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WIP: mesa compatibility
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21
CHANGELOG.md
21
CHANGELOG.md
@ -3,6 +3,27 @@ All notable changes to this project will be documented in this file.
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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).
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## [Unreleased]
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### Added
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* [WIP] Integration with MESA
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* `not_agent_ids` paramter to get sql in history
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### Changed
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* `soil.Environment` now also inherits from `mesa.Model`
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* `soil.Agent` now also inherits from `mesa.Agent`
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* `soil.time` to replace `simpy` events, delays, duration, etc.
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### Removed
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* `simpy` dependency and compatibility. Each agent used to be a simpy generator, but that made debugging and error handling more complex. That has been replaced by a scheduler within the `soil.Environment` class, similar to how `mesa` does it.
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### TODO:
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* agent_id -> unique_id?
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* mesa has Agent.model and soil has Agent.env
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* Environments.agents and mesa.Agent.agents are not the same. env is a property, and it only takes into account network and environment agents. Might rename environment_agents to other_agents or sth like that
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* soil.History should mimic a mesa.datacollector :/
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* soil.Simulation *could* mimic a mesa.batchrunner
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* DONE include scheduler in environment
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* DONE environment inherits from `mesa.Model`
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## [0.15.2]
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### Fixed
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* Pass the right known_modules and parameters to stats discovery in simulation
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@ -47,12 +47,6 @@ There are three main elements in a soil simulation:
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- The environment. It assigns agents to nodes in the network, and
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stores the environment parameters (shared state for all agents).
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Soil is based on ``simpy``, which is an event-based network simulation
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library. Soil provides several abstractions over events to make
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developing agents easier. This means you can use events (timeouts,
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delays) in soil, but for the most part we will assume your models will
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be step-based.
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Modeling behaviour
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------------------
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21
examples/mesa/mesa.yml
Normal file
21
examples/mesa/mesa.yml
Normal file
@ -0,0 +1,21 @@
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---
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name: mesa_sim
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group: tests
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dir_path: "/tmp"
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num_trials: 3
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max_time: 100
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interval: 1
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seed: '1'
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network_params:
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generator: social_wealth.graph_generator
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n: 5
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network_agents:
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- agent_type: social_wealth.SocialMoneyAgent
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weight: 1
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environment_class: social_wealth.MoneyEnv
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environment_params:
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num_mesa_agents: 5
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mesa_agent_type: social_wealth.MoneyAgent
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N: 10
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width: 50
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height: 50
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106
examples/mesa/server.py
Normal file
106
examples/mesa/server.py
Normal file
@ -0,0 +1,106 @@
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from mesa.visualization.ModularVisualization import ModularServer
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from soil.visualization import UserSettableParameter
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from mesa.visualization.modules import ChartModule, NetworkModule, CanvasGrid
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from social_wealth import MoneyEnv, graph_generator, SocialMoneyAgent
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class MyNetwork(NetworkModule):
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def render(self, model):
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return self.portrayal_method(model)
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def network_portrayal(env):
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# The model ensures there is 0 or 1 agent per node
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portrayal = dict()
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portrayal["nodes"] = [
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{
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"id": agent_id,
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"size": env.get_agent(agent_id).wealth,
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# "color": "#CC0000" if not agents or agents[0].wealth == 0 else "#007959",
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"color": "#CC0000",
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"label": f"{agent_id}: {env.get_agent(agent_id).wealth}",
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}
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for (agent_id) in env.G.nodes
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]
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# import pdb;pdb.set_trace()
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portrayal["edges"] = [
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{"id": edge_id, "source": source, "target": target, "color": "#000000"}
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for edge_id, (source, target) in enumerate(env.G.edges)
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]
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return portrayal
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def gridPortrayal(agent):
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"""
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This function is registered with the visualization server to be called
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each tick to indicate how to draw the agent in its current state.
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:param agent: the agent in the simulation
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:return: the portrayal dictionary
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"""
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color = max(10, min(agent.wealth*10, 100))
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return {
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"Shape": "rect",
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"w": 1,
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"h": 1,
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"Filled": "true",
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"Layer": 0,
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"Label": agent.unique_id,
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"Text": agent.unique_id,
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"x": agent.pos[0],
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"y": agent.pos[1],
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"Color": f"rgba(31, 10, 255, 0.{color})"
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}
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grid = MyNetwork(network_portrayal, 500, 500, library="sigma")
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chart = ChartModule(
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[{"Label": "Gini", "Color": "Black"}], data_collector_name="datacollector"
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)
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model_params = {
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"N": UserSettableParameter(
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"slider",
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"N",
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1,
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1,
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10,
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1,
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description="Choose how many agents to include in the model",
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),
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"network_agents": [{"agent_type": SocialMoneyAgent}],
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"height": UserSettableParameter(
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"slider",
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"height",
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5,
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5,
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10,
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1,
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description="Grid height",
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),
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"width": UserSettableParameter(
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"slider",
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"width",
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5,
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5,
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10,
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1,
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description="Grid width",
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),
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"network_params": {
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'generator': graph_generator
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},
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}
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canvas_element = CanvasGrid(gridPortrayal, model_params["width"].value, model_params["height"].value, 500, 500)
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server = ModularServer(
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MoneyEnv, [grid, chart, canvas_element], "Money Model", model_params
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)
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server.port = 8521
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server.launch(open_browser=False)
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134
examples/mesa/social_wealth.py
Normal file
134
examples/mesa/social_wealth.py
Normal file
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'''
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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
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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):
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super().__init__(unique_id=unique_id, model=model)
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self.wealth = 1
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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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print(self.pos, possible_steps)
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new_position = self.random.choice(possible_steps)
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print(self.pos, new_position)
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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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self.info("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.debug("Cellmates: ", cellmates)
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self.debug("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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class MoneyEnv(Environment):
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"""A model with some number of agents."""
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def __init__(self, N, width, height, *args, network_params, **kwargs):
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self.initialized = True
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# import pdb;pdb.set_trace()
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network_params['n'] = N
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super().__init__(*args, network_params=network_params, **kwargs)
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self.grid = MultiGrid(width, height, False)
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# self.schedule = RandomActivation(self)
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self.running = True
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# Create agents
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for agent in self.agents:
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self.schedule.add(agent)
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# a = MoneyAgent(i, self)
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# self.schedule.add(a)
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# Add the agent to a random grid cell
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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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def step(self):
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super().step()
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self.datacollector.collect(self)
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self.schedule.step()
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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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if __name__ == '__main__':
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G = graph_generator()
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fixed_params = {"topology": G,
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"width": 10,
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"network_agents": [{"agent_type": 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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83
examples/mesa/wealth.py
Normal file
83
examples/mesa/wealth.py
Normal file
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from mesa import Agent, Model
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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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def compute_gini(model):
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agent_wealths = [agent.wealth for agent in model.schedule.agents]
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x = sorted(agent_wealths)
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N = model.num_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(Agent):
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""" An agent with fixed initial wealth."""
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def __init__(self, unique_id, model):
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super().__init__(unique_id, model)
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self.wealth = 1
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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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self.move()
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if self.wealth > 0:
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self.give_money()
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class MoneyModel(Model):
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"""A model with some number of agents."""
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def __init__(self, N, width, height):
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self.num_agents = N
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self.grid = MultiGrid(width, height, True)
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self.schedule = RandomActivation(self)
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self.running = True
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# Create agents
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for i in range(self.num_agents):
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a = MoneyAgent(i, self)
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self.schedule.add(a)
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# Add the agent to a random grid cell
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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(a, (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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def step(self):
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self.datacollector.collect(self)
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self.schedule.step()
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if __name__ == '__main__':
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fixed_params = {"width": 10,
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"height": 10}
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variable_params = {"N": range(10, 500, 10)}
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batch_run = BatchRunner(MoneyModel,
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variable_params,
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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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@ -18,12 +18,12 @@ class TerroristSpreadModel(FSM, Geo):
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prob_interaction
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"""
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def __init__(self, environment=None, agent_id=0, state=()):
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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def __init__(self, model=None, unique_id=0, state=()):
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super().__init__(model=model, unique_id=unique_id, state=state)
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self.information_spread_intensity = environment.environment_params['information_spread_intensity']
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self.terrorist_additional_influence = environment.environment_params['terrorist_additional_influence']
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self.prob_interaction = environment.environment_params['prob_interaction']
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self.information_spread_intensity = model.environment_params['information_spread_intensity']
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self.terrorist_additional_influence = model.environment_params['terrorist_additional_influence']
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self.prob_interaction = model.environment_params['prob_interaction']
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if self['id'] == self.civilian.id: # Civilian
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self.mean_belief = random.uniform(0.00, 0.5)
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@ -34,10 +34,10 @@ class TerroristSpreadModel(FSM, Geo):
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else:
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raise Exception('Invalid state id: {}'.format(self['id']))
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if 'min_vulnerability' in environment.environment_params:
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self.vulnerability = random.uniform( environment.environment_params['min_vulnerability'], environment.environment_params['max_vulnerability'] )
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if 'min_vulnerability' in model.environment_params:
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self.vulnerability = random.uniform( model.environment_params['min_vulnerability'], model.environment_params['max_vulnerability'] )
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else :
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self.vulnerability = random.uniform( 0, environment.environment_params['max_vulnerability'] )
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self.vulnerability = random.uniform( 0, model.environment_params['max_vulnerability'] )
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@state
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@ -93,11 +93,11 @@ class TrainingAreaModel(FSM, Geo):
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Requires TerroristSpreadModel.
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"""
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def __init__(self, environment=None, agent_id=0, state=()):
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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self.training_influence = environment.environment_params['training_influence']
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if 'min_vulnerability' in environment.environment_params:
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self.min_vulnerability = environment.environment_params['min_vulnerability']
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def __init__(self, model=None, unique_id=0, state=()):
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super().__init__(model=model, unique_id=unique_id, state=state)
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self.training_influence = model.environment_params['training_influence']
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if 'min_vulnerability' in model.environment_params:
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self.min_vulnerability = model.environment_params['min_vulnerability']
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else: self.min_vulnerability = 0
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@default_state
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@ -120,13 +120,13 @@ class HavenModel(FSM, Geo):
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Requires TerroristSpreadModel.
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"""
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def __init__(self, environment=None, agent_id=0, state=()):
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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self.haven_influence = environment.environment_params['haven_influence']
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if 'min_vulnerability' in environment.environment_params:
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self.min_vulnerability = environment.environment_params['min_vulnerability']
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def __init__(self, model=None, unique_id=0, state=()):
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super().__init__(model=model, unique_id=unique_id, state=state)
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self.haven_influence = model.environment_params['haven_influence']
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if 'min_vulnerability' in model.environment_params:
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self.min_vulnerability = model.environment_params['min_vulnerability']
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else: self.min_vulnerability = 0
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self.max_vulnerability = environment.environment_params['max_vulnerability']
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self.max_vulnerability = model.environment_params['max_vulnerability']
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def get_occupants(self, **kwargs):
|
||||
return self.get_neighboring_agents(agent_type=TerroristSpreadModel, **kwargs)
|
||||
@ -162,13 +162,13 @@ class TerroristNetworkModel(TerroristSpreadModel):
|
||||
weight_link_distance
|
||||
"""
|
||||
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
def __init__(self, model=None, unique_id=0, state=()):
|
||||
super().__init__(model=model, unique_id=unique_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']
|
||||
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']
|
||||
|
||||
@state
|
||||
def terrorist(self):
|
||||
|
@ -1,9 +1,8 @@
|
||||
simpy>=4.0
|
||||
networkx>=2.5
|
||||
numpy
|
||||
matplotlib
|
||||
pyyaml>=5.1
|
||||
pandas>=0.23
|
||||
scipy>=1.3
|
||||
SALib>=1.3
|
||||
Jinja2
|
||||
Mesa>=0.8
|
||||
|
11
setup.py
11
setup.py
@ -16,6 +16,12 @@ def parse_requirements(filename):
|
||||
|
||||
install_reqs = parse_requirements("requirements.txt")
|
||||
test_reqs = parse_requirements("test-requirements.txt")
|
||||
extras_require={
|
||||
'mesa': ['mesa>=0.8.9'],
|
||||
'geo': ['scipy>=1.3'],
|
||||
'web': ['tornado']
|
||||
}
|
||||
extras_require['all'] = [dep for package in extras_require.values() for dep in package]
|
||||
|
||||
|
||||
setup(
|
||||
@ -40,10 +46,7 @@ setup(
|
||||
'Operating System :: POSIX',
|
||||
'Programming Language :: Python :: 3'],
|
||||
install_requires=install_reqs,
|
||||
extras_require={
|
||||
'web': ['tornado']
|
||||
|
||||
},
|
||||
extras_require=extras_require,
|
||||
tests_require=test_reqs,
|
||||
setup_requires=['pytest-runner', ],
|
||||
include_package_data=True,
|
||||
|
@ -11,6 +11,7 @@ try:
|
||||
except NameError:
|
||||
basestring = str
|
||||
|
||||
from .agents import *
|
||||
from . import agents
|
||||
from .simulation import *
|
||||
from .environment import Environment
|
||||
@ -18,6 +19,7 @@ from .history import History
|
||||
from . import serialization
|
||||
from . import analysis
|
||||
from .utils import logger
|
||||
from .time import *
|
||||
|
||||
def main():
|
||||
import argparse
|
||||
|
@ -1,40 +1,31 @@
|
||||
import random
|
||||
from . import BaseAgent
|
||||
from . import FSM, state, default_state
|
||||
|
||||
|
||||
class BassModel(BaseAgent):
|
||||
class BassModel(FSM):
|
||||
"""
|
||||
Settings:
|
||||
innovation_prob
|
||||
imitation_prob
|
||||
"""
|
||||
|
||||
def __init__(self, environment, agent_id, state, **kwargs):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
env_params = environment.environment_params
|
||||
self.state['sentimentCorrelation'] = 0
|
||||
sentimentCorrelation = 0
|
||||
|
||||
def step(self):
|
||||
self.behaviour()
|
||||
|
||||
def behaviour(self):
|
||||
# Outside effects
|
||||
if random.random() < self['innovation_prob']:
|
||||
if self.state['id'] == 0:
|
||||
self.state['id'] = 1
|
||||
self.state['sentimentCorrelation'] = 1
|
||||
else:
|
||||
pass
|
||||
|
||||
return
|
||||
|
||||
# Imitation effects
|
||||
if self.state['id'] == 0:
|
||||
aware_neighbors = self.get_neighboring_agents(state_id=1)
|
||||
@default_state
|
||||
@state
|
||||
def innovation(self):
|
||||
if random.random() < self.innovation_prob:
|
||||
self.sentimentCorrelation = 1
|
||||
return self.aware
|
||||
else:
|
||||
aware_neighbors = self.get_neighboring_agents(state_id=self.aware.id)
|
||||
num_neighbors_aware = len(aware_neighbors)
|
||||
if random.random() < (self['imitation_prob']*num_neighbors_aware):
|
||||
self.state['id'] = 1
|
||||
self.state['sentimentCorrelation'] = 1
|
||||
self.sentimentCorrelation = 1
|
||||
return self.aware
|
||||
|
||||
else:
|
||||
pass
|
||||
@state
|
||||
def aware(self):
|
||||
self.die()
|
||||
|
@ -1,8 +1,8 @@
|
||||
import random
|
||||
from . import BaseAgent
|
||||
from . import FSM, state, default_state
|
||||
|
||||
|
||||
class BigMarketModel(BaseAgent):
|
||||
class BigMarketModel(FSM):
|
||||
"""
|
||||
Settings:
|
||||
Names:
|
||||
@ -19,34 +19,25 @@ class BigMarketModel(BaseAgent):
|
||||
sentiment_about [Array]
|
||||
"""
|
||||
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
self.enterprises = environment.environment_params['enterprises']
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.enterprises = self.env.environment_params['enterprises']
|
||||
self.type = ""
|
||||
self.number_of_enterprises = len(environment.environment_params['enterprises'])
|
||||
|
||||
if self.id < self.number_of_enterprises: # Enterprises
|
||||
self.state['id'] = self.id
|
||||
if self.id < len(self.enterprises): # Enterprises
|
||||
self.set_state(self.enterprise.id)
|
||||
self.type = "Enterprise"
|
||||
self.tweet_probability = environment.environment_params['tweet_probability_enterprises'][self.id]
|
||||
else: # normal users
|
||||
self.state['id'] = self.number_of_enterprises
|
||||
self.type = "User"
|
||||
self.set_state(self.user.id)
|
||||
self.tweet_probability = environment.environment_params['tweet_probability_users']
|
||||
self.tweet_relevant_probability = environment.environment_params['tweet_relevant_probability']
|
||||
self.tweet_probability_about = environment.environment_params['tweet_probability_about'] # List
|
||||
self.sentiment_about = environment.environment_params['sentiment_about'] # List
|
||||
|
||||
def step(self):
|
||||
|
||||
if self.id < self.number_of_enterprises: # Enterprise
|
||||
self.enterpriseBehaviour()
|
||||
else: # Usuario
|
||||
self.userBehaviour()
|
||||
for i in range(self.number_of_enterprises): # So that it never is set to 0 if there are not changes (logs)
|
||||
self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
|
||||
|
||||
def enterpriseBehaviour(self):
|
||||
@state
|
||||
def enterprise(self):
|
||||
|
||||
if random.random() < self.tweet_probability: # Tweets
|
||||
aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) # Nodes neighbour users
|
||||
@ -64,12 +55,12 @@ class BigMarketModel(BaseAgent):
|
||||
|
||||
x.attrs['sentiment_enterprise_%s'% self.enterprises[self.id]] = x.sentiment_about[self.id]
|
||||
|
||||
def userBehaviour(self):
|
||||
|
||||
@state
|
||||
def user(self):
|
||||
if random.random() < self.tweet_probability: # Tweets
|
||||
if random.random() < self.tweet_relevant_probability: # Tweets something relevant
|
||||
# Tweet probability per enterprise
|
||||
for i in range(self.number_of_enterprises):
|
||||
for i in range(len(self.enterprises)):
|
||||
random_num = random.random()
|
||||
if random_num < self.tweet_probability_about[i]:
|
||||
# The condition is fulfilled, sentiments are evaluated towards that enterprise
|
||||
@ -82,8 +73,10 @@ class BigMarketModel(BaseAgent):
|
||||
else:
|
||||
# POSITIVO
|
||||
self.userTweets("positive",i)
|
||||
for i in range(len(self.enterprises)): # So that it never is set to 0 if there are not changes (logs)
|
||||
self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
|
||||
|
||||
def userTweets(self,sentiment,enterprise):
|
||||
def userTweets(self, sentiment,enterprise):
|
||||
aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) # Nodes neighbours users
|
||||
for x in aware_neighbors:
|
||||
if sentiment == "positive":
|
||||
|
20
soil/agents/Geo.py
Normal file
20
soil/agents/Geo.py
Normal file
@ -0,0 +1,20 @@
|
||||
from scipy.spatial import cKDTree as KDTree
|
||||
from . import NetworkAgent
|
||||
|
||||
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)]
|
||||
|
@ -10,10 +10,10 @@ class IndependentCascadeModel(BaseAgent):
|
||||
imitation_prob
|
||||
"""
|
||||
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
self.innovation_prob = environment.environment_params['innovation_prob']
|
||||
self.imitation_prob = environment.environment_params['imitation_prob']
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.innovation_prob = self.env.environment_params['innovation_prob']
|
||||
self.imitation_prob = self.env.environment_params['imitation_prob']
|
||||
self.state['time_awareness'] = 0
|
||||
self.state['sentimentCorrelation'] = 0
|
||||
|
||||
|
@ -21,8 +21,8 @@ class SpreadModelM2(BaseAgent):
|
||||
prob_generate_anti_rumor
|
||||
"""
|
||||
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
def __init__(self, model=None, unique_id=0, state=()):
|
||||
super().__init__(model=environment, unique_id=unique_id, state=state)
|
||||
|
||||
self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
|
||||
environment.environment_params['standard_variance'])
|
||||
@ -123,8 +123,8 @@ class ControlModelM2(BaseAgent):
|
||||
"""
|
||||
|
||||
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
def __init__(self, model=None, unique_id=0, state=()):
|
||||
super().__init__(model=environment, unique_id=unique_id, state=state)
|
||||
|
||||
self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
|
||||
environment.environment_params['standard_variance'])
|
||||
|
@ -29,8 +29,8 @@ class SISaModel(FSM):
|
||||
standard_variance
|
||||
"""
|
||||
|
||||
def __init__(self, environment, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
def __init__(self, environment, unique_id=0, state=()):
|
||||
super().__init__(model=environment, unique_id=unique_id, state=state)
|
||||
|
||||
self.neutral_discontent_spon_prob = np.random.normal(self.env['neutral_discontent_spon_prob'],
|
||||
self.env['standard_variance'])
|
||||
|
@ -16,8 +16,8 @@ class SentimentCorrelationModel(BaseAgent):
|
||||
disgust_prob
|
||||
"""
|
||||
|
||||
def __init__(self, environment, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
def __init__(self, environment, unique_id=0, state=()):
|
||||
super().__init__(model=environment, unique_id=unique_id, state=state)
|
||||
self.outside_effects_prob = environment.environment_params['outside_effects_prob']
|
||||
self.anger_prob = environment.environment_params['anger_prob']
|
||||
self.joy_prob = environment.environment_params['joy_prob']
|
||||
|
@ -1,21 +1,15 @@
|
||||
# networkStatus = {} # Dict that will contain the status of every agent in the network
|
||||
# sentimentCorrelationNodeArray = []
|
||||
# for x in range(0, settings.network_params["number_of_nodes"]):
|
||||
# sentimentCorrelationNodeArray.append({'id': x})
|
||||
# Initialize agent states. Let's assume everyone is normal.
|
||||
|
||||
|
||||
import logging
|
||||
from collections import OrderedDict
|
||||
from collections import OrderedDict, defaultdict
|
||||
from copy import deepcopy
|
||||
from functools import partial
|
||||
from scipy.spatial import cKDTree as KDTree
|
||||
import json
|
||||
import simpy
|
||||
import networkx as nx
|
||||
|
||||
from functools import wraps
|
||||
|
||||
from .. import serialization, history, utils
|
||||
from .. import serialization, history, utils, time
|
||||
|
||||
from mesa import Agent
|
||||
|
||||
|
||||
def as_node(agent):
|
||||
@ -24,39 +18,51 @@ def as_node(agent):
|
||||
return agent
|
||||
|
||||
|
||||
class BaseAgent:
|
||||
class BaseAgent(Agent):
|
||||
"""
|
||||
A special simpy BaseAgent that keeps track of its state history.
|
||||
A special Agent that keeps track of its state history.
|
||||
"""
|
||||
|
||||
defaults = {}
|
||||
|
||||
def __init__(self, environment, agent_id, state=None,
|
||||
name=None, interval=None):
|
||||
def __init__(self,
|
||||
unique_id,
|
||||
model,
|
||||
state=None,
|
||||
name=None,
|
||||
interval=None):
|
||||
# Check for REQUIRED arguments
|
||||
assert environment is not None, TypeError('__init__ missing 1 required keyword argument: \'environment\'. '
|
||||
'Cannot be NoneType.')
|
||||
# Initialize agent parameters
|
||||
self.id = agent_id
|
||||
self.name = name or '{}[{}]'.format(type(self).__name__, self.id)
|
||||
|
||||
# Register agent to environment
|
||||
self.env = environment
|
||||
if isinstance(unique_id, Agent):
|
||||
raise Exception()
|
||||
super().__init__(unique_id=unique_id, model=model)
|
||||
self.name = name or '{}[{}]'.format(type(self).__name__, self.unique_id)
|
||||
|
||||
self._neighbors = None
|
||||
self.alive = True
|
||||
real_state = deepcopy(self.defaults)
|
||||
real_state.update(state or {})
|
||||
self.state = real_state
|
||||
self.interval = interval
|
||||
|
||||
self.logger = logging.getLogger(self.env.name).getChild(self.name)
|
||||
self.interval = interval or self.get('interval', getattr(self.model, 'interval', 1))
|
||||
self.logger = logging.getLogger(self.model.name).getChild(self.name)
|
||||
|
||||
if hasattr(self, 'level'):
|
||||
self.logger.setLevel(self.level)
|
||||
|
||||
# initialize every time an instance of the agent is created
|
||||
self.action = self.env.process(self.run())
|
||||
|
||||
# TODO: refactor to clean up mesa compatibility
|
||||
@property
|
||||
def id(self):
|
||||
return self.unique_id
|
||||
|
||||
@property
|
||||
def env(self):
|
||||
return self.model
|
||||
|
||||
@env.setter
|
||||
def env(self, model):
|
||||
self.model = model
|
||||
|
||||
@property
|
||||
def state(self):
|
||||
@ -76,17 +82,17 @@ class BaseAgent:
|
||||
|
||||
@property
|
||||
def environment_params(self):
|
||||
return self.env.environment_params
|
||||
return self.model.environment_params
|
||||
|
||||
@environment_params.setter
|
||||
def environment_params(self, value):
|
||||
self.env.environment_params = value
|
||||
self.model.environment_params = value
|
||||
|
||||
def __getitem__(self, key):
|
||||
if isinstance(key, tuple):
|
||||
key, t_step = key
|
||||
k = history.Key(key=key, t_step=t_step, agent_id=self.id)
|
||||
return self.env[k]
|
||||
return self.model[k]
|
||||
return self._state.get(key, None)
|
||||
|
||||
def __delitem__(self, key):
|
||||
@ -100,7 +106,7 @@ class BaseAgent:
|
||||
k = history.Key(t_step=self.now,
|
||||
agent_id=self.id,
|
||||
key=key)
|
||||
self.env[k] = value
|
||||
self.model[k] = value
|
||||
|
||||
def items(self):
|
||||
return self._state.items()
|
||||
@ -111,29 +117,33 @@ class BaseAgent:
|
||||
@property
|
||||
def now(self):
|
||||
try:
|
||||
return self.env.now
|
||||
return self.model.now
|
||||
except AttributeError:
|
||||
# No environment
|
||||
return None
|
||||
|
||||
def run(self):
|
||||
if self.interval is not None:
|
||||
interval = self.interval
|
||||
elif 'interval' in self:
|
||||
interval = self['interval']
|
||||
else:
|
||||
interval = self.env.interval
|
||||
while self.alive:
|
||||
res = self.step()
|
||||
yield res or self.env.timeout(interval)
|
||||
|
||||
def die(self, remove=False):
|
||||
self.alive = False
|
||||
if remove:
|
||||
self.remove_node(self.id)
|
||||
|
||||
def step(self):
|
||||
return
|
||||
if not self.alive:
|
||||
return time.When('inf')
|
||||
return super().step() or time.Delta(self.interval)
|
||||
|
||||
def log(self, message, *args, level=logging.INFO, **kwargs):
|
||||
if not self.logger.isEnabledFor(level):
|
||||
return
|
||||
message = message + " ".join(str(i) for i in args)
|
||||
message = " @{:>3}: {}".format(self.now, message)
|
||||
for k, v in kwargs:
|
||||
message += " {k}={v} ".format(k, v)
|
||||
extra = {}
|
||||
extra['now'] = self.now
|
||||
extra['unique_id'] = self.unique_id
|
||||
extra['agent_name'] = self.name
|
||||
return self.logger.log(level, message, extra=extra)
|
||||
|
||||
def debug(self, *args, **kwargs):
|
||||
return self.log(*args, level=logging.DEBUG, **kwargs)
|
||||
@ -149,7 +159,7 @@ class BaseAgent:
|
||||
'''
|
||||
state = {}
|
||||
state['id'] = self.id
|
||||
state['environment'] = self.env
|
||||
state['environment'] = self.model
|
||||
state['_state'] = self._state
|
||||
return state
|
||||
|
||||
@ -157,19 +167,19 @@ class BaseAgent:
|
||||
'''
|
||||
Get back a serialized agent and try to re-compose it
|
||||
'''
|
||||
self.id = state['id']
|
||||
self.state_id = state['id']
|
||||
self._state = state['_state']
|
||||
self.env = state['environment']
|
||||
self.model = state['environment']
|
||||
|
||||
class NetworkAgent(BaseAgent):
|
||||
|
||||
@property
|
||||
def topology(self):
|
||||
return self.env.G
|
||||
return self.model.G
|
||||
|
||||
@property
|
||||
def G(self):
|
||||
return self.env.G
|
||||
return self.model.G
|
||||
|
||||
def count_agents(self, **kwargs):
|
||||
return len(list(self.get_agents(**kwargs)))
|
||||
@ -182,37 +192,26 @@ class NetworkAgent(BaseAgent):
|
||||
|
||||
def get_agents(self, agents=None, limit_neighbors=False, **kwargs):
|
||||
if limit_neighbors:
|
||||
agents = self.topology.neighbors(self.id)
|
||||
agents = self.topology.neighbors(self.unique_id)
|
||||
|
||||
agents = self.env.get_agents(agents)
|
||||
agents = self.model.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)
|
||||
message = " @{:>3}: {}".format(self.now, message)
|
||||
for k, v in kwargs:
|
||||
message += " {k}={v} ".format(k, v)
|
||||
extra = {}
|
||||
extra['now'] = self.now
|
||||
extra['agent_id'] = self.id
|
||||
extra['agent_name'] = self.name
|
||||
return self.logger.log(level, message, extra=extra)
|
||||
|
||||
def subgraph(self, center=True, **kwargs):
|
||||
include = [self] if center else []
|
||||
return self.topology.subgraph(n.id for n in self.get_agents(**kwargs)+include)
|
||||
return self.topology.subgraph(n.unique_id for n in self.get_agents(**kwargs)+include)
|
||||
|
||||
def remove_node(self, agent_id):
|
||||
self.topology.remove_node(agent_id)
|
||||
def remove_node(self, unique_id):
|
||||
self.topology.remove_node(unique_id)
|
||||
|
||||
def add_edge(self, other, edge_attr_dict=None, *edge_attrs):
|
||||
# return super(NetworkAgent, self).add_edge(node1=self.id, node2=other, **kwargs)
|
||||
if self.id not in self.topology.nodes(data=False):
|
||||
raise ValueError('{} not in list of existing agents in the network'.format(self.id))
|
||||
if self.unique_id not in self.topology.nodes(data=False):
|
||||
raise ValueError('{} not in list of existing agents in the network'.format(self.unique_id))
|
||||
if other not in self.topology.nodes(data=False):
|
||||
raise ValueError('{} not in list of existing agents in the network'.format(other))
|
||||
|
||||
self.topology.add_edge(self.id, other, edge_attr_dict=edge_attr_dict, *edge_attrs)
|
||||
self.topology.add_edge(self.unique_id, other, edge_attr_dict=edge_attr_dict, *edge_attrs)
|
||||
|
||||
|
||||
def ego_search(self, steps=1, center=False, node=None, **kwargs):
|
||||
@ -223,17 +222,17 @@ class NetworkAgent(BaseAgent):
|
||||
|
||||
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.topology)
|
||||
self.env._last_step = self.now
|
||||
return self.env._degree[node]
|
||||
if force or (not hasattr(self.model, '_degree')) or getattr(self.model, '_last_step', 0) < self.now:
|
||||
self.model._degree = nx.degree_centrality(self.topology)
|
||||
self.model._last_step = self.now
|
||||
return self.model._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.topology)
|
||||
self.env._last_step = self.now
|
||||
return self.env._betweenness[node]
|
||||
if force or (not hasattr(self.model, '_betweenness')) or getattr(self.model, '_last_step', 0) < self.now:
|
||||
self.model._betweenness = nx.betweenness_centrality(self.topology)
|
||||
self.model._last_step = self.now
|
||||
return self.model._betweenness[node]
|
||||
|
||||
|
||||
def state(name=None):
|
||||
@ -301,36 +300,29 @@ class FSM(NetworkAgent, metaclass=MetaFSM):
|
||||
super(FSM, self).__init__(*args, **kwargs)
|
||||
if 'id' not in self.state:
|
||||
if not self.default_state:
|
||||
raise ValueError('No default state specified for {}'.format(self.id))
|
||||
raise ValueError('No default state specified for {}'.format(self.unique_id))
|
||||
self['id'] = self.default_state.id
|
||||
self._next_change = simpy.core.Infinity
|
||||
self._next_state = self.state
|
||||
|
||||
self.set_state(self.state['id'])
|
||||
|
||||
def step(self):
|
||||
if self._next_change < self.now:
|
||||
next_state = self._next_state
|
||||
self._next_change = simpy.core.Infinity
|
||||
self['id'] = next_state
|
||||
elif 'id' in self.state:
|
||||
next_state = self['id']
|
||||
elif self.default_state:
|
||||
next_state = self.default_state.id
|
||||
else:
|
||||
raise Exception('{} has no valid state id or default state'.format(self))
|
||||
if next_state not in self.states:
|
||||
raise Exception('{} is not a valid id for {}'.format(next_state, self))
|
||||
return self.states[next_state](self)
|
||||
|
||||
def next_state(self, state):
|
||||
self._next_change = self.now
|
||||
self._next_state = state
|
||||
self.debug(f'Agent {self.unique_id} @ state {self["id"]}')
|
||||
interval = super().step()
|
||||
if 'id' not in self:
|
||||
if 'id' in self.state:
|
||||
self.set_state(self['state_id'])
|
||||
elif self.default_state:
|
||||
self.set_state(self.default_state.id)
|
||||
else:
|
||||
raise Exception('{} has no valid state id or default state'.format(self))
|
||||
return self.states[self['id']](self) or interval
|
||||
|
||||
def set_state(self, state):
|
||||
if hasattr(state, 'id'):
|
||||
state = state.id
|
||||
if state not in self.states:
|
||||
raise ValueError('{} is not a valid state'.format(state))
|
||||
self['id'] = state
|
||||
self['state_id'] = state
|
||||
return state
|
||||
|
||||
|
||||
@ -349,9 +341,6 @@ def prob(prob=1):
|
||||
return r < prob
|
||||
|
||||
|
||||
STATIC_THRESHOLD = (-1, -1)
|
||||
|
||||
|
||||
def calculate_distribution(network_agents=None,
|
||||
agent_type=None):
|
||||
'''
|
||||
@ -379,7 +368,7 @@ def calculate_distribution(network_agents=None,
|
||||
'agent_type_1'.
|
||||
'''
|
||||
if network_agents:
|
||||
network_agents = deepcopy(network_agents)
|
||||
network_agents = [deepcopy(agent) for agent in network_agents if not hasattr(agent, 'id')]
|
||||
elif agent_type:
|
||||
network_agents = [{'agent_type': agent_type}]
|
||||
else:
|
||||
@ -394,7 +383,6 @@ def calculate_distribution(network_agents=None,
|
||||
acc = 0
|
||||
for v in network_agents:
|
||||
if 'ids' in v:
|
||||
v['threshold'] = STATIC_THRESHOLD
|
||||
continue
|
||||
upper = acc + (v['weight']/total)
|
||||
v['threshold'] = [acc, upper]
|
||||
@ -409,7 +397,7 @@ def serialize_type(agent_type, known_modules=[], **kwargs):
|
||||
return serialization.serialize(agent_type, known_modules=known_modules, **kwargs)[1] # Get the name of the class
|
||||
|
||||
|
||||
def serialize_distribution(network_agents, known_modules=[]):
|
||||
def serialize_definition(network_agents, known_modules=[]):
|
||||
'''
|
||||
When serializing an agent distribution, remove the thresholds, in order
|
||||
to avoid cluttering the YAML definition file.
|
||||
@ -431,7 +419,7 @@ def deserialize_type(agent_type, known_modules=[]):
|
||||
return agent_type
|
||||
|
||||
|
||||
def deserialize_distribution(ind, **kwargs):
|
||||
def deserialize_definition(ind, **kwargs):
|
||||
d = deepcopy(ind)
|
||||
for v in d:
|
||||
v['agent_type'] = deserialize_type(v['agent_type'], **kwargs)
|
||||
@ -452,44 +440,84 @@ def _validate_states(states, topology):
|
||||
def _convert_agent_types(ind, to_string=False, **kwargs):
|
||||
'''Convenience method to allow specifying agents by class or class name.'''
|
||||
if to_string:
|
||||
return serialize_distribution(ind, **kwargs)
|
||||
return deserialize_distribution(ind, **kwargs)
|
||||
return serialize_definition(ind, **kwargs)
|
||||
return deserialize_definition(ind, **kwargs)
|
||||
|
||||
|
||||
def _agent_from_distribution(distribution, value=-1, agent_id=None):
|
||||
def _agent_from_definition(definition, value=-1, unique_id=None):
|
||||
"""Used in the initialization of agents given an agent distribution."""
|
||||
if value < 0:
|
||||
value = random.random()
|
||||
for d in sorted(distribution, key=lambda x: x['threshold']):
|
||||
threshold = d['threshold']
|
||||
for d in sorted(definition, key=lambda x: x.get('threshold')):
|
||||
threshold = d.get('threshold', (-1, -1))
|
||||
# Check if the definition matches by id (first) or by threshold
|
||||
if not ((agent_id is not None and threshold == STATIC_THRESHOLD and agent_id in d['ids']) or \
|
||||
(value >= threshold[0] and value < threshold[1])):
|
||||
continue
|
||||
state = {}
|
||||
if 'state' in d:
|
||||
state = deepcopy(d['state'])
|
||||
return d['agent_type'], state
|
||||
if (unique_id is not None and unique_id in d.get('ids', [])) or \
|
||||
(value >= threshold[0] and value < threshold[1]):
|
||||
state = {}
|
||||
if 'state' in d:
|
||||
state = deepcopy(d['state'])
|
||||
return d['agent_type'], state
|
||||
|
||||
raise Exception('Distribution for value {} not found in: {}'.format(value, distribution))
|
||||
raise Exception('Definition for value {} not found in: {}'.format(value, definition))
|
||||
|
||||
|
||||
class Geo(NetworkAgent):
|
||||
'''In this type of network, nodes have a "pos" attribute.'''
|
||||
def _definition_to_dict(definition, size=None, default_state=None):
|
||||
state = default_state or {}
|
||||
agents = {}
|
||||
remaining = {}
|
||||
if size:
|
||||
for ix in range(size):
|
||||
remaining[ix] = copy(state)
|
||||
else:
|
||||
remaining = defaultdict(lambda x: copy(state))
|
||||
|
||||
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)
|
||||
distro = sorted([item for item in definition if 'weight' in item])
|
||||
|
||||
G = self.subgraph(**kwargs)
|
||||
ix = 0
|
||||
def init_agent(item, id=ix):
|
||||
while id in agents:
|
||||
id += 1
|
||||
|
||||
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)]
|
||||
agent = remaining[id]
|
||||
agent['state'].update(copy(item.get('state', {})))
|
||||
agents[id] = agent
|
||||
del remaining[id]
|
||||
return agent
|
||||
|
||||
for item in definition:
|
||||
if 'ids' in item:
|
||||
ids = item['ids']
|
||||
del item['ids']
|
||||
for id in ids:
|
||||
agent = init_agent(item, id)
|
||||
|
||||
for item in definition:
|
||||
if 'number' in item:
|
||||
times = item['number']
|
||||
del item['number']
|
||||
for times in range(times):
|
||||
if size:
|
||||
ix = random.choice(remaining.keys())
|
||||
agent = init_agent(item, id)
|
||||
else:
|
||||
agent = init_agent(item)
|
||||
if not size:
|
||||
return agents
|
||||
|
||||
if len(remaining) < 0:
|
||||
raise Exception('Invalid definition. Too many agents to add')
|
||||
|
||||
|
||||
total_weight = float(sum(s['weight'] for s in distro))
|
||||
unit = size / total_weight
|
||||
|
||||
for item in distro:
|
||||
times = unit * item['weight']
|
||||
del item['weight']
|
||||
for times in range(times):
|
||||
ix = random.choice(remaining.keys())
|
||||
agent = init_agent(item, id)
|
||||
return agents
|
||||
|
||||
|
||||
def select(agents, state_id=None, agent_type=None, ignore=None, iterator=False, **kwargs):
|
||||
@ -502,22 +530,21 @@ def select(agents, state_id=None, agent_type=None, ignore=None, iterator=False,
|
||||
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
|
||||
checks = []
|
||||
|
||||
f = agents
|
||||
|
||||
f = filter(matches_all, agents)
|
||||
if ignore:
|
||||
f = filter(lambda x: x not in ignore, f)
|
||||
|
||||
if state_id is not None:
|
||||
f = filter(lambda agent: agent.state.get('id', None) in state_id, f)
|
||||
|
||||
if agent_type is not None:
|
||||
f = filter(lambda agent: isinstance(agent, agent_type), f)
|
||||
for k, v in kwargs.items():
|
||||
f = filter(lambda agent: agent.state.get(k, None) == v, f)
|
||||
|
||||
if iterator:
|
||||
return f
|
||||
return list(f)
|
||||
@ -530,3 +557,10 @@ from .ModelM2 import *
|
||||
from .SentimentCorrelationModel import *
|
||||
from .SISaModel import *
|
||||
from .CounterModel import *
|
||||
|
||||
try:
|
||||
import scipy
|
||||
from .Geo import Geo
|
||||
except ImportError:
|
||||
import sys
|
||||
print('Could not load the Geo Agent, scipy is not installed', file=sys.stderr)
|
||||
|
@ -61,7 +61,12 @@ def convert_row(row):
|
||||
|
||||
|
||||
def convert_types_slow(df):
|
||||
'''This is a slow operation.'''
|
||||
'''
|
||||
Go over every column in a dataframe and convert it to the type determined by the `get_types`
|
||||
function.
|
||||
|
||||
This is a slow operation.
|
||||
'''
|
||||
dtypes = get_types(df)
|
||||
for k, v in dtypes.items():
|
||||
t = df[df['key']==k]
|
||||
@ -102,6 +107,9 @@ def process(df, **kwargs):
|
||||
|
||||
|
||||
def get_types(df):
|
||||
'''
|
||||
Get the value type for every key stored in a raw history dataframe.
|
||||
'''
|
||||
dtypes = df.groupby(by=['key'])['value_type'].unique()
|
||||
return {k:v[0] for k,v in dtypes.iteritems()}
|
||||
|
||||
@ -126,8 +134,14 @@ def process_one(df, *keys, columns=['key', 'agent_id'], values='value',
|
||||
|
||||
|
||||
def get_count(df, *keys):
|
||||
'''
|
||||
For every t_step and key, get the value count.
|
||||
|
||||
The result is a dataframe with `t_step` as index, an a multiindex column based on `key` and the values found for each `key`.
|
||||
'''
|
||||
if keys:
|
||||
df = df[list(keys)]
|
||||
df.columns = df.columns.remove_unused_levels()
|
||||
counts = pd.DataFrame()
|
||||
for key in df.columns.levels[0]:
|
||||
g = df[[key]].apply(pd.Series.value_counts, axis=1).fillna(0)
|
||||
@ -137,10 +151,25 @@ def get_count(df, *keys):
|
||||
return counts
|
||||
|
||||
|
||||
def get_majority(df, *keys):
|
||||
'''
|
||||
For every t_step and key, get the value of the majority of agents
|
||||
|
||||
The result is a dataframe with `t_step` as index, and columns based on `key`.
|
||||
'''
|
||||
df = get_count(df, *keys)
|
||||
return df.stack(level=0).idxmax(axis=1).unstack()
|
||||
|
||||
|
||||
def get_value(df, *keys, aggfunc='sum'):
|
||||
'''
|
||||
For every t_step and key, get the value of *numeric columns*, aggregated using a specific function.
|
||||
'''
|
||||
if keys:
|
||||
df = df[list(keys)]
|
||||
return df.groupby(axis=1, level=0).agg(aggfunc)
|
||||
df.columns = df.columns.remove_unused_levels()
|
||||
df = df.select_dtypes('number')
|
||||
return df.groupby(level='key', axis=1).agg(aggfunc)
|
||||
|
||||
|
||||
def plot_all(*args, plot_args={}, **kwargs):
|
||||
|
26
soil/datacollection.py
Normal file
26
soil/datacollection.py
Normal file
@ -0,0 +1,26 @@
|
||||
from mesa import DataCollector as MDC
|
||||
|
||||
class SoilDataCollector(MDC):
|
||||
|
||||
|
||||
def __init__(self, environment, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
# Populate model and env reporters so they have a key per
|
||||
# So they can be shown in the web interface
|
||||
self.environment = environment
|
||||
|
||||
|
||||
@property
|
||||
def model_vars(self):
|
||||
pass
|
||||
|
||||
@model_vars.setter
|
||||
def model_vars(self, value):
|
||||
pass
|
||||
|
||||
@property
|
||||
def agent_reporters(self):
|
||||
self.model._history._
|
||||
|
||||
pass
|
||||
|
@ -1,28 +1,29 @@
|
||||
import os
|
||||
import sqlite3
|
||||
import time
|
||||
import csv
|
||||
import math
|
||||
import random
|
||||
import simpy
|
||||
import yaml
|
||||
import tempfile
|
||||
import pandas as pd
|
||||
from time import time as current_time
|
||||
from copy import deepcopy
|
||||
from networkx.readwrite import json_graph
|
||||
|
||||
import networkx as nx
|
||||
import simpy
|
||||
|
||||
from . import serialization, agents, analysis, history, utils
|
||||
from mesa import Model
|
||||
|
||||
from . import serialization, agents, analysis, history, utils, time
|
||||
|
||||
# These properties will be copied when pickling/unpickling the environment
|
||||
_CONFIG_PROPS = [ 'name',
|
||||
'states',
|
||||
'default_state',
|
||||
'interval',
|
||||
'states',
|
||||
'default_state',
|
||||
'interval',
|
||||
]
|
||||
|
||||
class Environment(simpy.Environment):
|
||||
class Environment(Model):
|
||||
"""
|
||||
The environment is key in a simulation. It contains the network topology,
|
||||
a reference to network and environment agents, as well as the environment
|
||||
@ -39,25 +40,41 @@ class Environment(simpy.Environment):
|
||||
states=None,
|
||||
default_state=None,
|
||||
interval=1,
|
||||
network_params=None,
|
||||
seed=None,
|
||||
topology=None,
|
||||
schedule=None,
|
||||
initial_time=0,
|
||||
**environment_params):
|
||||
environment_params=None,
|
||||
dir_path=None,
|
||||
**kwargs):
|
||||
|
||||
|
||||
super().__init__()
|
||||
|
||||
self.schedule = schedule
|
||||
if schedule is None:
|
||||
self.schedule = time.TimedActivation()
|
||||
|
||||
self.name = name or 'UnnamedEnvironment'
|
||||
seed = seed or time.time()
|
||||
seed = seed or current_time()
|
||||
random.seed(seed)
|
||||
if isinstance(states, list):
|
||||
states = dict(enumerate(states))
|
||||
self.states = deepcopy(states) if states else {}
|
||||
self.default_state = deepcopy(default_state) or {}
|
||||
|
||||
if topology is None:
|
||||
network_params = network_params or {}
|
||||
topology = serialization.load_network(network_params,
|
||||
dir_path=dir_path)
|
||||
if not topology:
|
||||
topology = nx.Graph()
|
||||
self.G = nx.Graph(topology)
|
||||
|
||||
super().__init__(initial_time=initial_time)
|
||||
self.environment_params = environment_params
|
||||
|
||||
self.environment_params = environment_params or {}
|
||||
self.environment_params.update(kwargs)
|
||||
|
||||
self._env_agents = {}
|
||||
self.interval = interval
|
||||
@ -66,8 +83,26 @@ class Environment(simpy.Environment):
|
||||
self['SEED'] = seed
|
||||
# Add environment agents first, so their events get
|
||||
# executed before network agents
|
||||
self.environment_agents = environment_agents or []
|
||||
self.network_agents = network_agents or []
|
||||
|
||||
|
||||
if network_agents:
|
||||
distro = agents.calculate_distribution(network_agents)
|
||||
self.network_agents = agents._convert_agent_types(distro)
|
||||
else:
|
||||
self.network_agents = []
|
||||
|
||||
environment_agents = environment_agents or []
|
||||
if environment_agents:
|
||||
distro = agents.calculate_distribution(environment_agents)
|
||||
environment_agents = agents._convert_agent_types(distro)
|
||||
self.environment_agents = environment_agents
|
||||
|
||||
|
||||
@property
|
||||
def now(self):
|
||||
if self.schedule:
|
||||
return self.schedule.time
|
||||
raise Exception('The environment has not been scheduled, so it has no sense of time')
|
||||
|
||||
@property
|
||||
def agents(self):
|
||||
@ -81,15 +116,9 @@ class Environment(simpy.Environment):
|
||||
|
||||
@environment_agents.setter
|
||||
def environment_agents(self, environment_agents):
|
||||
# Set up environmental agent
|
||||
self._env_agents = {}
|
||||
for item in environment_agents:
|
||||
kwargs = deepcopy(item)
|
||||
atype = kwargs.pop('agent_type')
|
||||
kwargs['agent_id'] = kwargs.get('agent_id', atype.__name__)
|
||||
kwargs['state'] = kwargs.get('state', {})
|
||||
a = atype(environment=self, **kwargs)
|
||||
self._env_agents[a.id] = a
|
||||
self._environment_agents = environment_agents
|
||||
|
||||
self._env_agents = agents._definition_to_dict(definition=environment_agents)
|
||||
|
||||
@property
|
||||
def network_agents(self):
|
||||
@ -102,9 +131,9 @@ class Environment(simpy.Environment):
|
||||
def network_agents(self, network_agents):
|
||||
self._network_agents = network_agents
|
||||
for ix in self.G.nodes():
|
||||
self.init_agent(ix, agent_distribution=network_agents)
|
||||
self.init_agent(ix, agent_definitions=network_agents)
|
||||
|
||||
def init_agent(self, agent_id, agent_distribution):
|
||||
def init_agent(self, agent_id, agent_definitions):
|
||||
node = self.G.nodes[agent_id]
|
||||
init = False
|
||||
state = dict(node)
|
||||
@ -119,8 +148,8 @@ class Environment(simpy.Environment):
|
||||
|
||||
if agent_type:
|
||||
agent_type = agents.deserialize_type(agent_type)
|
||||
elif agent_distribution:
|
||||
agent_type, state = agents._agent_from_distribution(agent_distribution, agent_id=agent_id)
|
||||
elif agent_definitions:
|
||||
agent_type, state = agents._agent_from_definition(agent_definitions, unique_id=agent_id)
|
||||
else:
|
||||
serialization.logger.debug('Skipping node {}'.format(agent_id))
|
||||
return
|
||||
@ -136,8 +165,8 @@ class Environment(simpy.Environment):
|
||||
a = None
|
||||
if agent_type:
|
||||
state = defstate
|
||||
a = agent_type(environment=self,
|
||||
agent_id=agent_id,
|
||||
a = agent_type(model=self,
|
||||
unique_id=agent_id,
|
||||
state=state)
|
||||
node['agent'] = a
|
||||
return a
|
||||
@ -159,30 +188,18 @@ class Environment(simpy.Environment):
|
||||
|
||||
def run(self, until, *args, **kwargs):
|
||||
self._save_state()
|
||||
super().run(until, *args, **kwargs)
|
||||
for agent in self.agents:
|
||||
self.schedule.add(agent)
|
||||
|
||||
while self.schedule.next_time <= until and not math.isinf(self.schedule.next_time):
|
||||
self.schedule.step(until=until)
|
||||
utils.logger.debug(f'Simulation step {self.schedule.time}/{until}. Next: {self.schedule.next_time}')
|
||||
self._history.flush_cache()
|
||||
|
||||
def _save_state(self, now=None):
|
||||
serialization.logger.debug('Saving state @{}'.format(self.now))
|
||||
self._history.save_records(self.state_to_tuples(now=now))
|
||||
|
||||
def save_state(self):
|
||||
'''
|
||||
:DEPRECATED:
|
||||
Periodically save the state of the environment and the agents.
|
||||
'''
|
||||
self._save_state()
|
||||
while self.peek() != simpy.core.Infinity:
|
||||
delay = max(self.peek() - self.now, self.interval)
|
||||
serialization.logger.debug('Step: {}'.format(self.now))
|
||||
ev = self.event()
|
||||
ev._ok = True
|
||||
# Schedule the event with minimum priority so
|
||||
# that it executes before all agents
|
||||
self.schedule(ev, -999, delay)
|
||||
yield ev
|
||||
self._save_state()
|
||||
|
||||
def __getitem__(self, key):
|
||||
if isinstance(key, tuple):
|
||||
self._history.flush_cache()
|
||||
@ -329,7 +346,7 @@ class Environment(simpy.Environment):
|
||||
state['G'] = json_graph.node_link_data(self.G)
|
||||
state['environment_agents'] = self._env_agents
|
||||
state['history'] = self._history
|
||||
state['_now'] = self._now
|
||||
state['schedule'] = self.schedule
|
||||
return state
|
||||
|
||||
def __setstate__(self, state):
|
||||
@ -338,7 +355,8 @@ class Environment(simpy.Environment):
|
||||
self._env_agents = state['environment_agents']
|
||||
self.G = json_graph.node_link_graph(state['G'])
|
||||
self._history = state['history']
|
||||
self._now = state['_now']
|
||||
# self._env = None
|
||||
self.schedule = state['schedule']
|
||||
self._queue = []
|
||||
|
||||
|
||||
|
@ -52,7 +52,7 @@ class History:
|
||||
|
||||
with self.db:
|
||||
logger.debug('Creating database {}'.format(self.db_path))
|
||||
self.db.execute('''CREATE TABLE IF NOT EXISTS history (agent_id text, t_step int, key text, value text)''')
|
||||
self.db.execute('''CREATE TABLE IF NOT EXISTS history (agent_id text, t_step real, key text, value text)''')
|
||||
self.db.execute('''CREATE TABLE IF NOT EXISTS value_types (key text, value_type text)''')
|
||||
self.db.execute('''CREATE TABLE IF NOT EXISTS stats (trial_id text)''')
|
||||
self.db.execute('''CREATE UNIQUE INDEX IF NOT EXISTS idx_history ON history (agent_id, t_step, key);''')
|
||||
@ -103,7 +103,7 @@ class History:
|
||||
dtype = 'real'
|
||||
int(value)
|
||||
dtype = 'int'
|
||||
except ValueError:
|
||||
except (ValueError, OverflowError):
|
||||
pass
|
||||
self.db.execute('ALTER TABLE stats ADD "{}" "{}"'.format(column, dtype))
|
||||
self._stats_columns.append(column)
|
||||
@ -167,6 +167,7 @@ class History:
|
||||
with self.db:
|
||||
self.db.execute("replace into value_types (key, value_type) values (?, ?)", (key, name))
|
||||
value = self._dtypes[key][1](value)
|
||||
|
||||
self._tups.append(Record(agent_id=agent_id,
|
||||
t_step=t_step,
|
||||
key=key,
|
||||
@ -183,9 +184,9 @@ class History:
|
||||
raise Exception('DB in readonly mode')
|
||||
logger.debug('Flushing cache {}'.format(self.db_path))
|
||||
with self.db:
|
||||
for rec in self._tups:
|
||||
self.db.execute("replace into history(agent_id, t_step, key, value) values (?, ?, ?, ?)", (rec.agent_id, rec.t_step, rec.key, rec.value))
|
||||
self._tups = list()
|
||||
self.db.executemany("replace into history(agent_id, t_step, key, value) values (?, ?, ?, ?)", self._tups)
|
||||
# (rec.agent_id, rec.t_step, rec.key, rec.value))
|
||||
self._tups.clear()
|
||||
|
||||
def to_tuples(self):
|
||||
self.flush_cache()
|
||||
@ -209,6 +210,7 @@ class History:
|
||||
self._dtypes[k] = (v, serializer, deserializer)
|
||||
|
||||
def __getitem__(self, key):
|
||||
# raise NotImplementedError()
|
||||
self.flush_cache()
|
||||
key = Key(*key)
|
||||
agent_ids = [key.agent_id] if key.agent_id is not None else []
|
||||
@ -223,7 +225,7 @@ class History:
|
||||
return r.value()
|
||||
return r
|
||||
|
||||
def read_sql(self, keys=None, agent_ids=None, t_steps=None, convert_types=False, limit=-1):
|
||||
def read_sql(self, keys=None, agent_ids=None, not_agent_ids=None, t_steps=None, convert_types=False, limit=-1):
|
||||
|
||||
self._read_types()
|
||||
|
||||
@ -233,7 +235,8 @@ class History:
|
||||
return ",".join(map(lambda x: "\'{}\'".format(x), v))
|
||||
|
||||
filters = [("key in ({})".format(escape_and_join(keys)), keys),
|
||||
("agent_id in ({})".format(escape_and_join(agent_ids)), agent_ids)
|
||||
("agent_id in ({})".format(escape_and_join(agent_ids)), agent_ids),
|
||||
("agent_id not in ({})".format(escape_and_join(not_agent_ids)), not_agent_ids)
|
||||
]
|
||||
filters = list(k[0] for k in filters if k[1])
|
||||
|
||||
|
@ -13,7 +13,6 @@ from jinja2 import Template
|
||||
|
||||
|
||||
logger = logging.getLogger('soil')
|
||||
logger.setLevel(logging.INFO)
|
||||
|
||||
|
||||
def load_network(network_params, dir_path=None):
|
||||
@ -51,6 +50,9 @@ def load_network(network_params, dir_path=None):
|
||||
|
||||
|
||||
def load_file(infile):
|
||||
folder = os.path.dirname(infile)
|
||||
if folder not in sys.path:
|
||||
sys.path.append(folder)
|
||||
with open(infile, 'r') as f:
|
||||
return list(chain.from_iterable(map(expand_template, load_string(f))))
|
||||
|
||||
|
@ -143,7 +143,7 @@ class Simulation:
|
||||
return list(self.run_gen(*args, **kwargs))
|
||||
|
||||
def _run_sync_or_async(self, parallel=False, *args, **kwargs):
|
||||
if parallel:
|
||||
if parallel and not os.environ.get('SENPY_DEBUG', None):
|
||||
p = Pool()
|
||||
func = partial(self.run_trial_exceptions,
|
||||
*args,
|
||||
@ -226,12 +226,14 @@ class Simulation:
|
||||
opts.update({
|
||||
'name': trial_id,
|
||||
'topology': self.topology.copy(),
|
||||
'network_params': self.network_params,
|
||||
'seed': '{}_trial_{}'.format(self.seed, trial_id),
|
||||
'initial_time': 0,
|
||||
'interval': self.interval,
|
||||
'network_agents': self.network_agents,
|
||||
'initial_time': 0,
|
||||
'states': self.states,
|
||||
'dir_path': self.dir_path,
|
||||
'default_state': self.default_state,
|
||||
'environment_agents': self.environment_agents,
|
||||
})
|
||||
@ -304,10 +306,10 @@ class Simulation:
|
||||
if k[0] != '_':
|
||||
state[k] = v
|
||||
state['topology'] = json_graph.node_link_data(self.topology)
|
||||
state['network_agents'] = agents.serialize_distribution(self.network_agents,
|
||||
known_modules = [])
|
||||
state['environment_agents'] = agents.serialize_distribution(self.environment_agents,
|
||||
known_modules = [])
|
||||
state['network_agents'] = agents.serialize_definition(self.network_agents,
|
||||
known_modules = [])
|
||||
state['environment_agents'] = agents.serialize_definition(self.environment_agents,
|
||||
known_modules = [])
|
||||
state['environment_class'] = serialization.serialize(self.environment_class,
|
||||
known_modules=['soil.environment'])[1] # func, name
|
||||
if state['load_module'] is None:
|
||||
@ -325,7 +327,6 @@ class Simulation:
|
||||
known_modules=[self.load_module])
|
||||
self.environment_class = serialization.deserialize(self.environment_class,
|
||||
known_modules=[self.load_module, 'soil.environment', ]) # func, name
|
||||
return state
|
||||
|
||||
|
||||
def all_from_config(config):
|
||||
|
84
soil/time.py
Normal file
84
soil/time.py
Normal file
@ -0,0 +1,84 @@
|
||||
from mesa.time import BaseScheduler
|
||||
from queue import Empty
|
||||
from heapq import heappush, heappop
|
||||
import math
|
||||
from .utils import logger
|
||||
from mesa import Agent
|
||||
|
||||
|
||||
class When:
|
||||
def __init__(self, time):
|
||||
self._time = float(time)
|
||||
|
||||
def abs(self, time):
|
||||
return self._time
|
||||
|
||||
|
||||
class Delta:
|
||||
def __init__(self, delta):
|
||||
self._delta = delta
|
||||
|
||||
def abs(self, time):
|
||||
return time + self._delta
|
||||
|
||||
|
||||
class TimedActivation(BaseScheduler):
|
||||
"""A scheduler which activates each agent when the agent requests.
|
||||
In each activation, each agent will update its 'next_time'.
|
||||
"""
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(self)
|
||||
self._queue = []
|
||||
self.next_time = 0
|
||||
|
||||
def add(self, agent: Agent):
|
||||
if agent.unique_id not in self._agents:
|
||||
heappush(self._queue, (self.time, agent.unique_id))
|
||||
super().add(agent)
|
||||
|
||||
def step(self, until: float =float('inf')) -> None:
|
||||
"""
|
||||
Executes agents in order, one at a time. After each step,
|
||||
an agent will signal when it wants to be scheduled next.
|
||||
"""
|
||||
|
||||
when = None
|
||||
agent_id = None
|
||||
unsched = []
|
||||
until = until or float('inf')
|
||||
|
||||
if not self._queue:
|
||||
self.time = until
|
||||
self.next_time = float('inf')
|
||||
return
|
||||
|
||||
(when, agent_id) = self._queue[0]
|
||||
|
||||
if until and when > until:
|
||||
self.time = until
|
||||
self.next_time = when
|
||||
return
|
||||
|
||||
self.time = when
|
||||
next_time = float("inf")
|
||||
|
||||
while when == self.time:
|
||||
heappop(self._queue)
|
||||
logger.debug(f'Stepping agent {agent_id}')
|
||||
when = (self._agents[agent_id].step() or Delta(1)).abs(self.time)
|
||||
heappush(self._queue, (when, agent_id))
|
||||
if when < next_time:
|
||||
next_time = when
|
||||
|
||||
if not self._queue or self._queue[0][0] > self.time:
|
||||
agent_id = None
|
||||
break
|
||||
else:
|
||||
(when, agent_id) = self._queue[0]
|
||||
|
||||
if when and when < self.time:
|
||||
raise Exception("Invalid scheduling time")
|
||||
|
||||
self.next_time = next_time
|
||||
self.steps += 1
|
@ -7,8 +7,8 @@ from shutil import copyfile
|
||||
from contextlib import contextmanager
|
||||
|
||||
logger = logging.getLogger('soil')
|
||||
logging.basicConfig()
|
||||
logger.setLevel(logging.INFO)
|
||||
# logging.basicConfig()
|
||||
# logger.setLevel(logging.INFO)
|
||||
|
||||
|
||||
@contextmanager
|
||||
|
5
soil/visualization.py
Normal file
5
soil/visualization.py
Normal file
@ -0,0 +1,5 @@
|
||||
from mesa.visualization.UserParam import UserSettableParameter
|
||||
|
||||
class UserSettableParameter(UserSettableParameter):
|
||||
def __str__(self):
|
||||
return self.value
|
@ -1 +1,4 @@
|
||||
pytest
|
||||
pytest
|
||||
mesa>=0.8.9
|
||||
scipy>=1.3
|
||||
tornado
|
||||
|
@ -21,11 +21,13 @@ class Ping(agents.FSM):
|
||||
@agents.default_state
|
||||
@agents.state
|
||||
def even(self):
|
||||
self.debug(f'Even {self["count"]}')
|
||||
self['count'] += 1
|
||||
return self.odd
|
||||
|
||||
@agents.state
|
||||
def odd(self):
|
||||
self.debug(f'Odd {self["count"]}')
|
||||
self['count'] += 1
|
||||
return self.even
|
||||
|
||||
@ -82,8 +84,7 @@ class TestAnalysis(TestCase):
|
||||
|
||||
import numpy as np
|
||||
res_mean = analysis.get_value(df, 'count', aggfunc=np.mean)
|
||||
assert res_mean['count'].iloc[0] == 1
|
||||
|
||||
res_total = analysis.get_value(df)
|
||||
assert res_mean['count'].iloc[15] == (16+8)/2
|
||||
|
||||
res_total = analysis.get_majority(df)
|
||||
res_total['SEED'].iloc[0] == self.env['SEED']
|
||||
|
@ -1,203 +0,0 @@
|
||||
from unittest import TestCase
|
||||
|
||||
import os
|
||||
import shutil
|
||||
from glob import glob
|
||||
|
||||
from soil import history
|
||||
from soil import utils
|
||||
|
||||
|
||||
ROOT = os.path.abspath(os.path.dirname(__file__))
|
||||
DBROOT = os.path.join(ROOT, 'testdb')
|
||||
|
||||
|
||||
class TestHistory(TestCase):
|
||||
|
||||
def setUp(self):
|
||||
if not os.path.exists(DBROOT):
|
||||
os.makedirs(DBROOT)
|
||||
|
||||
def tearDown(self):
|
||||
if os.path.exists(DBROOT):
|
||||
shutil.rmtree(DBROOT)
|
||||
|
||||
def test_history(self):
|
||||
"""
|
||||
"""
|
||||
tuples = (
|
||||
('a_0', 0, 'id', 'h'),
|
||||
('a_0', 1, 'id', 'e'),
|
||||
('a_0', 2, 'id', 'l'),
|
||||
('a_0', 3, 'id', 'l'),
|
||||
('a_0', 4, 'id', 'o'),
|
||||
('a_1', 0, 'id', 'v'),
|
||||
('a_1', 1, 'id', 'a'),
|
||||
('a_1', 2, 'id', 'l'),
|
||||
('a_1', 3, 'id', 'u'),
|
||||
('a_1', 4, 'id', 'e'),
|
||||
('env', 1, 'prob', 1),
|
||||
('env', 3, 'prob', 2),
|
||||
('env', 5, 'prob', 3),
|
||||
('a_2', 7, 'finished', True),
|
||||
)
|
||||
h = history.History()
|
||||
h.save_tuples(tuples)
|
||||
# assert h['env', 0, 'prob'] == 0
|
||||
for i in range(1, 7):
|
||||
assert h['env', i, 'prob'] == ((i-1)//2)+1
|
||||
|
||||
|
||||
for i, k in zip(range(5), 'hello'):
|
||||
assert h['a_0', i, 'id'] == k
|
||||
for record, value in zip(h['a_0', None, 'id'], 'hello'):
|
||||
t_step, val = record
|
||||
assert val == value
|
||||
|
||||
for i, k in zip(range(5), 'value'):
|
||||
assert h['a_1', i, 'id'] == k
|
||||
for i in range(5, 8):
|
||||
assert h['a_1', i, 'id'] == 'e'
|
||||
for i in range(7):
|
||||
assert h['a_2', i, 'finished'] == False
|
||||
assert h['a_2', 7, 'finished']
|
||||
|
||||
def test_history_gen(self):
|
||||
"""
|
||||
"""
|
||||
tuples = (
|
||||
('a_1', 0, 'id', 'v'),
|
||||
('a_1', 1, 'id', 'a'),
|
||||
('a_1', 2, 'id', 'l'),
|
||||
('a_1', 3, 'id', 'u'),
|
||||
('a_1', 4, 'id', 'e'),
|
||||
('env', 1, 'prob', 1),
|
||||
('env', 2, 'prob', 2),
|
||||
('env', 3, 'prob', 3),
|
||||
('a_2', 7, 'finished', True),
|
||||
)
|
||||
h = history.History()
|
||||
h.save_tuples(tuples)
|
||||
for t_step, key, value in h['env', None, None]:
|
||||
assert t_step == value
|
||||
assert key == 'prob'
|
||||
|
||||
records = list(h[None, 7, None])
|
||||
assert len(records) == 3
|
||||
for i in records:
|
||||
agent_id, key, value = i
|
||||
if agent_id == 'a_1':
|
||||
assert key == 'id'
|
||||
assert value == 'e'
|
||||
elif agent_id == 'a_2':
|
||||
assert key == 'finished'
|
||||
assert value
|
||||
else:
|
||||
assert key == 'prob'
|
||||
assert value == 3
|
||||
|
||||
records = h['a_1', 7, None]
|
||||
assert records['id'] == 'e'
|
||||
|
||||
def test_history_file(self):
|
||||
"""
|
||||
History should be saved to a file
|
||||
"""
|
||||
tuples = (
|
||||
('a_1', 0, 'id', 'v'),
|
||||
('a_1', 1, 'id', 'a'),
|
||||
('a_1', 2, 'id', 'l'),
|
||||
('a_1', 3, 'id', 'u'),
|
||||
('a_1', 4, 'id', 'e'),
|
||||
('env', 1, 'prob', 1),
|
||||
('env', 2, 'prob', 2),
|
||||
('env', 3, 'prob', 3),
|
||||
('a_2', 7, 'finished', True),
|
||||
)
|
||||
db_path = os.path.join(DBROOT, 'test')
|
||||
h = history.History(db_path=db_path)
|
||||
h.save_tuples(tuples)
|
||||
h.flush_cache()
|
||||
assert os.path.exists(db_path)
|
||||
|
||||
# Recover the data
|
||||
recovered = history.History(db_path=db_path)
|
||||
assert recovered['a_1', 0, 'id'] == 'v'
|
||||
assert recovered['a_1', 4, 'id'] == 'e'
|
||||
|
||||
# Using backup=True should create a backup copy, and initialize an empty history
|
||||
newhistory = history.History(db_path=db_path, backup=True)
|
||||
backuppaths = glob(db_path + '.backup*.sqlite')
|
||||
assert len(backuppaths) == 1
|
||||
backuppath = backuppaths[0]
|
||||
assert newhistory.db_path == h.db_path
|
||||
assert os.path.exists(backuppath)
|
||||
assert len(newhistory[None, None, None]) == 0
|
||||
|
||||
def test_history_tuples(self):
|
||||
"""
|
||||
The data recovered should be equal to the one recorded.
|
||||
"""
|
||||
tuples = (
|
||||
('a_1', 0, 'id', 'v'),
|
||||
('a_1', 1, 'id', 'a'),
|
||||
('a_1', 2, 'id', 'l'),
|
||||
('a_1', 3, 'id', 'u'),
|
||||
('a_1', 4, 'id', 'e'),
|
||||
('env', 1, 'prob', 1),
|
||||
('env', 2, 'prob', 2),
|
||||
('env', 3, 'prob', 3),
|
||||
('a_2', 7, 'finished', True),
|
||||
)
|
||||
h = history.History()
|
||||
h.save_tuples(tuples)
|
||||
recovered = list(h.to_tuples())
|
||||
assert recovered
|
||||
for i in recovered:
|
||||
assert i in tuples
|
||||
|
||||
def test_stats(self):
|
||||
"""
|
||||
The data recovered should be equal to the one recorded.
|
||||
"""
|
||||
tuples = (
|
||||
('a_1', 0, 'id', 'v'),
|
||||
('a_1', 1, 'id', 'a'),
|
||||
('a_1', 2, 'id', 'l'),
|
||||
('a_1', 3, 'id', 'u'),
|
||||
('a_1', 4, 'id', 'e'),
|
||||
('env', 1, 'prob', 1),
|
||||
('env', 2, 'prob', 2),
|
||||
('env', 3, 'prob', 3),
|
||||
('a_2', 7, 'finished', True),
|
||||
)
|
||||
stat_tuples = [
|
||||
{'num_infected': 5, 'runtime': 0.2},
|
||||
{'num_infected': 5, 'runtime': 0.2},
|
||||
{'new': '40'},
|
||||
]
|
||||
h = history.History()
|
||||
h.save_tuples(tuples)
|
||||
for stat in stat_tuples:
|
||||
h.save_stats(stat)
|
||||
recovered = h.get_stats()
|
||||
assert recovered
|
||||
assert recovered[0]['num_infected'] == 5
|
||||
assert recovered[1]['runtime'] == 0.2
|
||||
assert recovered[2]['new'] == '40'
|
||||
|
||||
def test_unflatten(self):
|
||||
ex = {'count.neighbors.3': 4,
|
||||
'count.times.2': 4,
|
||||
'count.total.4': 4,
|
||||
'mean.neighbors': 3,
|
||||
'mean.times': 2,
|
||||
'mean.total': 4,
|
||||
't_step': 2,
|
||||
'trial_id': 'exporter_sim_trial_1605817956-4475424'}
|
||||
res = utils.unflatten_dict(ex)
|
||||
|
||||
assert 'count' in res
|
||||
assert 'mean' in res
|
||||
assert 't_step' in res
|
||||
assert 'trial_id' in res
|
@ -126,7 +126,7 @@ class TestMain(TestCase):
|
||||
env = s.run_simulation(dry_run=True)[0]
|
||||
for agent in env.network_agents:
|
||||
last = 0
|
||||
assert len(agent[None, None]) == 10
|
||||
assert len(agent[None, None]) == 11
|
||||
for step, total in sorted(agent['total', None]):
|
||||
assert total == last + 2
|
||||
last = total
|
||||
@ -198,11 +198,11 @@ class TestMain(TestCase):
|
||||
"""
|
||||
config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]
|
||||
s = simulation.from_config(config)
|
||||
for i in range(5):
|
||||
s.run_simulation(dry_run=True)
|
||||
nconfig = s.to_dict()
|
||||
del nconfig['topology']
|
||||
assert config == nconfig
|
||||
|
||||
s.run_simulation(dry_run=True)
|
||||
nconfig = s.to_dict()
|
||||
del nconfig['topology']
|
||||
assert config == nconfig
|
||||
|
||||
def test_row_conversion(self):
|
||||
env = Environment()
|
||||
@ -211,7 +211,7 @@ class TestMain(TestCase):
|
||||
res = list(env.history_to_tuples())
|
||||
assert len(res) == len(env.environment_params)
|
||||
|
||||
env._now = 1
|
||||
env.schedule.time = 1
|
||||
env['test'] = 'second_value'
|
||||
res = list(env.history_to_tuples())
|
||||
|
||||
@ -281,7 +281,7 @@ class TestMain(TestCase):
|
||||
'weight': 2
|
||||
},
|
||||
]
|
||||
converted = agents.deserialize_distribution(agent_distro)
|
||||
converted = agents.deserialize_definition(agent_distro)
|
||||
assert converted[0]['agent_type'] == agents.CounterModel
|
||||
assert converted[1]['agent_type'] == CustomAgent
|
||||
pickle.dumps(converted)
|
||||
@ -297,14 +297,14 @@ class TestMain(TestCase):
|
||||
'weight': 2
|
||||
},
|
||||
]
|
||||
converted = agents.serialize_distribution(agent_distro)
|
||||
converted = agents.serialize_definition(agent_distro)
|
||||
assert converted[0]['agent_type'] == 'CounterModel'
|
||||
assert converted[1]['agent_type'] == 'test_main.CustomAgent'
|
||||
pickle.dumps(converted)
|
||||
|
||||
def test_pickle_agent_environment(self):
|
||||
env = Environment(name='Test')
|
||||
a = agents.BaseAgent(environment=env, agent_id=25)
|
||||
a = agents.BaseAgent(model=env, unique_id=25)
|
||||
|
||||
a['key'] = 'test'
|
||||
|
||||
@ -345,7 +345,7 @@ class TestMain(TestCase):
|
||||
|
||||
def test_until(self):
|
||||
config = {
|
||||
'name': 'exporter_sim',
|
||||
'name': 'until_sim',
|
||||
'network_params': {},
|
||||
'agent_type': 'CounterModel',
|
||||
'max_time': 2,
|
||||
|
69
tests/test_mesa.py
Normal file
69
tests/test_mesa.py
Normal file
@ -0,0 +1,69 @@
|
||||
'''
|
||||
Mesa-SOIL integration tests
|
||||
|
||||
We have to test that:
|
||||
- Mesa agents can be used in SOIL
|
||||
- Simplified soil agents can be used in mesa simulations
|
||||
- Mesa and soil agents can interact in a simulation
|
||||
|
||||
- Mesa visualizations work with SOIL simulations
|
||||
|
||||
'''
|
||||
from mesa import Agent, Model
|
||||
from mesa.time import RandomActivation
|
||||
from mesa.space import MultiGrid
|
||||
|
||||
class MoneyAgent(Agent):
|
||||
""" An agent with fixed initial wealth."""
|
||||
def __init__(self, unique_id, model):
|
||||
super().__init__(unique_id, model)
|
||||
self.wealth = 1
|
||||
|
||||
def step(self):
|
||||
self.move()
|
||||
if self.wealth > 0:
|
||||
self.give_money()
|
||||
|
||||
def give_money(self):
|
||||
cellmates = self.model.grid.get_cell_list_contents([self.pos])
|
||||
if len(cellmates) > 1:
|
||||
other = self.random.choice(cellmates)
|
||||
other.wealth += 1
|
||||
self.wealth -= 1
|
||||
|
||||
def move(self):
|
||||
possible_steps = self.model.grid.get_neighborhood(
|
||||
self.pos,
|
||||
moore=True,
|
||||
include_center=False)
|
||||
new_position = self.random.choice(possible_steps)
|
||||
self.model.grid.move_agent(self, new_position)
|
||||
|
||||
|
||||
class MoneyModel(Model):
|
||||
"""A model with some number of agents."""
|
||||
def __init__(self, N, width, height):
|
||||
self.num_agents = N
|
||||
self.grid = MultiGrid(width, height, True)
|
||||
self.schedule = RandomActivation(self)
|
||||
|
||||
# Create agents
|
||||
for i in range(self.num_agents):
|
||||
a = MoneyAgent(i, self)
|
||||
self.schedule.add(a)
|
||||
|
||||
# Add the agent to a random grid cell
|
||||
x = self.random.randrange(self.grid.width)
|
||||
y = self.random.randrange(self.grid.height)
|
||||
self.grid.place_agent(a, (x, y))
|
||||
|
||||
def step(self):
|
||||
'''Advance the model by one step.'''
|
||||
self.schedule.step()
|
||||
|
||||
|
||||
# model = MoneyModel(10)
|
||||
# for i in range(10):
|
||||
# model.step()
|
||||
|
||||
# agent_wealth = [a.wealth for a in model.schedule.agents]
|
Loading…
Reference in New Issue
Block a user