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mirror of https://github.com/gsi-upm/soil synced 2024-11-13 06:52:28 +00:00

Initial benchmarking

This commit is contained in:
J. Fernando Sánchez 2023-05-03 12:07:55 +02:00
parent eca4cae298
commit 5e93399d58
24 changed files with 865 additions and 0 deletions

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command,mean,stddev,median,user,system,min,max,parameter_sim
python noop/mesa_batchrunner.py,1.3258325165599998,0.05822826666377271,1.31279976286,1.2978164199999997,0.25767558,1.2780627573599999,1.46763559736,mesa_batchrunner
python noop/mesa_simulation.py,1.3915081544599999,0.07311646048704976,1.37166811936,1.35267662,0.29222067999999995,1.32746067836,1.58495303336,mesa_simulation
python noop/soil_step.py,1.9859962588599998,0.12143759641749913,1.93586195486,2.0000750199999997,0.54126188,1.9061700903599998,2.2532835533599997,soil_step
python noop/soil_step_pqueue.py,2.1347049971600005,0.01336179424666973,2.13492341986,2.1368160200000004,0.56862948,2.11810132936,2.16042739636,soil_step_pqueue
python noop/soil_gens.py,2.1284937893599998,0.03030587681163665,2.13585231586,2.14158812,0.54900038,2.0768625143599997,2.19043625236,soil_gens
python noop/soil_gens_pqueue.py,2.3469003942599995,0.019461346004472344,2.3486906343599996,2.36505852,0.54629858,2.31766326036,2.37998102136,soil_gens_pqueue
python noop/soil_async.py,2.85755484126,0.0314955571121844,2.84774029536,2.86388112,0.55261338,2.81428668936,2.90567961636,soil_async
python noop/soil_async_pqueue.py,3.1999731134600005,0.04432336803797717,3.20255954186,3.2162337199999995,0.5501872800000001,3.1406816913599997,3.26137401936,soil_async_pqueue
python noop/soilent_step.py,1.30038977816,0.017973958957989845,1.30187804986,1.3231730199999998,0.5452653799999999,1.27058263436,1.31902240836,soilent_step
python noop/soilent_step_pqueue.py,1.4708435788599998,0.027193290392962755,1.4707784423599999,1.4900387199999998,0.54749428,1.43498127536,1.53065598436,soilent_step_pqueue
python noop/soilent_gens.py,1.6338810973599998,0.05752539125688073,1.63513330036,1.65216122,0.51846678,1.54135944036,1.7038832853599999,soilent_gens
1 command mean stddev median user system min max parameter_sim
2 python noop/mesa_batchrunner.py 1.3258325165599998 0.05822826666377271 1.31279976286 1.2978164199999997 0.25767558 1.2780627573599999 1.46763559736 mesa_batchrunner
3 python noop/mesa_simulation.py 1.3915081544599999 0.07311646048704976 1.37166811936 1.35267662 0.29222067999999995 1.32746067836 1.58495303336 mesa_simulation
4 python noop/soil_step.py 1.9859962588599998 0.12143759641749913 1.93586195486 2.0000750199999997 0.54126188 1.9061700903599998 2.2532835533599997 soil_step
5 python noop/soil_step_pqueue.py 2.1347049971600005 0.01336179424666973 2.13492341986 2.1368160200000004 0.56862948 2.11810132936 2.16042739636 soil_step_pqueue
6 python noop/soil_gens.py 2.1284937893599998 0.03030587681163665 2.13585231586 2.14158812 0.54900038 2.0768625143599997 2.19043625236 soil_gens
7 python noop/soil_gens_pqueue.py 2.3469003942599995 0.019461346004472344 2.3486906343599996 2.36505852 0.54629858 2.31766326036 2.37998102136 soil_gens_pqueue
8 python noop/soil_async.py 2.85755484126 0.0314955571121844 2.84774029536 2.86388112 0.55261338 2.81428668936 2.90567961636 soil_async
9 python noop/soil_async_pqueue.py 3.1999731134600005 0.04432336803797717 3.20255954186 3.2162337199999995 0.5501872800000001 3.1406816913599997 3.26137401936 soil_async_pqueue
10 python noop/soilent_step.py 1.30038977816 0.017973958957989845 1.30187804986 1.3231730199999998 0.5452653799999999 1.27058263436 1.31902240836 soilent_step
11 python noop/soilent_step_pqueue.py 1.4708435788599998 0.027193290392962755 1.4707784423599999 1.4900387199999998 0.54749428 1.43498127536 1.53065598436 soilent_step_pqueue
12 python noop/soilent_gens.py 1.6338810973599998 0.05752539125688073 1.63513330036 1.65216122 0.51846678 1.54135944036 1.7038832853599999 soilent_gens

11
benchmarks/noop-bench.csv Normal file
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command,mean,stddev,median,user,system,min,max,parameter_sim
python noop/mesa1_batchrunner.py,1.2559917394000002,0.012031173494887278,1.2572688413000002,1.2168630799999998,0.31825289999999995,1.2346063853,1.2735512493,mesa1_batchrunner
python noop/mesa1_simulation.py,1.3024417227,0.022498874113931668,1.2994157323,1.2595484799999999,0.3087897,1.2697029703,1.3350640403,mesa1_simulation
python noop/soil1.py,1.8789492443,0.18023367899835044,1.8186795393000001,1.86076288,0.5309521,1.7326687413000001,2.2928370642999996,soil1
python noop/soil1_pqueue.py,1.9841675890000001,0.01735524088843906,1.9884363323,2.01830338,0.5787977999999999,1.9592171483,2.0076169282999996,soil1_pqueue
python noop/soil2.py,2.0135188921999996,0.02869307129649681,2.0184709453,2.03951308,0.5885591,1.9680417823,2.0567112592999997,soil2
python noop/soil2_pqueue.py,2.2367320454999997,0.024339667344486046,2.2357249777999995,2.2515216799999997,0.5978869,2.1957917303,2.2688685033,soil2_pqueue
python noop/soilent1.py,1.1309301329,0.015133005948737871,1.1276461497999999,1.14056688,0.6027519,1.1135821423,1.1625753893,soilent1
python noop/soilent1_pqueue.py,1.3097537665000003,0.018821977712258842,1.3073709358,1.3270259799999997,0.6000067999999998,1.2874580013,1.3381646823,soilent1_pqueue
python noop/soilent2.py,1.5055360476,0.05166674417574119,1.4883118568,1.5121205799999997,0.5817363999999999,1.4490918363,1.6005909333000001,soilent2
python noop/soilent2_pqueue.py,1.6622598218,0.031130739036296016,1.6588702603,1.6862567799999997,0.5854159,1.6289724583,1.7330545383,soilent2_pqueue
1 command mean stddev median user system min max parameter_sim
2 python noop/mesa1_batchrunner.py 1.2559917394000002 0.012031173494887278 1.2572688413000002 1.2168630799999998 0.31825289999999995 1.2346063853 1.2735512493 mesa1_batchrunner
3 python noop/mesa1_simulation.py 1.3024417227 0.022498874113931668 1.2994157323 1.2595484799999999 0.3087897 1.2697029703 1.3350640403 mesa1_simulation
4 python noop/soil1.py 1.8789492443 0.18023367899835044 1.8186795393000001 1.86076288 0.5309521 1.7326687413000001 2.2928370642999996 soil1
5 python noop/soil1_pqueue.py 1.9841675890000001 0.01735524088843906 1.9884363323 2.01830338 0.5787977999999999 1.9592171483 2.0076169282999996 soil1_pqueue
6 python noop/soil2.py 2.0135188921999996 0.02869307129649681 2.0184709453 2.03951308 0.5885591 1.9680417823 2.0567112592999997 soil2
7 python noop/soil2_pqueue.py 2.2367320454999997 0.024339667344486046 2.2357249777999995 2.2515216799999997 0.5978869 2.1957917303 2.2688685033 soil2_pqueue
8 python noop/soilent1.py 1.1309301329 0.015133005948737871 1.1276461497999999 1.14056688 0.6027519 1.1135821423 1.1625753893 soilent1
9 python noop/soilent1_pqueue.py 1.3097537665000003 0.018821977712258842 1.3073709358 1.3270259799999997 0.6000067999999998 1.2874580013 1.3381646823 soilent1_pqueue
10 python noop/soilent2.py 1.5055360476 0.05166674417574119 1.4883118568 1.5121205799999997 0.5817363999999999 1.4490918363 1.6005909333000001 soilent2
11 python noop/soilent2_pqueue.py 1.6622598218 0.031130739036296016 1.6588702603 1.6862567799999997 0.5854159 1.6289724583 1.7330545383 soilent2_pqueue

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import os
NUM_AGENTS = int(os.environ.get('NUM_AGENTS', 100))
NUM_ITERS = int(os.environ.get('NUM_ITERS', 10))
MAX_STEPS = int(os.environ.get('MAX_STEPS', 1000))
def run_sim(model, **kwargs):
from soil import Simulation
opts = dict(model=model,
dump=False,
num_processes=1,
parameters={'num_agents': NUM_AGENTS},
max_steps=MAX_STEPS,
iterations=NUM_ITERS)
opts.update(kwargs)
res = Simulation(**opts).run()
total = sum(a.num_calls for e in res for a in e.schedule.agents)
expected = NUM_AGENTS * NUM_ITERS * MAX_STEPS
print(total)
print(expected)
assert total == expected
return res

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from mesa import batch_run, DataCollector, Agent, Model
from mesa.time import RandomActivation
class NoopAgent(Agent):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.num_calls = 0
def step(self):
# import pdb;pdb.set_trace()
self.num_calls += 1
class NoopModel(Model):
def __init__(self, N):
super().__init__()
self.schedule = RandomActivation(self)
for i in range(N):
self.schedule.add(NoopAgent(self.next_id(), self))
self.datacollector = DataCollector(model_reporters={"num_agents": lambda m: m.schedule.get_agent_count(),
"time": lambda m: m.schedule.time},
agent_reporters={"num_calls": "num_calls"})
self.datacollector.collect(self)
def step(self):
self.schedule.step()
self.datacollector.collect(self)
if __name__ == "__main__":
from _config import *
res = batch_run(model_cls=NoopModel,
max_steps=MAX_STEPS,
iterations=NUM_ITERS,
number_processes=1,
parameters={'N': NUM_AGENTS})
total = sum(s["num_calls"] for s in res)
total_agents = sum(s["num_agents"] for s in res)
assert len(res) == NUM_AGENTS * NUM_ITERS
assert total == NUM_AGENTS * NUM_ITERS * MAX_STEPS
assert total_agents == NUM_AGENTS * NUM_AGENTS * NUM_ITERS

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from mesa import batch_run, DataCollector, Agent, Model
from mesa.time import RandomActivation
from soil import Simulation
from _config import *
class NoopAgent(Agent):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.num_calls = 0
def step(self):
# import pdb;pdb.set_trace()
self.num_calls += 1
class NoopModel(Model):
def __init__(self, num_agents, *args, **kwargs):
super().__init__()
self.schedule = RandomActivation(self)
for i in range(num_agents):
self.schedule.add(NoopAgent(self.next_id(), self))
self.datacollector = DataCollector(model_reporters={"num_agents": lambda m: m.schedule.get_agent_count(),
"time": lambda m: m.schedule.time},
agent_reporters={"num_calls": "num_calls"})
self.datacollector.collect(self)
def step(self):
self.schedule.step()
self.datacollector.collect(self)
def run():
run_sim(model=NoopModel)
if __name__ == "__main__":
run()

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command,mean,stddev,median,user,system,min,max,parameter_sim
python mesa1_batchrunner.py,1.2932078178200002,0.05649377020829272,1.2705532802200001,1.25902256,0.27242284,1.22210926572,1.40867459172,mesa1_batchrunner
python mesa1_simulation.py,1.81112963812,0.015491072368938567,1.81342524572,1.8594407599999996,0.8005329399999999,1.78538603972,1.84176361172,mesa1_simulation
1 command mean stddev median user system min max parameter_sim
2 python mesa1_batchrunner.py 1.2932078178200002 0.05649377020829272 1.2705532802200001 1.25902256 0.27242284 1.22210926572 1.40867459172 mesa1_batchrunner
3 python mesa1_simulation.py 1.81112963812 0.015491072368938567 1.81342524572 1.8594407599999996 0.8005329399999999 1.78538603972 1.84176361172 mesa1_simulation

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from soil import BaseAgent, Environment, Simulation
class NoopAgent(BaseAgent):
num_calls = 0
async def step(self):
while True:
self.num_calls += 1
await self.delay()
class NoopEnvironment(Environment):
num_agents = 100
def init(self):
self.add_agents(NoopAgent, k=self.num_agents)
self.add_agent_reporter("num_calls")
if __name__ == "__main__":
from _config import *
run_sim(model=NoopEnvironment)

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from soil import BaseAgent, Environment, Simulation, PQueueActivation
class NoopAgent(BaseAgent):
num_calls = 0
async def step(self):
while True:
self.num_calls += 1
await self.delay()
class NoopEnvironment(Environment):
num_agents = 100
schedule_class = PQueueActivation
def init(self):
self.add_agents(NoopAgent, k=self.num_agents)
self.add_agent_reporter("num_calls")
if __name__ == "__main__":
from _config import *
run_sim(model=NoopEnvironment)

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from soil import BaseAgent, Environment, Simulation
class NoopAgent(BaseAgent):
num_calls = 0
def step(self):
while True:
self.num_calls += 1
yield self.delay()
class NoopEnvironment(Environment):
num_agents = 100
def init(self):
self.add_agents(NoopAgent, k=self.num_agents)
self.add_agent_reporter("num_calls")
if __name__ == "__main__":
from _config import *
run_sim(model=NoopEnvironment)

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from soil import BaseAgent, Environment, Simulation, PQueueActivation
class NoopAgent(BaseAgent):
num_calls = 0
def step(self):
while True:
self.num_calls += 1
yield self.delay()
class NoopEnvironment(Environment):
num_agents = 100
schedule_class = PQueueActivation
def init(self):
self.add_agents(NoopAgent, k=self.num_agents)
self.add_agent_reporter("num_calls")
if __name__ == "__main__":
from _config import *
run_sim(model=NoopEnvironment)

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from soil import BaseAgent, Environment, Simulation
class NoopAgent(BaseAgent):
num_calls = 0
def step(self):
self.num_calls += 1
class NoopEnvironment(Environment):
num_agents = 100
def init(self):
self.add_agents(NoopAgent, k=self.num_agents)
self.add_agent_reporter("num_calls")
if __name__ == "__main__":
from _config import *
run_sim(model=NoopEnvironment)

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from soil import BaseAgent, Environment, Simulation, PQueueActivation
class NoopAgent(BaseAgent):
num_calls = 0
def step(self):
self.num_calls += 1
class NoopEnvironment(Environment):
num_agents = 100
schedule_class = PQueueActivation
def init(self):
self.add_agents(NoopAgent, k=self.num_agents)
self.add_agent_reporter("num_calls")
if __name__ == "__main__":
from _config import *
run_sim(model=NoopEnvironment)

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from soil import Agent, Environment, Simulation
from soilent import Scheduler
class NoopAgent(Agent):
num_calls = 0
async def step(self):
while True:
self.num_calls += 1
# yield self.delay(1)
await self.delay()
class NoopEnvironment(Environment):
num_agents = 100
schedule_class = Scheduler
def init(self):
self.add_agents(NoopAgent, k=self.num_agents)
self.add_agent_reporter("num_calls")
if __name__ == "__main__":
from _config import *
res = run_sim(model=NoopEnvironment)
for r in res:
assert isinstance(r.schedule, Scheduler)

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from soil import Agent, Environment
from soilent import PQueueScheduler
class NoopAgent(Agent):
num_calls = 0
async def step(self):
while True:
self.num_calls += 1
await self.delay()
class NoopEnvironment(Environment):
num_agents = 100
schedule_class = PQueueScheduler
def init(self):
self.add_agents(NoopAgent, k=self.num_agents)
self.add_agent_reporter("num_calls")
if __name__ == "__main__":
from _config import *
res = run_sim(model=NoopEnvironment)
for r in res:
assert isinstance(r.schedule, PQueueScheduler)

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from soil import Agent, Environment, Simulation
from soilent import Scheduler
class NoopAgent(Agent):
num_calls = 0
def step(self):
while True:
self.num_calls += 1
# yield self.delay(1)
yield self.delay()
class NoopEnvironment(Environment):
num_agents = 100
schedule_class = Scheduler
def init(self):
self.add_agents(NoopAgent, k=self.num_agents)
self.add_agent_reporter("num_calls")
if __name__ == "__main__":
from _config import *
res = run_sim(model=NoopEnvironment)
for r in res:
assert isinstance(r.schedule, Scheduler)

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from soil import Agent, Environment
from soilent import PQueueScheduler
class NoopAgent(Agent):
num_calls = 0
def step(self):
while True:
self.num_calls += 1
# yield self.delay(1)
yield
class NoopEnvironment(Environment):
num_agents = 100
schedule_class = PQueueScheduler
def init(self):
self.add_agents(NoopAgent, k=self.num_agents)
self.add_agent_reporter("num_calls")
if __name__ == "__main__":
from _config import *
res = run_sim(model=NoopEnvironment)
for r in res:
assert isinstance(r.schedule, PQueueScheduler)

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from soil import BaseAgent, Environment, Simulation
from soilent import Scheduler
class NoopAgent(BaseAgent):
num_calls = 0
def step(self):
self.num_calls += 1
class NoopEnvironment(Environment):
num_agents = 100
schedule_class = Scheduler
def init(self):
self.add_agents(NoopAgent, k=self.num_agents)
self.add_agent_reporter("num_calls")
if __name__ == "__main__":
from _config import *
res = run_sim(model=NoopEnvironment)
for r in res:
assert isinstance(r.schedule, Scheduler)

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from soil import BaseAgent, Environment, Simulation
from soilent import PQueueScheduler
class NoopAgent(BaseAgent):
num_calls = 0
def step(self):
self.num_calls += 1
class NoopEnvironment(Environment):
num_agents = 100
schedule_class = PQueueScheduler
def init(self):
self.add_agents(NoopAgent, k=self.num_agents)
self.add_agent_reporter("num_calls")
if __name__ == "__main__":
from _config import *
res = run_sim(model=NoopEnvironment)
for r in res:
assert isinstance(r.schedule, PQueueScheduler)

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benchmarks/run.py Executable file
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#!/bin/env python
import sys
import os
import subprocess
import argparse
parser = argparse.ArgumentParser(
prog='Profiler for soil')
parser.add_argument('--suffix', default=None)
parser.add_argument('files', nargs="+")
args = parser.parse_args()
for fname in args.files:
suffix = ("_" + args.suffix) if args.suffix else ""
simname = f"{fname.replace('/', '-')}{suffix}"
profpath = os.path.join("profs", simname + ".prof")
print(f"Running {fname} and saving profile to {profpath}")
subprocess.call(["python", "-m", "cProfile", "-o", profpath, fname])

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command,mean,stddev,median,user,system,min,max,parameter_sim
python virusonnetwork/mesa_basic.py,3.8381473157,0.0518143371442526,3.8475315791,3.873109219999999,0.55102658,3.7523016936,3.9095182436,mesa_basic.py
python virusonnetwork/soil_step.py,3.2167258977000004,0.02337131987357665,3.2257620261,3.28374132,0.51343958,3.1792271306,3.2511521286000002,soil_step.py
python virusonnetwork/soil_states.py,3.4908183217,0.03726734070349347,3.4912775086,3.5684004200000006,0.50416068,3.4272087936,3.5529207346000002,soil_states.py
1 command mean stddev median user system min max parameter_sim
2 python virusonnetwork/mesa_basic.py 3.8381473157 0.0518143371442526 3.8475315791 3.873109219999999 0.55102658 3.7523016936 3.9095182436 mesa_basic.py
3 python virusonnetwork/soil_step.py 3.2167258977000004 0.02337131987357665 3.2257620261 3.28374132 0.51343958 3.1792271306 3.2511521286000002 soil_step.py
4 python virusonnetwork/soil_states.py 3.4908183217 0.03726734070349347 3.4912775086 3.5684004200000006 0.50416068 3.4272087936 3.5529207346000002 soil_states.py

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import os
NUM_AGENTS = int(os.environ.get('NUM_AGENTS', 100))
NUM_ITERS = int(os.environ.get('NUM_ITERS', 10))
MAX_STEPS = int(os.environ.get('MAX_STEPS', 1000))
def run_sim(model, **kwargs):
from soil import Simulation
opts = dict(model=model,
dump=False,
num_processes=1,
parameters={'num_nodes': NUM_AGENTS,
"avg_node_degree": 3,
"initial_outbreak_size": 5,
"virus_spread_chance": 0.25,
"virus_check_frequency": 0.25,
"recovery_chance": 0.3,
"gain_resistance_chance": 0.1,
},
max_steps=MAX_STEPS,
iterations=NUM_ITERS)
opts.update(kwargs)
its = Simulation(**opts).run()
assert all(it.schedule.steps == MAX_STEPS for it in its)
ratios = list(it.resistant_susceptible_ratio() for it in its)
print("Max - Avg - Min ratio:", max(ratios), sum(ratios)/len(ratios), min(ratios))
assert all(sum([it.number_susceptible,
it.number_infected,
it.number_resistant]) == NUM_AGENTS for it in its)
return its

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# Verbatim copy from mesa
# https://github.com/projectmesa/mesa/blob/976ddfc8a1e5feaaf8007a7abaa9abc7093881a0/examples/virus_on_network/virus_on_network/model.py
import math
from enum import Enum
import networkx as nx
import mesa
class State(Enum):
SUSCEPTIBLE = 0
INFECTED = 1
RESISTANT = 2
def number_state(model, state):
return sum(1 for a in model.grid.get_all_cell_contents() if a.state is state)
def number_infected(model):
return number_state(model, State.INFECTED)
def number_susceptible(model):
return number_state(model, State.SUSCEPTIBLE)
def number_resistant(model):
return number_state(model, State.RESISTANT)
class VirusOnNetwork(mesa.Model):
"""A virus model with some number of agents"""
def __init__(
self,
*args,
num_nodes=10,
avg_node_degree=3,
initial_outbreak_size=1,
virus_spread_chance=0.4,
virus_check_frequency=0.4,
recovery_chance=0.3,
gain_resistance_chance=0.5,
**kwargs,
):
self.num_nodes = num_nodes
prob = avg_node_degree / self.num_nodes
self.G = nx.erdos_renyi_graph(n=self.num_nodes, p=prob)
self.grid = mesa.space.NetworkGrid(self.G)
self.schedule = mesa.time.RandomActivation(self)
self.initial_outbreak_size = (
initial_outbreak_size if initial_outbreak_size <= num_nodes else num_nodes
)
self.virus_spread_chance = virus_spread_chance
self.virus_check_frequency = virus_check_frequency
self.recovery_chance = recovery_chance
self.gain_resistance_chance = gain_resistance_chance
self.datacollector = mesa.DataCollector(
{
"Ratio": "resistant_susceptible_ratio",
"Infected": number_infected,
"Susceptible": number_susceptible,
"Resistant": number_resistant,
}
)
# Create agents
for i, node in enumerate(self.G.nodes()):
a = VirusAgent(
i,
self,
State.SUSCEPTIBLE,
self.virus_spread_chance,
self.virus_check_frequency,
self.recovery_chance,
self.gain_resistance_chance,
)
self.schedule.add(a)
# Add the agent to the node
self.grid.place_agent(a, node)
# Infect some nodes
infected_nodes = self.random.sample(list(self.G), self.initial_outbreak_size)
for a in self.grid.get_cell_list_contents(infected_nodes):
a.state = State.INFECTED
self.running = True
self.datacollector.collect(self)
@property
def number_susceptible(self):
return number_susceptible(self)
@property
def number_resistant(self):
return number_resistant(self)
@property
def number_infected(self):
return number_infected(self)
def resistant_susceptible_ratio(self):
try:
return number_state(self, State.RESISTANT) / number_state(
self, State.SUSCEPTIBLE
)
except ZeroDivisionError:
return math.inf
def step(self):
self.schedule.step()
# collect data
self.datacollector.collect(self)
def run_model(self, n):
for i in range(n):
self.step()
class VirusAgent(mesa.Agent):
def __init__(
self,
unique_id,
model,
initial_state,
virus_spread_chance,
virus_check_frequency,
recovery_chance,
gain_resistance_chance,
):
super().__init__(unique_id, model)
self.state = initial_state
self.virus_spread_chance = virus_spread_chance
self.virus_check_frequency = virus_check_frequency
self.recovery_chance = recovery_chance
self.gain_resistance_chance = gain_resistance_chance
def try_to_infect_neighbors(self):
neighbors_nodes = self.model.grid.get_neighbors(self.pos, include_center=False)
susceptible_neighbors = [
agent
for agent in self.model.grid.get_cell_list_contents(neighbors_nodes)
if agent.state is State.SUSCEPTIBLE
]
for a in susceptible_neighbors:
if self.random.random() < self.virus_spread_chance:
a.state = State.INFECTED
def try_gain_resistance(self):
if self.random.random() < self.gain_resistance_chance:
self.state = State.RESISTANT
def try_remove_infection(self):
# Try to remove
if self.random.random() < self.recovery_chance:
# Success
self.state = State.SUSCEPTIBLE
self.try_gain_resistance()
else:
# Failed
self.state = State.INFECTED
def try_check_situation(self):
if self.random.random() < self.virus_check_frequency:
# Checking...
if self.state is State.INFECTED:
self.try_remove_infection()
def step(self):
if self.state is State.INFECTED:
self.try_to_infect_neighbors()
self.try_check_situation()
from _config import run_sim
run_sim(model=VirusOnNetwork)

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# Verbatim copy from mesa
# https://github.com/projectmesa/mesa/blob/976ddfc8a1e5feaaf8007a7abaa9abc7093881a0/examples/virus_on_network/virus_on_network/model.py
import math
from enum import Enum
import networkx as nx
from soil import *
class VirusOnNetwork(Environment):
"""A virus model with some number of agents"""
num_nodes = 10
avg_node_degree = 3
initial_outbreak_size = 1
virus_spread_chance = 0.4
virus_check_frequency = 0.4
recovery_chance = 0
gain_resistance_chance = 0
def init(self):
prob = self.avg_node_degree / self.num_nodes
# Use internal seed with the networkx generator
self.create_network(generator=nx.erdos_renyi_graph, n=self.num_nodes, p=prob)
self.initial_outbreak_size = min(self.initial_outbreak_size, self.num_nodes)
self.populate_network(VirusAgent)
# Infect some nodes
infected_nodes = self.random.sample(list(self.G), self.initial_outbreak_size)
for a in self.agents(node_id=infected_nodes):
a.set_state(VirusAgent.infected)
assert self.number_infected == self.initial_outbreak_size
@report
def resistant_susceptible_ratio(self):
try:
return self.number_resistant / self.number_susceptible
except ZeroDivisionError:
return math.inf
@report
@property
def number_infected(self):
return self.count_agents(state_id=VirusAgent.infected.id)
@report
@property
def number_susceptible(self):
return self.count_agents(state_id=VirusAgent.susceptible.id)
@report
@property
def number_resistant(self):
return self.count_agents(state_id=VirusAgent.resistant.id)
class VirusAgent(Agent):
virus_spread_chance = None # Inherit from model
virus_check_frequency = None # Inherit from model
recovery_chance = None # Inherit from model
gain_resistance_chance = None # Inherit from model
just_been_infected = False
@state(default=True)
def susceptible(self):
if self.just_been_infected:
self.just_been_infected = False
return self.infected
@state
def infected(self):
susceptible_neighbors = self.get_neighbors(state_id=self.susceptible.id)
for a in susceptible_neighbors:
if self.prob(self.virus_spread_chance):
a.just_been_infected = True
if self.prob(self.virus_check_frequency):
if self.prob(self.recovery_chance):
if self.prob(self.gain_resistance_chance):
return self.resistant
else:
return self.susceptible
else:
return self.infected
@state
def resistant(self):
return self.at(INFINITY)
if __name__ == "__main__":
from _config import run_sim
run_sim(model=VirusOnNetwork)

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# Verbatim copy from mesa
# https://github.com/projectmesa/mesa/blob/976ddfc8a1e5feaaf8007a7abaa9abc7093881a0/examples/virus_on_network/virus_on_network/model.py
import math
from enum import Enum
import networkx as nx
from soil import *
class State(Enum):
SUSCEPTIBLE = 0
INFECTED = 1
RESISTANT = 2
class VirusOnNetwork(Environment):
"""A virus model with some number of agents"""
num_nodes = 10
avg_node_degree = 3
initial_outbreak_size = 1
virus_spread_chance = 0.4
virus_check_frequency = 0.4
recovery_chance = 0
gain_resistance_chance = 0
def init(self):
prob = self.avg_node_degree / self.num_nodes
# Use internal seed with the networkx generator
self.create_network(generator=nx.erdos_renyi_graph, n=self.num_nodes, p=prob)
self.initial_outbreak_size = min(self.initial_outbreak_size, self.num_nodes)
self.populate_network(VirusAgent)
# Infect some nodes
infected_nodes = self.random.sample(list(self.G), self.initial_outbreak_size)
for a in self.agents(node_id=infected_nodes):
a.status = State.INFECTED
assert self.number_infected == self.initial_outbreak_size
@report
def resistant_susceptible_ratio(self):
try:
return self.number_resistant / self.number_susceptible
except ZeroDivisionError:
return math.inf
@report
@property
def number_infected(self):
return self.count_agents(status=State.INFECTED)
@report
@property
def number_susceptible(self):
return self.count_agents(status=State.SUSCEPTIBLE)
@report
@property
def number_resistant(self):
return self.count_agents(status=State.RESISTANT)
class VirusAgent(Agent):
status = State.SUSCEPTIBLE
virus_spread_chance = None # Inherit from model
virus_check_frequency = None # Inherit from model
recovery_chance = None # Inherit from model
gain_resistance_chance = None # Inherit from model
def try_to_infect_neighbors(self):
susceptible_neighbors = self.get_neighbors(status=State.SUSCEPTIBLE)
for a in susceptible_neighbors:
if self.prob(self.virus_spread_chance):
a.status = State.INFECTED
def try_gain_resistance(self):
if self.prob(self.gain_resistance_chance):
self.status = State.RESISTANT
return self.at(INFINITY)
def try_remove_infection(self):
# Try to remove
if self.prob(self.recovery_chance):
# Success
self.status = State.SUSCEPTIBLE
return self.try_gain_resistance()
def try_check_situation(self):
if self.prob(self.virus_check_frequency):
# Checking...
if self.status is State.INFECTED:
return self.try_remove_infection()
def step(self):
if self.status is State.INFECTED:
self.try_to_infect_neighbors()
return self.try_check_situation()
if __name__ == "__main__":
from _config import run_sim
run_sim(model=VirusOnNetwork)