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Code Issues Releases Wiki Activity

0.13.8

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This commit is contained in:
J. Fernando Sánchez 2019-02-19 21:17:19 +01:00
parent 65f6aa72f3
commit a3ea434f23
14 changed files with 414 additions and 335 deletions
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12
CHANGELOG.md
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@ -3,9 +3,17 @@ 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).
## [Unreleased]
## [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]
@ -16,4 +24,4 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
* 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`.
* `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`.

5
MANIFEST.in
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@ -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]

10
examples/newsspread/NewsSpread.yml
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@ -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

208
examples/terrorism/TerroristNetworkModel.py Normal file
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@ -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')

1
soil/web/TerroristNetworkModel.yml → examples/terrorism/TerroristNetworkModel.yml
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.

1
requirements.txt
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@ -5,3 +5,4 @@ numpy
matplotlib
pyyaml
pandas
scipy

2
soil/VERSION
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@ -1 +1 @@
0.13.7
0.13.8

14
soil/agents/CounterModel.py
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@ -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))

199
soil/agents/__init__.py
View File

@ -10,6 +10,7 @@ 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
@ -17,6 +18,12 @@ from functools import wraps
from .. import utils, history
def as_node(agent):
if isinstance(agent, BaseAgent):
return agent.id
return agent
class BaseAgent(nxsim.BaseAgent):
"""
A special simpy BaseAgent that keeps track of its state history.
@ -46,8 +53,7 @@ class BaseAgent(nxsim.BaseAgent):
if not hasattr(self, 'level'):
self.level = logging.DEBUG
self.logger = logging.getLogger('{}.{}'.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
@ -134,43 +140,21 @@ class BaseAgent(nxsim.BaseAgent):
def step(self):
pass
def count_agents(self, state_id=None, limit_neighbors=False):
def count_agents(self, **kwargs):
return len(list(self.get_agents(**kwargs)))
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, agent_type=None, limit_neighbors=False, iterator=False, **kwargs):
agents = self.env.agents
if limit_neighbors:
agents = super().get_agents(state_id, limit_neighbors)
def matches_all(agent):
if state_id is not None:
if agent.state.get('id', None) != state_id:
return False
if agent_type is not None:
if type(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 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)
@ -208,31 +192,76 @@ class BaseAgent(nxsim.BaseAgent):
self._state = state['_state']
self.env = state['environment']
def add_edge(self, node1, node2, **attrs):
node1 = as_node(node1)
node2 = as_node(node2)
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.
'''
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)
@wraps(func)
def func_wrapper(self):
next_state = func(self)
when = None
if next_state is None:
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)
class NetworkAgent(BaseAgent):
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):
@ -340,7 +369,7 @@ 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)
@ -427,6 +456,58 @@ def _agent_from_distribution(distribution, value=-1, agent_id=None):
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 *

17
soil/environment.py
View File

@ -102,8 +102,7 @@ class Environment(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():
self.init_agent(ix, agent_distribution=network_agents)
@ -124,6 +123,9 @@ class Environment(nxsim.NetworkEnvironment):
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):
@ -149,12 +151,13 @@ class Environment(nxsim.NetworkEnvironment):
a['visible'] = True
return a
def add_edge(self, agent1, agent2, attrs=None):
def add_edge(self, agent1, agent2, start=None, **attrs):
if hasattr(agent1, 'id'):
agent1 = agent1.id
if hasattr(agent2, 'id'):
agent2 = agent2.id
return self.G.add_edge(agent1, agent2)
start = start or self.now
return self.G.add_edge(agent1, agent2, **attrs)
def run(self, *args, **kwargs):
self._save_state()
@ -231,8 +234,10 @@ class Environment(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),

2
soil/utils.py
View File

@ -147,7 +147,7 @@ def deserializer(type_, known_modules=[]):
module = importlib.import_module(modname)
cls = getattr(module, tname)
return getattr(cls, 'deserialize', cls)
except (ImportError, AttributeError) as ex:
except (ModuleNotFoundError, AttributeError) as ex:
errors.append((modname, tname, ex))
raise Exception('Could not find type {}. Tried: {}'.format(type_, errors))

255
soil/web/TerroristNetworkModel.py
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')

7
tests/test_examples.py
View File

@ -7,6 +7,8 @@ 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
@ -19,7 +21,10 @@ def make_example_test(path, config):
s = simulation.from_config(config)
iterations = s.max_time * s.num_trials
if iterations > 1000:
self.skipTest('This example would probably take too long')
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:

16
tests/test_main.py
View File

@ -320,3 +320,19 @@ class TestMain(TestCase):
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