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Big refactor v0.30

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All test pass, except for the TestConfig suite, which is not too critical as the
plan for this version onwards is to avoid configuration as much as possible.
This commit is contained in:
J. Fernando Sánchez
2023-04-09 04:19:24 +02:00
parent 2869b1e1e6
commit 73282530fd
45 changed files with 721 additions and 82265 deletions
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133
examples/newsspread/NewsSpread.yml
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@@ -1,133 +0,0 @@
---
default_state: {}
environment_agents: []
environment_params:
prob_neighbor_spread: 0.0
prob_tv_spread: 0.01
interval: 1
max_steps: 300
name: Sim_all_dumb
network_agents:
- agent_class: newsspread.DumbViewer
state:
has_tv: false
weight: 1
- agent_class: newsspread.DumbViewer
state:
has_tv: true
weight: 1
network_params:
generator: barabasi_albert_graph
n: 500
m: 5
num_trials: 50
---
default_state: {}
environment_agents: []
environment_params:
prob_neighbor_spread: 0.0
prob_tv_spread: 0.01
interval: 1
max_steps: 300
name: Sim_half_herd
network_agents:
- agent_class: newsspread.DumbViewer
state:
has_tv: false
weight: 1
- agent_class: newsspread.DumbViewer
state:
has_tv: true
weight: 1
- agent_class: newsspread.HerdViewer
state:
has_tv: false
weight: 1
- agent_class: newsspread.HerdViewer
state:
has_tv: true
weight: 1
network_params:
generator: barabasi_albert_graph
n: 500
m: 5
num_trials: 50
---
default_state: {}
environment_agents: []
environment_params:
prob_neighbor_spread: 0.0
prob_tv_spread: 0.01
interval: 1
max_steps: 300
name: Sim_all_herd
network_agents:
- agent_class: newsspread.HerdViewer
state:
has_tv: true
state_id: neutral
weight: 1
- agent_class: newsspread.HerdViewer
state:
has_tv: true
state_id: neutral
weight: 1
network_params:
generator: barabasi_albert_graph
n: 500
m: 5
num_trials: 50
---
default_state: {}
environment_agents: []
environment_params:
prob_neighbor_spread: 0.0
prob_tv_spread: 0.01
prob_neighbor_cure: 0.1
interval: 1
max_steps: 300
name: Sim_wise_herd
network_agents:
- agent_class: newsspread.HerdViewer
state:
has_tv: true
state_id: neutral
weight: 1
- agent_class: newsspread.WiseViewer
state:
has_tv: true
weight: 1
network_params:
generator: barabasi_albert_graph
n: 500
m: 5
num_trials: 50
---
default_state: {}
environment_agents: []
environment_params:
prob_neighbor_spread: 0.0
prob_tv_spread: 0.01
prob_neighbor_cure: 0.1
interval: 1
max_steps: 300
name: Sim_all_wise
network_agents:
- agent_class: newsspread.WiseViewer
state:
has_tv: true
state_id: neutral
weight: 1
- agent_class: newsspread.WiseViewer
state:
has_tv: true
weight: 1
network_params:
generator: barabasi_albert_graph
n: 500
m: 5
network_params:
generator: barabasi_albert_graph
n: 500
m: 5
num_trials: 50

87
examples/newsspread/newsspread.py
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@@ -1,87 +0,0 @@
from soil.agents import FSM, NetworkAgent, state, default_state, prob
import logging
class DumbViewer(FSM, NetworkAgent):
"""
A viewer that gets infected via TV (if it has one) and tries to infect
its neighbors once it's infected.
"""
prob_neighbor_spread = 0.5
prob_tv_spread = 0.1
has_been_infected = False
@default_state
@state
def neutral(self):
if self["has_tv"]:
if self.prob(self.model["prob_tv_spread"]):
return self.infected
if self.has_been_infected:
return self.infected
@state
def infected(self):
for neighbor in self.get_neighbors(state_id=self.neutral.id):
if self.prob(self.model["prob_neighbor_spread"]):
neighbor.infect()
def infect(self):
"""
This is not a state. It is a function that other agents can use to try to
infect this agent. DumbViewer always gets infected, but other agents like
HerdViewer might not become infected right away
"""
self.has_been_infected = True
class HerdViewer(DumbViewer):
"""
A viewer whose probability of infection depends on the state of its neighbors.
"""
def infect(self):
"""Notice again that this is NOT a state. See DumbViewer.infect for reference"""
infected = self.count_neighbors(state_id=self.infected.id)
total = self.count_neighbors()
prob_infect = self.model["prob_neighbor_spread"] * infected / total
self.debug("prob_infect", prob_infect)
if self.prob(prob_infect):
self.has_been_infected = True
class WiseViewer(HerdViewer):
"""
A viewer that can change its mind.
"""
defaults = {
"prob_neighbor_spread": 0.5,
"prob_neighbor_cure": 0.25,
"prob_tv_spread": 0.1,
}
@state
def cured(self):
prob_cure = self.model["prob_neighbor_cure"]
for neighbor in self.get_neighbors(state_id=self.infected.id):
if self.prob(prob_cure):
try:
neighbor.cure()
except AttributeError:
self.debug("Viewer {} cannot be cured".format(neighbor.id))
def cure(self):
self.has_been_cured = True
@state
def infected(self):
if self.has_been_cured:
return self.cured
cured = max(self.count_neighbors(self.cured.id), 1.0)
infected = max(self.count_neighbors(self.infected.id), 1.0)
prob_cure = self.model["prob_neighbor_cure"] * (cured / infected)
if self.prob(prob_cure):
return self.cured

129
examples/newsspread/newsspread_sim.py Normal file
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from soil.agents import FSM, NetworkAgent, state, default_state, prob
from soil.parameters import *
import logging
from soil.environment import Environment
class DumbViewer(FSM, NetworkAgent):
"""
A viewer that gets infected via TV (if it has one) and tries to infect
its neighbors once it's infected.
"""
has_been_infected: bool = False
has_tv: bool = False
@default_state
@state
def neutral(self):
if self.has_tv:
if self.prob(self.get("prob_tv_spread")):
return self.infected
if self.has_been_infected:
return self.infected
@state
def infected(self):
for neighbor in self.get_neighbors(state_id=self.neutral.id):
if self.prob(self.get("prob_neighbor_spread")):
neighbor.infect()
def infect(self):
"""
This is not a state. It is a function that other agents can use to try to
infect this agent. DumbViewer always gets infected, but other agents like
HerdViewer might not become infected right away
"""
self.has_been_infected = True
class HerdViewer(DumbViewer):
"""
A viewer whose probability of infection depends on the state of its neighbors.
"""
def infect(self):
"""Notice again that this is NOT a state. See DumbViewer.infect for reference"""
infected = self.count_neighbors(state_id=self.infected.id)
total = self.count_neighbors()
prob_infect = self.get("prob_neighbor_spread") * infected / total
self.debug("prob_infect", prob_infect)
if self.prob(prob_infect):
self.has_been_infected = True
class WiseViewer(HerdViewer):
"""
A viewer that can change its mind.
"""
@state
def cured(self):
prob_cure = self.get("prob_neighbor_cure")
for neighbor in self.get_neighbors(state_id=self.infected.id):
if self.prob(prob_cure):
try:
neighbor.cure()
except AttributeError:
self.debug("Viewer {} cannot be cured".format(neighbor.id))
def cure(self):
self.has_been_cured = True
@state
def infected(self):
if self.has_been_cured:
return self.cured
cured = max(self.count_neighbors(self.cured.id), 1.0)
infected = max(self.count_neighbors(self.infected.id), 1.0)
prob_cure = self.get("prob_neighbor_cure") * (cured / infected)
if self.prob(prob_cure):
return self.cured
class NewsSpread(Environment):
ratio_dumb: probability = 1,
ratio_herd: probability = 0,
ratio_wise: probability = 0,
prob_tv_spread: probability = 0.1,
prob_neighbor_spread: probability = 0.1,
prob_neighbor_cure: probability = 0.05,
def init(self):
self.populate_network([DumbViewer, HerdViewer, WiseViewer], [self.ratio_dumb, self.ratio_herd, self.ratio_wise])
from itertools import permutations
from soil import Simulation
# We want to investigate the effect of different agent distributions on the spread of news.
# To do that, we will run different simulations, with a varying ratio of DumbViewers, HerdViewers, and WiseViewers
# Because the effect of these agents might also depend on the network structure, we will run our simulations on two different networks:
# one with a small-world structure and one with a connected structure.
for [r1, r2, r3] in permutations([0, 0.5, 1.0], 3):
for (generator, netparams) in {
"barabasi_albert_graph": {"m": 5},
"erdos_renyi_graph": {"p": 0.1},
}.items():
print(r1, r2, r3, generator)
# Create new simulation
netparams["n"] = 500
sim = Simulation(
model=NewsSpread,
model_params={
"ratio_dumb": r1,
"ratio_herd": r2,
"ratio_wise": r3,
"network_generator": generator,
"network_params": netparams,
"prob_neighbor_spread": 0,
},
num_trials=50,
max_steps=300,
dry_run=True,
)
# Run all the necessary instances
sim.run()