that control the environment
environment_class: soil.environment.Environment subclass, optional
Class for the environment. It defailts to soil.environment.Environment
load_module : str, module name, deprecated
If specified, soil will load the content of this module under 'soil.agents.custom'
"""
def __init__(self, name=None, topology=None, network_params=None,
network_agents=None, agent_type=None, states=None,
default_state=None, interval=1, dump=None, dry_run=False,
dir_path=None, num_trials=1, max_time=100,
agent_module=None, load_module=None, seed=None,
environment_agents=None, environment_params=None, **kwargs):
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)
load_module=None, seed=None,
environment_agents=None, environment_params=None,
environment_class=None, **kwargs):
self.seed = str(seed) or str(time.time())
self.load_module = load_module
self.topology = nx.Graph(topology)
self.network_params = network_params
self.name = name or 'UnnamedSimulation'
self.num_trials = num_trials
@@ -66,22 +97,31 @@ class SoilSimulation(NetworkSimulation):
self.default_state = default_state or {}
self.dir_path = dir_path or os.getcwd()
self.interval = interval
self.seed = str(seed) or str(time.time())
self.dump = dump
self.dry_run = dry_run
self.environment_params = environment_params or {}
if load_module:
path = sys.path + [self.dir_path, os.getcwd()]
f, fp, desc = imp.find_module(load_module, path)
imp.load_module('soil.agents.custom', f, fp, desc)
sys.path += [self.dir_path, os.getcwd()]
if topology is None:
topology = utils.load_network(network_params,
dir_path=self.dir_path)
elif isinstance(topology, basestring) or isinstance(topology, dict):
topology = json_graph.node_link_graph(topology)
self.topology = nx.Graph(topology)
self.environment_params = environment_params or {}
self.environment_class = utils.deserialize(environment_class,
known_modules=['soil.environment', ]) or Environment
environment_agents = environment_agents or []
self.environment_agents = agents._convert_agent_types(environment_agents)
self.environment_agents = agents._convert_agent_types(environment_agents,
known_modules=[self.load_module])
distro = agents.calculate_distribution(network_agents,
agent_type)
self.network_agents = agents._convert_agent_types(distro)
self.network_agents = agents._convert_agent_types(distro,
known_modules=[self.load_module])
self.states = agents._validate_states(states,
self.topology)
@@ -97,13 +137,17 @@ class SoilSimulation(NetworkSimulation):
p = Pool()
with utils.timer('simulation {}'.format(self.name)):
if parallel:
func = partial(self.run_trial, dry_run=dry_run or self.dry_run,
return_env=not parallel, **kwargs)
func = partial(self.run_trial_exceptions, dry_run=dry_run or self.dry_run,
return_env=True,
**kwargs)
for i in p.imap_unordered(func, range(self.num_trials)):
if isinstance(i, Exception):
logger.error('Trial failed:\n\t{}'.format(i.message))
continue
yield i
else:
for i in range(self.num_trials):
yield self.run_trial(i, dry_run=dry_run or self.dry_run, **kwargs)
yield self.run_trial(i, dry_run = dry_run or self.dry_run, **kwargs)
if not (dry_run or self.dry_run):
logger.info('Dumping results to {}'.format(self.dir_path))
self.dump_pickle(self.dir_path)
@@ -111,9 +155,9 @@ class SoilSimulation(NetworkSimulation):
else:
logger.info('NOT dumping results')
def get_env(self, trial_id=0, **kwargs):
opts = self.environment_params.copy()
env_name = '{}_trial_{}'.format(self.name, trial_id)
def get_env(self, trial_id = 0, **kwargs):
opts=self.environment_params.copy()
env_name='{}_trial_{}'.format(self.name, trial_id)
opts.update({
'name': env_name,
'topology': self.topology.copy(),
@@ -128,10 +172,10 @@ class SoilSimulation(NetworkSimulation):
'dir_path': self.dir_path,
})
opts.update(kwargs)
env = environment.SoilEnvironment(**opts)
env=self.environment_class(**opts)
return env
def run_trial(self, trial_id=0, until=None, return_env=True, **opts):
def run_trial(self, trial_id = 0, until = None, return_env = True, **opts):
"""Run a single trial of the simulation
Parameters
@@ -139,16 +183,27 @@ class SoilSimulation(NetworkSimulation):
trial_id : int
"""
# Set-up trial environment and graph
until = until or self.max_time
env = self.get_env(trial_id=trial_id, **opts)
until=until or self.max_time
env=self.get_env(trial_id = trial_id, **opts)
# Set up agents on nodes
with utils.timer('Simulation {} trial {}'.format(self.name, trial_id)):
env.run(until)
if self.dump and not self.dry_run:
with utils.timer('Dumping simulation {} trial {}'.format(self.name, trial_id)):
env.dump(formats=self.dump)
env.dump(formats = self.dump)
if return_env:
return env
def run_trial_exceptions(self, *args, **kwargs):
'''
A wrapper for run_trial that catches exceptions and returns them.
It is meant for async simulations
'''
try:
return self.run_trial(*args, **kwargs)
except Exception as ex:
c = ex.__cause__
c.message = ''.join(traceback.format_exception(type(c), c, c.__traceback__)[:])
return c
def to_dict(self):
return self.__getstate__()
@@ -156,39 +211,53 @@ class SoilSimulation(NetworkSimulation):
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
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,
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
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,
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'] = agents._serialize_distribution(self.network_agents)
state['environment_agents'] = agents._convert_agent_types(self.environment_agents,
to_string=True)
state={}
for k, v in self.__dict__.items():
if k[0] != '_':
state[k]=v
state['topology']=json_graph.node_link_data(self.topology)
state['network_agents']=agents.serialize_distribution(self.network_agents,
known_modules = [])
state['environment_agents']=agents.serialize_distribution(self.environment_agents,
known_modules = [])
state['environment_class']=utils.serialize(self.environment_class,
known_modules=['soil.environment'])[1] # func, name
if state['load_module'] is None:
del state['load_module']
return state
def __setstate__(self, state):
self.__dict__ = state
self.load_module = getattr(self, 'load_module', None)
if self.dir_path not in sys.path:
sys.path += [self.dir_path, os.getcwd()]
self.topology = json_graph.node_link_graph(state['topology'])
self.network_agents = agents.calculate_distribution(agents._convert_agent_types(self.network_agents))
self.environment_agents = agents._convert_agent_types(self.environment_agents)
self.environment_agents = agents._convert_agent_types(self.environment_agents,
known_modules=[self.load_module])
self.environment_class = utils.deserialize(self.environment_class,
known_modules=[self.load_module, 'soil.environment', ]) # func, name
return state
@@ -197,11 +266,11 @@ def from_config(config):
if len(config) > 1:
raise AttributeError('Provide only one configuration')
config = config[0][0]
sim = SoilSimulation(**config)
sim = Simulation(**config)
return sim
def run_from_config(*configs, results_dir='soil_output', dry_run=False, dump=None, timestamp=False, **kwargs):
def run_from_config(*configs, results_dir='soil_output', dump=None, timestamp=False, **kwargs):
for config_def in configs:
# logger.info("Found {} config(s)".format(len(ls)))
for config, _ in utils.load_config(config_def):
@@ -214,6 +283,8 @@ def run_from_config(*configs, results_dir='soil_output', dry_run=False, dump=Non
else:
sim_folder = name
dir_path = os.path.join(results_dir, sim_folder)
sim = SoilSimulation(dir_path=dir_path, dump=dump, **config)
if dump is not None:
config['dump'] = dump
sim = Simulation(dir_path=dir_path, **config)
logger.info('Dumping results to {} : {}'.format(sim.dir_path, sim.dump))
sim.run_simulation(**kwargs)

View File

@@ -1,8 +1,10 @@
import os
import ast
import sys
import yaml
import logging
import importlib
from time import time
import time
from glob import glob
from random import random
from copy import deepcopy
@@ -35,9 +37,14 @@ def load_network(network_params, dir_path=None):
return method(path, **kwargs)
net_args = network_params.copy()
net_type = net_args.pop('generator')
net_gen = net_args.pop('generator')
if dir_path not in sys.path:
sys.path.append(dir_path)
method = deserializer(net_gen,
known_modules=['networkx.generators',])
method = getattr(nx.generators, net_type)
return method(**net_args)
@@ -62,44 +69,94 @@ def load_config(config):
@contextmanager
def timer(name='task', pre="", function=logger.info, to_object=None):
start = time()
function('{}Starting {} at {}.'.format(pre, name, start))
start = time.time()
function('{}Starting {} at {}.'.format(pre, name,
time.strftime("%X", time.gmtime(start))))
yield start
end = time()
function('{}Finished {} in {} seconds'.format(pre, name, str(end-start)))
end = time.time()
function('{}Finished {} at {} in {} seconds'.format(pre, name,
time.strftime("%X", time.gmtime(end)),
str(end-start)))
if to_object:
to_object.start = start
to_object.end = end
def repr(v):
func = serializer(v)
tname = name(v)
return func(v), tname
builtins = importlib.import_module('builtins')
def name(v):
return type(v).__name__
def name(value, known_modules=[]):
'''Return a name that can be imported, to serialize/deserialize an object'''
if value is None:
return 'None'
if not isinstance(value, type): # Get the class name first
value = type(value)
tname = value.__name__
if hasattr(builtins, tname):
return tname
modname = value.__module__
if modname == '__main__':
return tname
if known_modules and modname in known_modules:
return tname
for kmod in known_modules:
if not kmod:
continue
module = importlib.import_module(kmod)
if hasattr(module, tname):
return tname
return '{}.{}'.format(modname, tname)
def serializer(type_):
if type_ == 'bool':
return lambda x: "true" if x else ""
if type_ != 'str' and hasattr(builtins, type_):
return repr
return lambda x: x
def deserializer(type_):
try:
# Check if it's a builtin type
module = importlib.import_module('builtins')
cls = getattr(module, type_)
except AttributeError:
# if not, separate module and class
def serialize(v, known_modules=[]):
'''Get a text representation of an object.'''
tname = name(v, known_modules=known_modules)
func = serializer(tname)
return func(v), tname
def deserializer(type_, known_modules=[]):
if type(type_) != str: # Already deserialized
return type_
if type_ == 'str':
return lambda x='': x
if type_ == 'None':
return lambda x=None: None
if hasattr(builtins, type_): # Check if it's a builtin type
cls = getattr(builtins, type_)
return lambda x=None: ast.literal_eval(x) if x is not None else cls()
# Otherwise, see if we can find the module and the class
modules = known_modules or []
options = []
for mod in modules:
if mod:
options.append((mod, type_))
if '.' in type_: # Fully qualified module
module, type_ = type_.rsplit(".", 1)
module = importlib.import_module(module)
cls = getattr(module, type_)
return cls
options.append ((module, type_))
errors = []
for modname, tname in options:
try:
module = importlib.import_module(modname)
cls = getattr(module, tname)
return getattr(cls, 'deserialize', cls)
except (ModuleNotFoundError, AttributeError) as ex:
errors.append((modname, tname, ex))
raise Exception('Could not find type {}. Tried: {}'.format(type_, errors))
def convert(value, type_):
return deserializer(type_)(value)
def deserialize(type_, value=None, **kwargs):
'''Get an object from a text representation'''
if not isinstance(type_, str):
return type_
des = deserializer(type_, **kwargs)
if value is None:
return des
return des(value)

View File

@@ -1,255 +0,0 @@
import random
import networkx as nx
from soil.agents import BaseAgent, FSM, state, default_state
from scipy.spatial import cKDTree as KDTree
global betweenness_centrality_global
global degree_centrality_global
betweenness_centrality_global = None
degree_centrality_global = None
class TerroristSpreadModel(FSM):
"""
Settings:
information_spread_intensity
terrorist_additional_influence
min_vulnerability (optional else zero)
max_vulnerability
prob_interaction
"""
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
global betweenness_centrality_global
global degree_centrality_global
if betweenness_centrality_global == None:
betweenness_centrality_global = nx.betweenness_centrality(self.global_topology)
if degree_centrality_global == None:
degree_centrality_global = nx.degree_centrality(self.global_topology)
self.information_spread_intensity = environment.environment_params['information_spread_intensity']
self.terrorist_additional_influence = environment.environment_params['terrorist_additional_influence']
self.prob_interaction = environment.environment_params['prob_interaction']
if self['id'] == self.civilian.id: # Civilian
self.initial_belief = random.uniform(0.00, 0.5)
elif self['id'] == self.terrorist.id: # Terrorist
self.initial_belief = random.uniform(0.8, 1.00)
elif self['id'] == self.leader.id: # Leader
self.initial_belief = 1.00
else:
raise Exception('Invalid state id: {}'.format(self['id']))
if 'min_vulnerability' in environment.environment_params:
self.vulnerability = random.uniform( environment.environment_params['min_vulnerability'], environment.environment_params['max_vulnerability'] )
else :
self.vulnerability = random.uniform( 0, environment.environment_params['max_vulnerability'] )
self.mean_belief = self.initial_belief
self.betweenness_centrality = betweenness_centrality_global[self.id]
self.degree_centrality = degree_centrality_global[self.id]
# self.state['radicalism'] = self.mean_belief
def count_neighboring_agents(self, state_id=None):
if isinstance(state_id, list):
return len(self.get_neighboring_agents(state_id))
else:
return len(super().get_agents(state_id, limit_neighbors=True))
def get_neighboring_agents(self, state_id=None):
if isinstance(state_id, list):
_list = []
for i in state_id:
_list += super().get_agents(i, limit_neighbors=True)
return [ neighbour for neighbour in _list if isinstance(neighbour, TerroristSpreadModel) ]
else:
_list = super().get_agents(state_id, limit_neighbors=True)
return [ neighbour for neighbour in _list if isinstance(neighbour, TerroristSpreadModel) ]
@state
def civilian(self):
if self.count_neighboring_agents() > 0:
neighbours = []
for neighbour in self.get_neighboring_agents():
if random.random() < self.prob_interaction:
neighbours.append(neighbour)
influence = sum( neighbour.degree_centrality for neighbour in neighbours )
mean_belief = sum( neighbour.mean_belief * neighbour.degree_centrality / influence for neighbour in neighbours )
self.initial_belief = self.mean_belief
mean_belief = mean_belief * self.information_spread_intensity + self.initial_belief * ( 1 - self.information_spread_intensity )
self.mean_belief = mean_belief * self.vulnerability + self.initial_belief * ( 1 - self.vulnerability )
if self.mean_belief >= 0.8:
return self.terrorist
@state
def leader(self):
self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
if self.count_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]) > 0:
for neighbour in self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]):
if neighbour.betweenness_centrality > self.betweenness_centrality:
return self.terrorist
@state
def terrorist(self):
if self.count_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]) > 0:
neighbours = self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id])
influence = sum( neighbour.degree_centrality for neighbour in neighbours )
mean_belief = sum( neighbour.mean_belief * neighbour.degree_centrality / influence for neighbour in neighbours )
self.initial_belief = self.mean_belief
self.mean_belief = mean_belief * self.vulnerability + self.initial_belief * ( 1 - self.vulnerability )
self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
if self.count_neighboring_agents(state_id=self.leader.id) == 0 and self.count_neighboring_agents(state_id=self.terrorist.id) > 0:
max_betweenness_centrality = self
for neighbour in self.get_neighboring_agents(state_id=self.terrorist.id):
if neighbour.betweenness_centrality > max_betweenness_centrality.betweenness_centrality:
max_betweenness_centrality = neighbour
if max_betweenness_centrality == self:
return self.leader
def add_edge(self, G, source, target):
G.add_edge(source.id, target.id, start=self.env._now)
def link_search(self, G, node, radius):
pos = nx.get_node_attributes(G, 'pos')
nodes, coords = list(zip(*pos.items()))
kdtree = KDTree(coords) # Cannot provide generator.
edge_indexes = kdtree.query_pairs(radius, 2)
_list = [ edge[int(not edge.index(node))] for edge in edge_indexes if node in edge ]
return [ G.nodes()[index]['agent'] for index in _list ]
def social_search(self, G, node, steps):
nodes = list(nx.ego_graph(G, node, radius=steps).nodes())
nodes.remove(node)
return [ G.nodes()[index]['agent'] for index in nodes ]
class TrainingAreaModel(FSM):
"""
Settings:
training_influence
min_vulnerability
Requires TerroristSpreadModel.
"""
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.training_influence = environment.environment_params['training_influence']
if 'min_vulnerability' in environment.environment_params:
self.min_vulnerability = environment.environment_params['min_vulnerability']
else: self.min_vulnerability = 0
@default_state
@state
def terrorist(self):
for neighbour in self.get_neighboring_agents():
if isinstance(neighbour, TerroristSpreadModel) and neighbour.vulnerability > self.min_vulnerability:
neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.training_influence )
class HavenModel(FSM):
"""
Settings:
haven_influence
min_vulnerability
max_vulnerability
Requires TerroristSpreadModel.
"""
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.haven_influence = environment.environment_params['haven_influence']
if 'min_vulnerability' in environment.environment_params:
self.min_vulnerability = environment.environment_params['min_vulnerability']
else: self.min_vulnerability = 0
self.max_vulnerability = environment.environment_params['max_vulnerability']
@state
def civilian(self):
for neighbour_agent in self.get_neighboring_agents():
if isinstance(neighbour_agent, TerroristSpreadModel) and neighbour_agent['id'] == neighbour_agent.civilian.id:
for neighbour in self.get_neighboring_agents():
if isinstance(neighbour, TerroristSpreadModel) and neighbour.vulnerability > self.min_vulnerability:
neighbour.vulnerability = neighbour.vulnerability * ( 1 - self.haven_influence )
return self.civilian
return self.terrorist
@state
def terrorist(self):
for neighbour in self.get_neighboring_agents():
if isinstance(neighbour, TerroristSpreadModel) and neighbour.vulnerability < self.max_vulnerability:
neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.haven_influence )
return self.terrorist
class TerroristNetworkModel(TerroristSpreadModel):
"""
Settings:
sphere_influence
vision_range
weight_social_distance
weight_link_distance
"""
def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.vision_range = environment.environment_params['vision_range']
self.sphere_influence = environment.environment_params['sphere_influence']
self.weight_social_distance = environment.environment_params['weight_social_distance']
self.weight_link_distance = environment.environment_params['weight_link_distance']
@state
def terrorist(self):
self.update_relationships()
return super().terrorist()
@state
def leader(self):
self.update_relationships()
return super().leader()
def update_relationships(self):
if self.count_neighboring_agents(state_id=self.civilian.id) == 0:
close_ups = self.link_search(self.global_topology, self.id, self.vision_range)
step_neighbours = self.social_search(self.global_topology, self.id, self.sphere_influence)
search = list(set(close_ups).union(step_neighbours))
neighbours = self.get_neighboring_agents()
search = [item for item in search if not item in neighbours and isinstance(item, TerroristNetworkModel)]
for agent in search:
social_distance = 1 / self.shortest_path_length(self.global_topology, self.id, agent.id)
spatial_proximity = ( 1 - self.get_distance(self.global_topology, self.id, agent.id) )
prob_new_interaction = self.weight_social_distance * social_distance + self.weight_link_distance * spatial_proximity
if agent['id'] == agent.civilian.id and random.random() < prob_new_interaction:
self.add_edge(self.global_topology, self, agent)
break
def get_distance(self, G, source, target):
source_x, source_y = nx.get_node_attributes(G, 'pos')[source]
target_x, target_y = nx.get_node_attributes(G, 'pos')[target]
dx = abs( source_x - target_x )
dy = abs( source_y - target_y )
return ( dx ** 2 + dy ** 2 ) ** ( 1 / 2 )
def shortest_path_length(self, G, source, target):
try:
return nx.shortest_path_length(G, source, target)
except nx.NetworkXNoPath:
return float('inf')

View File

@@ -19,7 +19,7 @@ from xml.etree.ElementTree import tostring
from tornado.concurrent import run_on_executor
from concurrent.futures import ThreadPoolExecutor
from ..simulation import SoilSimulation
from ..simulation import Simulation
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
@@ -168,7 +168,7 @@ class SocketHandler(tornado.websocket.WebSocketHandler):
@run_on_executor
def nonblocking(self, config):
simulation = SoilSimulation(**config)
simulation = Simulation(**config)
return simulation.run()
@tornado.gen.coroutine
@@ -271,4 +271,4 @@ def main():
parser.add_argument('--verbose', '-v', help='verbose mode', action='store_true')
args = parser.parse_args()
run(name=args.name, port=(args.port[0] if isinstance(args.port, list) else args.port), verbose=args.verbose)
run(name=args.name, port=(args.port[0] if isinstance(args.port, list) else args.port), verbose=args.verbose)

View File

@@ -0,0 +1 @@
pytest

12
tests/test.gexf Normal file
View File

@@ -0,0 +1,12 @@
<?xml version='1.0' encoding='utf-8'?>
<gexf version="1.2" xmlns="http://www.gexf.net/1.2draft" xmlns:viz="http://www.gexf.net/1.2draft/viz" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.w3.org/2001/XMLSchema-instance">
<graph defaultedgetype="undirected" mode="static">
<nodes>
<node id="0" label="0" />
<node id="1" label="1" />
</nodes>
<edges>
<edge id="0" source="0" target="1" />
</edges>
</graph>
</gexf>

53
tests/test_examples.py Normal file
View File

@@ -0,0 +1,53 @@
from unittest import TestCase
import os
from os.path import join
from soil import utils, simulation
ROOT = os.path.abspath(os.path.dirname(__file__))
EXAMPLES = join(ROOT, '..', 'examples')
FORCE_TESTS = os.environ.get('FORCE_TESTS', '')
class TestExamples(TestCase):
pass
def make_example_test(path, config):
def wrapped(self):
root = os.getcwd()
os.chdir(os.path.dirname(path))
s = simulation.from_config(config)
iterations = s.max_time * s.num_trials
if iterations > 1000:
s.max_time = 100
s.num_trials = 1
if config.get('skip_test', False) and not FORCE_TESTS:
self.skipTest('Example ignored.')
envs = s.run_simulation(dry_run=True)
assert envs
for env in envs:
assert env
try:
n = config['network_params']['n']
assert len(list(env.network_agents)) == n
assert env.now > 2 # It has run
assert env.now <= config['max_time'] # But not further than allowed
except KeyError:
pass
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(TestExamples, p.__name__, p)
del p
add_example_tests()

View File

@@ -116,18 +116,41 @@ class TestHistory(TestCase):
db_path = os.path.join(DBROOT, 'test')
h = history.History(db_path=db_path)
h.save_tuples(tuples)
h.flush_cache()
assert os.path.exists(db_path)
# Recover the data
recovered = history.History(db_path=db_path, backup=False)
recovered = history.History(db_path=db_path)
assert recovered['a_1', 0, 'id'] == 'v'
assert recovered['a_1', 4, 'id'] == 'e'
# Using the same name should create a backup copy
# Using backup=True should create a backup copy, and initialize an empty history
newhistory = history.History(db_path=db_path, backup=True)
backuppaths = glob(db_path + '.backup*.sqlite')
assert len(backuppaths) == 1
backuppath = backuppaths[0]
assert newhistory._db_path == h._db_path
assert newhistory.db_path == h.db_path
assert os.path.exists(backuppath)
assert not len(newhistory[None, None, None])
assert len(newhistory[None, None, None]) == 0
def test_history_tuples(self):
"""
The data recovered should be equal to the one recorded.
"""
tuples = (
('a_1', 0, 'id', 'v'),
('a_1', 1, 'id', 'a'),
('a_1', 2, 'id', 'l'),
('a_1', 3, 'id', 'u'),
('a_1', 4, 'id', 'e'),
('env', 1, 'prob', 1),
('env', 2, 'prob', 2),
('env', 3, 'prob', 3),
('a_2', 7, 'finished', True),
)
h = history.History()
h.save_tuples(tuples)
recovered = list(h.to_tuples())
assert recovered
for i in recovered:
assert i in tuples

View File

@@ -2,18 +2,23 @@ from unittest import TestCase
import os
import yaml
import pickle
import networkx as nx
from functools import partial
from os.path import join
from soil import simulation, environment, agents, utils
from soil import simulation, Environment, agents, utils, history
ROOT = os.path.abspath(os.path.dirname(__file__))
EXAMPLES = join(ROOT, '..', 'examples')
class CustomAgent(agents.BaseAgent):
def step(self):
self.state['neighbors'] = self.count_agents(state_id=0,
limit_neighbors=True)
class TestMain(TestCase):
def test_load_graph(self):
@@ -127,10 +132,6 @@ class TestMain(TestCase):
def test_custom_agent(self):
"""Allow for search of neighbors with a certain state_id"""
class CustomAgent(agents.BaseAgent):
def step(self):
self.state['neighbors'] = self.count_agents(state_id=0,
limit_neighbors=True)
config = {
'dry_run': True,
'network_params': {
@@ -188,8 +189,6 @@ class TestMain(TestCase):
recovered = yaml.load(serial)
with utils.timer('deleting'):
del recovered['topology']
del recovered['load_module']
del recovered['dry_run']
assert config == recovered
def test_configuration_changes(self):
@@ -197,25 +196,17 @@ class TestMain(TestCase):
The configuration should not change after running
the simulation.
"""
config = utils.load_file('examples/complete.yml')[0]
config = utils.load_file(join(EXAMPLES, 'complete.yml'))[0]
s = simulation.from_config(config)
s.dry_run = True
for i in range(5):
s.run_simulation(dry_run=True)
nconfig = s.to_dict()
del nconfig['topology']
del nconfig['dry_run']
del nconfig['load_module']
assert config == nconfig
def test_examples(self):
"""
Make sure all examples in the examples folder are correct
"""
pass
def test_row_conversion(self):
env = environment.SoilEnvironment(dry_run=True)
env = Environment(dry_run=True)
env['test'] = 'test_value'
res = list(env.history_to_tuples())
@@ -234,7 +225,7 @@ class TestMain(TestCase):
from geometric models. We should work around it.
"""
G = nx.random_geometric_graph(20, 0.1)
env = environment.SoilEnvironment(topology=G, dry_run=True)
env = Environment(topology=G, dry_run=True)
env.dump_gexf('/tmp/dump-gexf')
def test_save_graph(self):
@@ -245,7 +236,7 @@ class TestMain(TestCase):
'''
G = nx.cycle_graph(5)
distribution = agents.calculate_distribution(None, agents.BaseAgent)
env = environment.SoilEnvironment(topology=G, network_agents=distribution, dry_run=True)
env = Environment(topology=G, network_agents=distribution, dry_run=True)
env[0, 0, 'testvalue'] = 'start'
env[0, 10, 'testvalue'] = 'finish'
nG = env.history_to_graph()
@@ -253,33 +244,95 @@ class TestMain(TestCase):
assert ('start', 0, 10) in values
assert ('finish', 10, None) in values
def test_serialize_class(self):
ser, name = utils.serialize(agents.BaseAgent)
assert name == 'soil.agents.BaseAgent'
assert ser == agents.BaseAgent
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(dry_run=True)
assert envs
for env in envs:
assert env
try:
n = config['network_params']['n']
assert len(env.get_agents()) == n
except KeyError:
pass
os.chdir(root)
return wrapped
ser, name = utils.serialize(CustomAgent)
assert name == 'test_main.CustomAgent'
assert ser == CustomAgent
pickle.dumps(ser)
def test_serialize_builtin_types(self):
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
for i in [1, None, True, False, {}, [], list(), dict()]:
ser, name = utils.serialize(i)
assert type(ser) == str
des = utils.deserialize(name, ser)
assert i == des
def test_serialize_agent_type(self):
'''A class from soil.agents should be serialized without the module part'''
ser = agents.serialize_type(CustomAgent)
assert ser == 'test_main.CustomAgent'
ser = agents.serialize_type(agents.BaseAgent)
assert ser == 'BaseAgent'
pickle.dumps(ser)
def test_deserialize_agent_distribution(self):
agent_distro = [
{
'agent_type': 'CounterModel',
'weight': 1
},
{
'agent_type': 'test_main.CustomAgent',
'weight': 2
},
]
converted = agents.deserialize_distribution(agent_distro)
assert converted[0]['agent_type'] == agents.CounterModel
assert converted[1]['agent_type'] == CustomAgent
pickle.dumps(converted)
add_example_tests()
def test_serialize_agent_distribution(self):
agent_distro = [
{
'agent_type': agents.CounterModel,
'weight': 1
},
{
'agent_type': CustomAgent,
'weight': 2
},
]
converted = agents.serialize_distribution(agent_distro)
assert converted[0]['agent_type'] == 'CounterModel'
assert converted[1]['agent_type'] == 'test_main.CustomAgent'
pickle.dumps(converted)
def test_pickle_agent_environment(self):
env = Environment(name='Test')
a = agents.BaseAgent(environment=env, agent_id=25)
a['key'] = 'test'
pickled = pickle.dumps(a)
recovered = pickle.loads(pickled)
assert recovered.env.name == 'Test'
assert recovered['key'] == 'test'
assert recovered['key', 0] == 'test'
def test_history(self):
'''Test storing in and retrieving from history (sqlite)'''
h = history.History()
h.save_record(agent_id=0, t_step=0, key="test", value="hello")
assert h[0, 0, "test"] == "hello"
def test_subgraph(self):
'''An agent should be able to subgraph the global topology'''
G = nx.Graph()
G.add_node(3)
G.add_edge(1, 2)
distro = agents.calculate_distribution(agent_type=agents.NetworkAgent)
env = Environment(name='Test', topology=G, network_agents=distro)
lst = list(env.network_agents)
a2 = env.get_agent(2)
a3 = env.get_agent(3)
assert len(a2.subgraph(limit_neighbors=True)) == 2
assert len(a3.subgraph(limit_neighbors=True)) == 1
assert len(a3.subgraph(limit_neighbors=True, center=False)) == 0
assert len(a3.subgraph(agent_type=agents.NetworkAgent)) == 3