mirror of
https://github.com/gsi-upm/soil
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d9947c2c52
Documentation needs some improvement The API has been simplified to only allow for ONE topology per NetworkEnvironment. This covers the main use case, and simplifies the code.
127 lines
3.9 KiB
Python
127 lines
3.9 KiB
Python
'''
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This is an example that adds soil agents and environment in a normal
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mesa workflow.
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'''
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from mesa import Agent as MesaAgent
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from mesa.space import MultiGrid
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# from mesa.time import RandomActivation
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from mesa.datacollection import DataCollector
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from mesa.batchrunner import BatchRunner
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import networkx as nx
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from soil import NetworkAgent, Environment, serialization
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def compute_gini(model):
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agent_wealths = [agent.wealth for agent in model.agents]
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x = sorted(agent_wealths)
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N = len(list(model.agents))
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B = sum( xi * (N-i) for i,xi in enumerate(x) ) / (N*sum(x))
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return (1 + (1/N) - 2*B)
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class MoneyAgent(MesaAgent):
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"""
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A MESA agent with fixed initial wealth.
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It will only share wealth with neighbors based on grid proximity
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"""
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def __init__(self, unique_id, model, wealth=1):
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super().__init__(unique_id=unique_id, model=model)
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self.wealth = wealth
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def move(self):
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possible_steps = self.model.grid.get_neighborhood(
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self.pos,
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moore=True,
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include_center=False)
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new_position = self.random.choice(possible_steps)
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self.model.grid.move_agent(self, new_position)
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def give_money(self):
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cellmates = self.model.grid.get_cell_list_contents([self.pos])
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if len(cellmates) > 1:
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other = self.random.choice(cellmates)
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other.wealth += 1
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self.wealth -= 1
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def step(self):
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print("Crying wolf", self.pos)
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self.move()
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if self.wealth > 0:
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self.give_money()
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class SocialMoneyAgent(NetworkAgent, MoneyAgent):
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wealth = 1
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def give_money(self):
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cellmates = set(self.model.grid.get_cell_list_contents([self.pos]))
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friends = set(self.get_neighboring_agents())
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self.info("Trying to give money")
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self.info("Cellmates: ", cellmates)
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self.info("Friends: ", friends)
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nearby_friends = list(cellmates & friends)
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if len(nearby_friends):
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other = self.random.choice(nearby_friends)
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other.wealth += 1
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self.wealth -= 1
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def graph_generator(n=5):
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G = nx.Graph()
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for ix in range(n):
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G.add_edge(0, ix)
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return G
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class MoneyEnv(Environment):
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"""A model with some number of agents."""
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def __init__(self, width, height, N, generator=graph_generator,
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agent_class=SocialMoneyAgent,
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topology=None, **kwargs):
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generator = serialization.deserialize(generator)
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agent_class = serialization.deserialize(agent_class, globs=globals())
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topology = generator(n=N)
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super().__init__(topology=topology,
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N=N,
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**kwargs)
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self.grid = MultiGrid(width, height, False)
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self.populate_network(agent_class=agent_class)
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# Create agents
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for agent in self.agents:
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x = self.random.randrange(self.grid.width)
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y = self.random.randrange(self.grid.height)
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self.grid.place_agent(agent, (x, y))
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self.datacollector = DataCollector(
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model_reporters={"Gini": compute_gini},
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agent_reporters={"Wealth": "wealth"})
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if __name__ == '__main__':
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fixed_params = {"generator": nx.complete_graph,
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"width": 10,
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"network_agents": [{"agent_class": SocialMoneyAgent,
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'weight': 1}],
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"height": 10}
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variable_params = {"N": range(10, 100, 10)}
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batch_run = BatchRunner(MoneyEnv,
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variable_parameters=variable_params,
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fixed_parameters=fixed_params,
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iterations=5,
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max_steps=100,
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model_reporters={"Gini": compute_gini})
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batch_run.run_all()
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run_data = batch_run.get_model_vars_dataframe()
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run_data.head()
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print(run_data.Gini)
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