mirror of
https://github.com/gsi-upm/soil
synced 2025-10-30 23:28:17 +00:00
390 lines
11 KiB
Python
390 lines
11 KiB
Python
# networkStatus = {} # Dict that will contain the status of every agent in the network
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# sentimentCorrelationNodeArray = []
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# for x in range(0, settings.network_params["number_of_nodes"]):
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# sentimentCorrelationNodeArray.append({'id': x})
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# Initialize agent states. Let's assume everyone is normal.
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import nxsim
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import logging
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from collections import OrderedDict
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from copy import deepcopy
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from functools import partial
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import json
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from functools import wraps
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from .. import utils, history
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agent_types = {}
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class MetaAgent(type):
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def __init__(cls, name, bases, nmspc):
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super(MetaAgent, cls).__init__(name, bases, nmspc)
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agent_types[name] = cls
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class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
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"""
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A special simpy BaseAgent that keeps track of its state history.
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"""
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defaults = {}
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def __init__(self, environment=None, agent_id=None, state=None,
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name='network_process', interval=None, **state_params):
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# Check for REQUIRED arguments
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assert environment is not None, TypeError('__init__ missing 1 required keyword argument: \'environment\'. '
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'Cannot be NoneType.')
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# Initialize agent parameters
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self.id = agent_id
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self.name = name
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self.state_params = state_params
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# Global parameters
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self.global_topology = environment.G
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self.environment_params = environment.environment_params
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# Register agent to environment
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self.env = environment
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self._neighbors = None
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self.alive = True
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real_state = deepcopy(self.defaults)
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real_state.update(state or {})
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self.state = real_state
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self.interval = interval
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if not hasattr(self, 'level'):
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self.level = logging.DEBUG
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self.logger = logging.getLogger('{}-Agent-{}'.format(self.env.name,
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self.id))
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self.logger.setLevel(self.level)
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# initialize every time an instance of the agent is created
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self.action = self.env.process(self.run())
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@property
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def state(self):
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'''
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Return the agent itself, which behaves as a dictionary.
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Changes made to `agent.state` will be reflected in the history.
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This method shouldn't be used, but is kept here for backwards compatibility.
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'''
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return self
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@state.setter
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def state(self, value):
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self._state = {}
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for k, v in value.items():
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self[k] = v
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def __getitem__(self, key):
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if isinstance(key, tuple):
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key, t_step = key
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k = history.Key(key=key, t_step=t_step, agent_id=self.id)
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return self.env[k]
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return self._state.get(key, None)
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def __delitem__(self, key):
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self._state[key] = None
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def __contains__(self, key):
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return key in self._state
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def __setitem__(self, key, value):
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self._state[key] = value
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k = history.Key(t_step=self.now,
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agent_id=self.id,
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key=key)
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self.env[k] = value
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def items(self):
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return self._state.items()
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def get(self, key, default=None):
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return self[key] if key in self else default
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@property
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def now(self):
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try:
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return self.env.now
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except AttributeError:
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# No environment
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return None
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def run(self):
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if self.interval is not None:
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interval = self.interval
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elif 'interval' in self:
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interval = self['interval']
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else:
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interval = self.env.interval
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while self.alive:
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res = self.step()
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yield res or self.env.timeout(interval)
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def die(self, remove=False):
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self.alive = False
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if remove:
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super().die()
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def step(self):
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pass
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def to_json(self):
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return json.dumps(self.state)
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def count_agents(self, state_id=None, limit_neighbors=False):
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if limit_neighbors:
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agents = self.global_topology.neighbors(self.id)
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else:
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agents = self.global_topology.nodes()
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count = 0
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for agent in agents:
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if state_id and state_id != self.global_topology.node[agent]['agent']['id']:
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continue
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count += 1
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return count
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def count_neighboring_agents(self, state_id=None):
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return len(super().get_agents(state_id, limit_neighbors=True))
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def get_agents(self, state_id=None, limit_neighbors=False, iterator=False, **kwargs):
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if limit_neighbors:
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agents = super().get_agents(state_id, limit_neighbors)
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else:
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agents = filter(lambda x: state_id is None or x.state.get('id', None) == state_id,
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self.env.agents)
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def matches_all(agent):
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state = agent.state
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for k, v in kwargs.items():
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if state.get(k, None) != v:
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return False
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return True
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f = filter(matches_all, agents)
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if iterator:
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return f
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return list(f)
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def log(self, message, *args, level=logging.INFO, **kwargs):
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message = message + " ".join(str(i) for i in args)
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message = "\t@{:>5}:\t{}".format(self.now, message)
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for k, v in kwargs:
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message += " {k}={v} ".format(k, v)
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extra = {}
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extra['now'] = self.now
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extra['id'] = self.id
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return self.logger.log(level, message, extra=extra)
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def debug(self, *args, **kwargs):
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return self.log(*args, level=logging.DEBUG, **kwargs)
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def info(self, *args, **kwargs):
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return self.log(*args, level=logging.INFO, **kwargs)
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def state(func):
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'''
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A state function should return either a state id, or a tuple (state_id, when)
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The default value for state_id is the current state id.
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The default value for when is the interval defined in the nevironment.
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'''
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@wraps(func)
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def func_wrapper(self):
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next_state = func(self)
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when = None
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if next_state is None:
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return when
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try:
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next_state, when = next_state
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except (ValueError, TypeError):
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pass
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if next_state:
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self.set_state(next_state)
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return when
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func_wrapper.id = func.__name__
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func_wrapper.is_default = False
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return func_wrapper
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def default_state(func):
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func.is_default = True
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return func
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class MetaFSM(MetaAgent):
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def __init__(cls, name, bases, nmspc):
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super(MetaFSM, cls).__init__(name, bases, nmspc)
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states = {}
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# Re-use states from inherited classes
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default_state = None
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for i in bases:
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if isinstance(i, MetaFSM):
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for state_id, state in i.states.items():
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if state.is_default:
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default_state = state
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states[state_id] = state
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# Add new states
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for name, func in nmspc.items():
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if hasattr(func, 'id'):
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if func.is_default:
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default_state = func
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states[func.id] = func
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cls.default_state = default_state
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cls.states = states
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class FSM(BaseAgent, metaclass=MetaFSM):
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def __init__(self, *args, **kwargs):
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super(FSM, self).__init__(*args, **kwargs)
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if 'id' not in self.state:
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if not self.default_state:
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raise ValueError('No default state specified for {}'.format(self.id))
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self['id'] = self.default_state.id
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def step(self):
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if 'id' in self.state:
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next_state = self['id']
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elif self.default_state:
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next_state = self.default_state.id
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else:
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raise Exception('{} has no valid state id or default state'.format(self))
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if next_state not in self.states:
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raise Exception('{} is not a valid id for {}'.format(next_state, self))
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self.states[next_state](self)
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def set_state(self, state):
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if hasattr(state, 'id'):
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state = state.id
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if state not in self.states:
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raise ValueError('{} is not a valid state'.format(state))
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self['id'] = state
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return state
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def prob(prob=1):
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'''
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A true/False uniform distribution with a given probability.
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To be used like this:
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.. code-block:: python
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if prob(0.3):
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do_something()
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'''
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r = random.random()
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return r < prob
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def calculate_distribution(network_agents=None,
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agent_type=None):
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'''
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Calculate the threshold values (thresholds for a uniform distribution)
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of an agent distribution given the weights of each agent type.
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The input has this form: ::
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[
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{'agent_type': 'agent_type_1',
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'weight': 0.2,
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'state': {
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'id': 0
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}
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},
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{'agent_type': 'agent_type_2',
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'weight': 0.8,
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'state': {
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'id': 1
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}
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}
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]
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In this example, 20% of the nodes will be marked as type
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'agent_type_1'.
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'''
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if network_agents:
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network_agents = deepcopy(network_agents)
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elif agent_type:
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network_agents = [{'agent_type': agent_type}]
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else:
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return []
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# Calculate the thresholds
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total = sum(x.get('weight', 1) for x in network_agents)
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acc = 0
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for v in network_agents:
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upper = acc + (v.get('weight', 1)/total)
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v['threshold'] = [acc, upper]
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acc = upper
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return network_agents
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def _serialize_distribution(network_agents):
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d = _convert_agent_types(network_agents,
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to_string=True)
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'''
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When serializing an agent distribution, remove the thresholds, in order
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to avoid cluttering the YAML definition file.
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'''
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for v in d:
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if 'threshold' in v:
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del v['threshold']
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return d
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def _validate_states(states, topology):
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'''Validate states to avoid ignoring states during initialization'''
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states = states or []
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if isinstance(states, dict):
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for x in states:
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assert x in topology.node
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else:
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assert len(states) <= len(topology)
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return states
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def _convert_agent_types(ind, to_string=False):
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'''Convenience method to allow specifying agents by class or class name.'''
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d = deepcopy(ind)
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for v in d:
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agent_type = v['agent_type']
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if to_string and not isinstance(agent_type, str):
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v['agent_type'] = str(agent_type.__name__)
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elif not to_string and isinstance(agent_type, str):
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v['agent_type'] = agent_types[agent_type]
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return d
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def _agent_from_distribution(distribution, value=-1):
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"""Used in the initialization of agents given an agent distribution."""
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if value < 0:
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value = random.random()
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for d in distribution:
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threshold = d['threshold']
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if value >= threshold[0] and value < threshold[1]:
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state = {}
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if 'state' in d:
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state = deepcopy(d['state'])
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return d['agent_type'], state
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raise Exception('Distribution for value {} not found in: {}'.format(value, distribution))
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from .BassModel import *
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from .BigMarketModel import *
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from .IndependentCascadeModel import *
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from .ModelM2 import *
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from .SentimentCorrelationModel import *
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from .SISaModel import *
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from .CounterModel import *
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from .DrawingAgent import *
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