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Code Issues Releases Wiki Activity

Large set of changes for v0.30

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The examples weren't being properly tested in the last commit. When we fixed
that a lot of bugs in the new implementation of environment and agent were
found, which accounts for most of these changes.

The main difference is the mechanism to load simulations from a configuration
file. For that to work, we had to rework our module loading code in
`serialization` and add a `source_file` attribute to configurations (and
simulations, for that matter).
This commit is contained in:
J. Fernando Sánchez
2023-04-14 19:41:24 +02:00
parent 73282530fd
commit feab0ba79e
36 changed files with 739 additions and 875 deletions
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72
examples/terrorism/TerroristNetworkModel_sim.py
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@@ -5,7 +5,6 @@ from soil.parameters import *
class TerroristEnvironment(Environment):
generator: function = nx.random_geometric_graph
n: Integer = 100
radius: Float = 0.2
@@ -37,8 +36,11 @@ class TerroristEnvironment(Environment):
TerroristNetworkModel.w(state_id='leader'),
TrainingAreaModel,
HavenModel
], [self.ratio_civil, self.ratio_leader, self.ratio_trainig, self.ratio_heaven])
], [self.ratio_civil, self.ratio_leader, self.ratio_training, self.ratio_haven])
@staticmethod
def generator(*args, **kwargs):
return nx.random_geometric_graph(*args, **kwargs)
class TerroristSpreadModel(FSM, Geo):
"""
@@ -50,10 +52,13 @@ class TerroristSpreadModel(FSM, Geo):
min_vulnerability (optional else zero)
max_vulnerability
prob_interaction
"""
information_spread_intensity = 0.1
terrorist_additional_influence = 0.1
min_vulnerability = 0
max_vulnerability = 1
def init(self):
if self.state_id == self.civilian.id: # Civilian
self.mean_belief = self.model.random.uniform(0.00, 0.5)
@@ -75,7 +80,7 @@ class TerroristSpreadModel(FSM, Geo):
if len(neighbours) > 0:
# Only interact with some of the neighbors
interactions = list(
n for n in neighbours if self.random.random() <= self.prob_interaction
n for n in neighbours if self.random.random() <= self.model.prob_interaction
)
influence = sum(self.degree(i) for i in interactions)
mean_belief = sum(
@@ -121,7 +126,7 @@ class TerroristSpreadModel(FSM, Geo):
)
# Check if there are any leaders in the group
leaders = list(filter(lambda x: x.state.id == self.leader.id, neighbours))
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
@@ -132,12 +137,11 @@ class TerroristSpreadModel(FSM, Geo):
def ego_search(self, steps=1, center=False, agent=None, **kwargs):
"""Get a list of nodes in the ego network of *node* of radius *steps*"""
node = agent.node
node = agent.node_id
G = self.subgraph(**kwargs)
return nx.ego_graph(G, node, center=center, radius=steps).nodes()
def degree(self, agent, force=False):
node = agent.node
if (
force
or (not hasattr(self.model, "_degree"))
@@ -145,10 +149,9 @@ class TerroristSpreadModel(FSM, Geo):
):
self.model._degree = nx.degree_centrality(self.G)
self.model._last_step = self.now
return self.model._degree[node]
return self.model._degree[agent.node_id]
def betweenness(self, agent, force=False):
node = agent.node
if (
force
or (not hasattr(self.model, "_betweenness"))
@@ -156,7 +159,7 @@ class TerroristSpreadModel(FSM, Geo):
):
self.model._betweenness = nx.betweenness_centrality(self.G)
self.model._last_step = self.now
return self.model._betweenness[node]
return self.model._betweenness[agent.node_id]
class TrainingAreaModel(FSM, Geo):
@@ -169,13 +172,12 @@ class TrainingAreaModel(FSM, Geo):
Requires TerroristSpreadModel.
"""
def __init__(self, model=None, unique_id=0, state=()):
super().__init__(model=model, unique_id=unique_id, state=state)
self.training_influence = model.environment_params["training_influence"]
if "min_vulnerability" in model.environment_params:
self.min_vulnerability = model.environment_params["min_vulnerability"]
else:
self.min_vulnerability = 0
training_influence = 0.1
min_vulnerability = 0
def init(self):
self.mean_believe = 1
self.vulnerability = 0
@default_state
@state
@@ -199,18 +201,19 @@ class HavenModel(FSM, Geo):
Requires TerroristSpreadModel.
"""
def __init__(self, model=None, unique_id=0, state=()):
super().__init__(model=model, unique_id=unique_id, state=state)
self.haven_influence = model.environment_params["haven_influence"]
if "min_vulnerability" in model.environment_params:
self.min_vulnerability = model.environment_params["min_vulnerability"]
else:
self.min_vulnerability = 0
self.max_vulnerability = model.environment_params["max_vulnerability"]
min_vulnerability = 0
haven_influence = 0.1
max_vulnerability = 0.5
def init(self):
self.mean_believe = 0
self.vulnerability = 0
def get_occupants(self, **kwargs):
return self.get_neighbors(agent_class=TerroristSpreadModel, **kwargs)
return self.get_neighbors(agent_class=TerroristSpreadModel,
**kwargs)
@default_state
@state
def civilian(self):
civilians = self.get_occupants(state_id=self.civilian.id)
@@ -246,13 +249,10 @@ class TerroristNetworkModel(TerroristSpreadModel):
weight_link_distance
"""
def __init__(self, model=None, unique_id=0, state=()):
super().__init__(model=model, unique_id=unique_id, state=state)
self.vision_range = model.environment_params["vision_range"]
self.sphere_influence = model.environment_params["sphere_influence"]
self.weight_social_distance = model.environment_params["weight_social_distance"]
self.weight_link_distance = model.environment_params["weight_link_distance"]
sphere_influence: float
vision_range: float
weight_social_distance: float
weight_link_distance: float
@state
def terrorist(self):
@@ -316,8 +316,8 @@ sim = Simulation(
num_trials=1,
name="TerroristNetworkModel_sim",
max_steps=150,
skip_test=True,
dry_run=True,
skip_test=False,
dump=False,
)
# TODO: integrate visualization