mirror of https://github.com/gsi-upm/soil
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
881 lines
27 KiB
Python
881 lines
27 KiB
Python
from __future__ import annotations
|
|
|
|
import logging
|
|
from collections import OrderedDict, defaultdict
|
|
from collections.abc import MutableMapping, Mapping, Set
|
|
from abc import ABCMeta
|
|
from copy import deepcopy, copy
|
|
from functools import partial, wraps
|
|
from itertools import islice, chain
|
|
import inspect
|
|
import types
|
|
import textwrap
|
|
import networkx as nx
|
|
|
|
from typing import Any
|
|
|
|
from mesa import Agent as MesaAgent
|
|
from typing import Dict, List
|
|
|
|
from .. import serialization, utils, time, config
|
|
|
|
|
|
|
|
def as_node(agent):
|
|
if isinstance(agent, BaseAgent):
|
|
return agent.id
|
|
return agent
|
|
|
|
IGNORED_FIELDS = ('model', 'logger')
|
|
|
|
|
|
class DeadAgent(Exception):
|
|
pass
|
|
|
|
|
|
class MetaAgent(ABCMeta):
|
|
def __new__(mcls, name, bases, namespace):
|
|
defaults = {}
|
|
|
|
# Re-use defaults from inherited classes
|
|
for i in bases:
|
|
if isinstance(i, MetaAgent):
|
|
defaults.update(i._defaults)
|
|
|
|
new_nmspc = {
|
|
'_defaults': defaults,
|
|
}
|
|
|
|
for attr, func in namespace.items():
|
|
if isinstance(func, types.FunctionType) or isinstance(func, property) or isinstance(func, classmethod) or attr[0] == '_':
|
|
new_nmspc[attr] = func
|
|
elif attr == 'defaults':
|
|
defaults.update(func)
|
|
else:
|
|
defaults[attr] = copy(func)
|
|
|
|
return super().__new__(mcls=mcls, name=name, bases=bases, namespace=new_nmspc)
|
|
|
|
|
|
class BaseAgent(MesaAgent, MutableMapping, metaclass=MetaAgent):
|
|
"""
|
|
A special type of Mesa Agent that:
|
|
|
|
* Can be used as a dictionary to access its state.
|
|
* Has logging built-in
|
|
* Can be given default arguments through a defaults class attribute,
|
|
which will be used on construction to initialize each agent's state
|
|
|
|
Any attribute that is not preceded by an underscore (`_`) will also be added to its state.
|
|
"""
|
|
|
|
def __init__(self,
|
|
unique_id,
|
|
model,
|
|
name=None,
|
|
interval=None,
|
|
**kwargs):
|
|
# Check for REQUIRED arguments
|
|
# Initialize agent parameters
|
|
if isinstance(unique_id, MesaAgent):
|
|
raise Exception()
|
|
assert isinstance(unique_id, int)
|
|
super().__init__(unique_id=unique_id, model=model)
|
|
|
|
self.name = str(name) if name else'{}[{}]'.format(type(self).__name__, self.unique_id)
|
|
|
|
|
|
self.alive = True
|
|
|
|
self.interval = interval or self.get('interval', 1)
|
|
logger = utils.logger.getChild(getattr(self.model, 'id', self.model)).getChild(self.name)
|
|
self.logger = logging.LoggerAdapter(logger, {'agent_name': self.name})
|
|
|
|
if hasattr(self, 'level'):
|
|
self.logger.setLevel(self.level)
|
|
|
|
for (k, v) in self._defaults.items():
|
|
if not hasattr(self, k) or getattr(self, k) is None:
|
|
setattr(self, k, deepcopy(v))
|
|
|
|
for (k, v) in kwargs.items():
|
|
|
|
setattr(self, k, v)
|
|
|
|
def __hash__(self):
|
|
return hash(self.unique_id)
|
|
|
|
def prob(self, probability):
|
|
return prob(probability, self.model.random)
|
|
|
|
# TODO: refactor to clean up mesa compatibility
|
|
@property
|
|
def id(self):
|
|
return self.unique_id
|
|
|
|
@classmethod
|
|
def from_dict(cls, model, attrs, warn_extra=True):
|
|
ignored = {}
|
|
args = {}
|
|
for k, v in attrs.items():
|
|
if k in inspect.signature(cls).parameters:
|
|
args[k] = v
|
|
else:
|
|
ignored[k] = v
|
|
if ignored and warn_extra:
|
|
utils.logger.info(f'Ignoring the following arguments for agent class { agent_class.__name__ }: { ignored }')
|
|
return cls(model=model, **args)
|
|
|
|
def __getitem__(self, key):
|
|
try:
|
|
return getattr(self, key)
|
|
except AttributeError:
|
|
raise KeyError(f'key {key} not found in agent')
|
|
|
|
def __delitem__(self, key):
|
|
return delattr(self, key)
|
|
|
|
def __contains__(self, key):
|
|
return hasattr(self, key)
|
|
|
|
def __setitem__(self, key, value):
|
|
setattr(self, key, value)
|
|
|
|
def __len__(self):
|
|
return sum(1 for n in self.keys())
|
|
|
|
def __iter__(self):
|
|
return self.items()
|
|
|
|
def keys(self):
|
|
return (k for k in self.__dict__ if k[0] != '_' and k not in IGNORED_FIELDS)
|
|
|
|
def items(self, keys=None, skip=None):
|
|
keys = keys if keys is not None else self.keys()
|
|
it = ((k, self.get(k, None)) for k in keys)
|
|
if skip:
|
|
return filter(lambda x: x[0] not in skip, it)
|
|
return it
|
|
|
|
def get(self, key, default=None):
|
|
return self[key] if key in self else default
|
|
|
|
@property
|
|
def now(self):
|
|
try:
|
|
return self.model.now
|
|
except AttributeError:
|
|
# No environment
|
|
return None
|
|
|
|
def die(self):
|
|
self.info(f'agent dying')
|
|
self.alive = False
|
|
return time.NEVER
|
|
|
|
def step(self):
|
|
if not self.alive:
|
|
raise DeadAgent(self.unique_id)
|
|
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 = "[@{:>4}]\t{:>10}: {}".format(self.now, repr(self), 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)
|
|
|
|
def info(self, *args, **kwargs):
|
|
return self.log(*args, level=logging.INFO, **kwargs)
|
|
|
|
def count_agents(self, **kwargs):
|
|
return len(list(self.get_agents(**kwargs)))
|
|
|
|
def get_agents(self, *args, **kwargs):
|
|
it = self.iter_agents(*args, **kwargs)
|
|
return list(it)
|
|
|
|
def iter_agents(self, *args, **kwargs):
|
|
yield from filter_agents(self.model.schedule._agents, *args, **kwargs)
|
|
|
|
def __str__(self):
|
|
return self.to_str()
|
|
|
|
def to_str(self, keys=None, skip=None, pretty=False):
|
|
content = dict(self.items(keys=keys))
|
|
if pretty and content:
|
|
d = content
|
|
content = '\n'
|
|
for k, v in d.items():
|
|
content += f'- {k}: {v}\n'
|
|
content = textwrap.indent(content, ' ')
|
|
return f"{repr(self)}{content}"
|
|
|
|
def __repr__(self):
|
|
return f"{self.__class__.__name__}({self.unique_id})"
|
|
|
|
|
|
class NetworkAgent(BaseAgent):
|
|
|
|
def __init__(self, *args, topology, node_id, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
assert topology is not None
|
|
assert node_id is not None
|
|
self.G = topology
|
|
assert self.G
|
|
self.node_id = node_id
|
|
|
|
def count_neighboring_agents(self, state_id=None, **kwargs):
|
|
return len(self.get_neighboring_agents(state_id=state_id, **kwargs))
|
|
|
|
def get_neighboring_agents(self, **kwargs):
|
|
return list(self.iter_agents(limit_neighbors=True, **kwargs))
|
|
|
|
def add_edge(self, other):
|
|
self.topology.add_edge(self.node_id, other.node_id)
|
|
|
|
@property
|
|
def node(self):
|
|
return self.topology.nodes[self.node_id]
|
|
|
|
|
|
def iter_agents(self, unique_id=None, *, limit_neighbors=False, **kwargs):
|
|
unique_ids = None
|
|
if isinstance(unique_id, list):
|
|
unique_ids = set(unique_id)
|
|
elif unique_id is not None:
|
|
unique_ids = set([unique_id,])
|
|
|
|
if limit_neighbors:
|
|
neighbor_ids = set()
|
|
for node_id in self.G.neighbors(self.node_id):
|
|
if self.G.nodes[node_id].get('agent') is not None:
|
|
neighbor_ids.add(node_id)
|
|
if unique_ids:
|
|
unique_ids = unique_ids & neighbor_ids
|
|
else:
|
|
unique_ids = neighbor_ids
|
|
if not unique_ids:
|
|
return
|
|
unique_ids = list(unique_ids)
|
|
yield from super().iter_agents(unique_id=unique_ids, **kwargs)
|
|
|
|
def subgraph(self, center=True, **kwargs):
|
|
include = [self] if center else []
|
|
G = self.G.subgraph(n.node_id for n in list(self.get_agents(**kwargs)+include))
|
|
return G
|
|
|
|
def remove_node(self):
|
|
self.G.remove_node(self.node_id)
|
|
|
|
def add_edge(self, other, edge_attr_dict=None, *edge_attrs):
|
|
if self.node_id not in self.G.nodes(data=False):
|
|
raise ValueError('{} not in list of existing agents in the network'.format(self.unique_id))
|
|
if other.node_id not in self.G.nodes(data=False):
|
|
raise ValueError('{} not in list of existing agents in the network'.format(other))
|
|
|
|
self.G.add_edge(self.node_id, other.node_id, edge_attr_dict=edge_attr_dict, *edge_attrs)
|
|
|
|
def die(self, remove=True):
|
|
if remove:
|
|
self.remove_node()
|
|
return super().die()
|
|
|
|
|
|
def state(name=None):
|
|
def decorator(func, name=None):
|
|
'''
|
|
A state function should return either a state id, or a tuple (state_id, when)
|
|
The default value for state_id is the current state id.
|
|
The default value for when is the interval defined in the environment.
|
|
'''
|
|
if inspect.isgeneratorfunction(func):
|
|
orig_func = func
|
|
|
|
@wraps(func)
|
|
def func(self):
|
|
while True:
|
|
if not self._coroutine:
|
|
self._coroutine = orig_func(self)
|
|
try:
|
|
n = next(self._coroutine)
|
|
if n:
|
|
return None, n
|
|
return
|
|
except StopIteration as ex:
|
|
self._coroutine = None
|
|
next_state = ex.value
|
|
if next_state is not None:
|
|
self.set_state(next_state)
|
|
return next_state
|
|
|
|
func.id = name or func.__name__
|
|
func.is_default = False
|
|
return func
|
|
|
|
if callable(name):
|
|
return decorator(name)
|
|
else:
|
|
return partial(decorator, name=name)
|
|
|
|
|
|
def default_state(func):
|
|
func.is_default = True
|
|
return func
|
|
|
|
|
|
class MetaFSM(MetaAgent):
|
|
def __new__(mcls, name, bases, namespace):
|
|
states = {}
|
|
# Re-use states from inherited classes
|
|
default_state = None
|
|
for i in bases:
|
|
if isinstance(i, MetaFSM):
|
|
for state_id, state in i._states.items():
|
|
if state.is_default:
|
|
default_state = state
|
|
states[state_id] = state
|
|
|
|
# Add new states
|
|
for attr, func in namespace.items():
|
|
if hasattr(func, 'id'):
|
|
if func.is_default:
|
|
default_state = func
|
|
states[func.id] = func
|
|
|
|
namespace.update({
|
|
'_default_state': default_state,
|
|
'_states': states,
|
|
})
|
|
|
|
return super(MetaFSM, mcls).__new__(mcls=mcls, name=name, bases=bases, namespace=namespace)
|
|
|
|
|
|
class FSM(BaseAgent, metaclass=MetaFSM):
|
|
def __init__(self, *args, **kwargs):
|
|
super(FSM, self).__init__(*args, **kwargs)
|
|
if not hasattr(self, 'state_id'):
|
|
if not self._default_state:
|
|
raise ValueError('No default state specified for {}'.format(self.unique_id))
|
|
self.state_id = self._default_state.id
|
|
|
|
self._coroutine = None
|
|
self.set_state(self.state_id)
|
|
|
|
def step(self):
|
|
self.debug(f'Agent {self.unique_id} @ state {self.state_id}')
|
|
default_interval = super().step()
|
|
|
|
next_state = self._states[self.state_id](self)
|
|
|
|
when = None
|
|
try:
|
|
next_state, *when = next_state
|
|
if not when:
|
|
when = None
|
|
elif len(when) == 1:
|
|
when = when[0]
|
|
else:
|
|
raise ValueError('Too many values returned. Only state (and time) allowed')
|
|
except TypeError:
|
|
pass
|
|
|
|
if next_state is not None:
|
|
self.set_state(next_state)
|
|
|
|
return when or default_interval
|
|
|
|
def set_state(self, state, when=None):
|
|
if hasattr(state, 'id'):
|
|
state = state.id
|
|
if state not in self._states:
|
|
raise ValueError('{} is not a valid state'.format(state))
|
|
self.state_id = state
|
|
if when is not None:
|
|
self.model.schedule.add(self, when=when)
|
|
return state
|
|
|
|
def die(self):
|
|
return self.dead, super().die()
|
|
|
|
@state
|
|
def dead(self):
|
|
return self.die()
|
|
|
|
|
|
def prob(prob, random):
|
|
'''
|
|
A true/False uniform distribution with a given probability.
|
|
To be used like this:
|
|
|
|
.. code-block:: python
|
|
|
|
if prob(0.3):
|
|
do_something()
|
|
|
|
'''
|
|
r = random.random()
|
|
return r < prob
|
|
|
|
|
|
def calculate_distribution(network_agents=None,
|
|
agent_class=None):
|
|
'''
|
|
Calculate the threshold values (thresholds for a uniform distribution)
|
|
of an agent distribution given the weights of each agent type.
|
|
|
|
The input has this form: ::
|
|
|
|
[
|
|
{'agent_class': 'agent_class_1',
|
|
'weight': 0.2,
|
|
'state': {
|
|
'id': 0
|
|
}
|
|
},
|
|
{'agent_class': 'agent_class_2',
|
|
'weight': 0.8,
|
|
'state': {
|
|
'id': 1
|
|
}
|
|
}
|
|
]
|
|
|
|
In this example, 20% of the nodes will be marked as type
|
|
'agent_class_1'.
|
|
'''
|
|
if network_agents:
|
|
network_agents = [deepcopy(agent) for agent in network_agents if not hasattr(agent, 'id')]
|
|
elif agent_class:
|
|
network_agents = [{'agent_class': agent_class}]
|
|
else:
|
|
raise ValueError('Specify a distribution or a default agent type')
|
|
|
|
# Fix missing weights and incompatible types
|
|
for x in network_agents:
|
|
x['weight'] = float(x.get('weight', 1))
|
|
|
|
# Calculate the thresholds
|
|
total = sum(x['weight'] for x in network_agents)
|
|
acc = 0
|
|
for v in network_agents:
|
|
if 'ids' in v:
|
|
continue
|
|
upper = acc + (v['weight']/total)
|
|
v['threshold'] = [acc, upper]
|
|
acc = upper
|
|
return network_agents
|
|
|
|
|
|
def serialize_type(agent_class, known_modules=[], **kwargs):
|
|
if isinstance(agent_class, str):
|
|
return agent_class
|
|
known_modules += ['soil.agents']
|
|
return serialization.serialize(agent_class, known_modules=known_modules, **kwargs)[1] # Get the name of the class
|
|
|
|
|
|
def serialize_definition(network_agents, known_modules=[]):
|
|
'''
|
|
When serializing an agent distribution, remove the thresholds, in order
|
|
to avoid cluttering the YAML definition file.
|
|
'''
|
|
d = deepcopy(list(network_agents))
|
|
for v in d:
|
|
if 'threshold' in v:
|
|
del v['threshold']
|
|
v['agent_class'] = serialize_type(v['agent_class'],
|
|
known_modules=known_modules)
|
|
return d
|
|
|
|
|
|
def deserialize_type(agent_class, known_modules=[]):
|
|
if not isinstance(agent_class, str):
|
|
return agent_class
|
|
known = known_modules + ['soil.agents', 'soil.agents.custom' ]
|
|
agent_class = serialization.deserializer(agent_class, known_modules=known)
|
|
return agent_class
|
|
|
|
|
|
def deserialize_definition(ind, **kwargs):
|
|
d = deepcopy(ind)
|
|
for v in d:
|
|
v['agent_class'] = deserialize_type(v['agent_class'], **kwargs)
|
|
return d
|
|
|
|
|
|
def _validate_states(states, topology):
|
|
'''Validate states to avoid ignoring states during initialization'''
|
|
states = states or []
|
|
if isinstance(states, dict):
|
|
for x in states:
|
|
assert x in topology.nodes
|
|
else:
|
|
assert len(states) <= len(topology)
|
|
return states
|
|
|
|
|
|
def _convert_agent_classs(ind, to_string=False, **kwargs):
|
|
'''Convenience method to allow specifying agents by class or class name.'''
|
|
if to_string:
|
|
return serialize_definition(ind, **kwargs)
|
|
return deserialize_definition(ind, **kwargs)
|
|
|
|
|
|
# def _agent_from_definition(definition, random, 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(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 (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_class'], state
|
|
|
|
# raise Exception('Definition for value {} not found in: {}'.format(value, definition))
|
|
|
|
|
|
# def _definition_to_dict(definition, random, 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))
|
|
|
|
# distro = sorted([item for item in definition if 'weight' in item])
|
|
|
|
# id = 0
|
|
|
|
# def init_agent(item, id=ix):
|
|
# while id in agents:
|
|
# id += 1
|
|
|
|
# agent = remaining[id]
|
|
# agent['state'].update(copy(item.get('state', {})))
|
|
# agents[agent.unique_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
|
|
|
|
|
|
class AgentView(Mapping, Set):
|
|
"""A lazy-loaded list of agents.
|
|
"""
|
|
|
|
__slots__ = ("_agents",)
|
|
|
|
|
|
def __init__(self, agents):
|
|
self._agents = agents
|
|
|
|
def __getstate__(self):
|
|
return {"_agents": self._agents}
|
|
|
|
def __setstate__(self, state):
|
|
self._agents = state["_agents"]
|
|
|
|
# Mapping methods
|
|
def __len__(self):
|
|
return len(self._agents)
|
|
|
|
def __iter__(self):
|
|
yield from self._agents.values()
|
|
|
|
def __getitem__(self, agent_id):
|
|
if isinstance(agent_id, slice):
|
|
raise ValueError(f"Slicing is not supported")
|
|
if agent_id in self._agents:
|
|
return self._agents[agent_id]
|
|
raise ValueError(f"Agent {agent_id} not found")
|
|
|
|
def filter(self, *args, **kwargs):
|
|
yield from filter_agents(self._agents, *args, **kwargs)
|
|
|
|
def one(self, *args, **kwargs):
|
|
return next(filter_agents(self._agents, *args, **kwargs))
|
|
|
|
def __call__(self, *args, **kwargs):
|
|
return list(self.filter(*args, **kwargs))
|
|
|
|
def __contains__(self, agent_id):
|
|
return agent_id in self._agents
|
|
|
|
def __str__(self):
|
|
return str(list(unique_id for unique_id in self.keys()))
|
|
|
|
def __repr__(self):
|
|
return f"{self.__class__.__name__}({self})"
|
|
|
|
|
|
def filter_agents(agents, *id_args, unique_id=None, state_id=None, agent_class=None, ignore=None, state=None,
|
|
limit=None, **kwargs):
|
|
'''
|
|
Filter agents given as a dict, by the criteria given as arguments (e.g., certain type or state id).
|
|
'''
|
|
assert isinstance(agents, dict)
|
|
|
|
ids = []
|
|
|
|
if unique_id is not None:
|
|
if isinstance(unique_id, list):
|
|
ids += unique_id
|
|
else:
|
|
ids.append(unique_id)
|
|
|
|
if id_args:
|
|
ids += id_args
|
|
|
|
if ids:
|
|
f = (agents[aid] for aid in ids if aid in agents)
|
|
else:
|
|
f = (a for a in agents.values())
|
|
|
|
if state_id is not None and not isinstance(state_id, (tuple, list)):
|
|
state_id = tuple([state_id])
|
|
|
|
if agent_class is not None:
|
|
agent_class = deserialize_type(agent_class)
|
|
try:
|
|
agent_class = tuple(agent_class)
|
|
except TypeError:
|
|
agent_class = tuple([agent_class])
|
|
|
|
if ignore:
|
|
f = filter(lambda x: x not in ignore, f)
|
|
|
|
if state_id is not None:
|
|
f = filter(lambda agent: agent.get('state_id', None) in state_id, f)
|
|
|
|
if agent_class is not None:
|
|
f = filter(lambda agent: isinstance(agent, agent_class), f)
|
|
|
|
state = state or dict()
|
|
state.update(kwargs)
|
|
|
|
for k, v in state.items():
|
|
f = filter(lambda agent: getattr(agent, k, None) == v, f)
|
|
|
|
if limit is not None:
|
|
f = islice(f, limit)
|
|
|
|
yield from f
|
|
|
|
|
|
def from_config(cfg: config.AgentConfig, random, topology: nx.Graph = None) -> List[Dict[str, Any]]:
|
|
'''
|
|
This function turns an agentconfig into a list of individual "agent specifications", which are just a dictionary
|
|
with the parameters that the environment will use to construct each agent.
|
|
|
|
This function does NOT return a list of agents, mostly because some attributes to the agent are not known at the
|
|
time of calling this function, such as `unique_id`.
|
|
'''
|
|
default = cfg or config.AgentConfig()
|
|
if not isinstance(cfg, config.AgentConfig):
|
|
cfg = config.AgentConfig(**cfg)
|
|
return _agents_from_config(cfg, topology=topology, random=random)
|
|
|
|
|
|
def _agents_from_config(cfg: config.AgentConfig,
|
|
topology: nx.Graph,
|
|
random) -> List[Dict[str, Any]]:
|
|
if cfg and not isinstance(cfg, config.AgentConfig):
|
|
cfg = config.AgentConfig(**cfg)
|
|
|
|
agents = []
|
|
|
|
assigned_total = 0
|
|
assigned_network = 0
|
|
|
|
if cfg.fixed is not None:
|
|
agents, assigned_total, assigned_network = _from_fixed(cfg.fixed, topology=cfg.topology, default=cfg)
|
|
|
|
n = cfg.n
|
|
|
|
if cfg.distribution:
|
|
topo_size = len(topology) if topology else 0
|
|
|
|
networked = []
|
|
total = []
|
|
|
|
for d in cfg.distribution:
|
|
if d.strategy == config.Strategy.topology:
|
|
topo = d.topology if ('topology' in d.__fields_set__) else cfg.topology
|
|
if not topo:
|
|
raise ValueError('The "topology" strategy only works if the topology parameter is set to True')
|
|
if not topo_size:
|
|
raise ValueError(f'Topology does not have enough free nodes to assign one to the agent')
|
|
|
|
networked.append(d)
|
|
|
|
if d.strategy == config.Strategy.total:
|
|
if not cfg.n:
|
|
raise ValueError('Cannot use the "total" strategy without providing the total number of agents')
|
|
total.append(d)
|
|
|
|
|
|
if networked:
|
|
new_agents = _from_distro(networked,
|
|
n= topo_size - assigned_network,
|
|
topology=topo,
|
|
default=cfg,
|
|
random=random)
|
|
assigned_total += len(new_agents)
|
|
assigned_network += len(new_agents)
|
|
agents += new_agents
|
|
|
|
if total:
|
|
remaining = n - assigned_total
|
|
agents += _from_distro(total, n=remaining,
|
|
default=cfg,
|
|
random=random)
|
|
|
|
|
|
if assigned_network < topo_size:
|
|
utils.logger.warn(f'The total number of agents does not match the total number of nodes in '
|
|
'every topology. This may be due to a definition error: assigned: '
|
|
f'{ assigned } total size: { topo_size }')
|
|
|
|
return agents
|
|
|
|
|
|
def _from_fixed(lst: List[config.FixedAgentConfig], topology: bool, default: config.SingleAgentConfig) -> List[Dict[str, Any]]:
|
|
agents = []
|
|
|
|
counts_total = 0
|
|
counts_network = 0
|
|
|
|
for fixed in lst:
|
|
agent = {}
|
|
if default:
|
|
agent = default.state.copy()
|
|
agent.update(fixed.state)
|
|
cls = serialization.deserialize(fixed.agent_class or (default and default.agent_class))
|
|
agent['agent_class'] = cls
|
|
topo = fixed.topology if ('topology' in fixed.__fields_set__) else topology or default.topology
|
|
|
|
if topo:
|
|
agent['topology'] = True
|
|
counts_network += 1
|
|
if not fixed.hidden:
|
|
counts_total += 1
|
|
agents.append(agent)
|
|
|
|
return agents, counts_total, counts_network
|
|
|
|
|
|
def _from_distro(distro: List[config.AgentDistro],
|
|
n: int,
|
|
topology: str,
|
|
default: config.SingleAgentConfig,
|
|
random) -> List[Dict[str, Any]]:
|
|
|
|
agents = []
|
|
|
|
if n is None:
|
|
if any(lambda dist: dist.n is None, distro):
|
|
raise ValueError('You must provide a total number of agents, or the number of each type')
|
|
n = sum(dist.n for dist in distro)
|
|
|
|
weights = list(dist.weight if dist.weight is not None else 1 for dist in distro)
|
|
minw = min(weights)
|
|
norm = list(weight / minw for weight in weights)
|
|
total = sum(norm)
|
|
chunk = n // total
|
|
|
|
# random.choices would be enough to get a weighted distribution. But it can vary a lot for smaller k
|
|
# So instead we calculate our own distribution to make sure the actual ratios are close to what we would expect
|
|
|
|
# Calculate how many times each has to appear
|
|
indices = list(chain.from_iterable([idx] * int(n*chunk) for (idx, n) in enumerate(norm)))
|
|
|
|
# Complete with random agents following the original weight distribution
|
|
if len(indices) < n:
|
|
indices += random.choices(list(range(len(distro))), weights=[d.weight for d in distro], k=n-len(indices))
|
|
|
|
# Deserialize classes for efficiency
|
|
classes = list(serialization.deserialize(i.agent_class or default.agent_class) for i in distro)
|
|
|
|
# Add them in random order
|
|
random.shuffle(indices)
|
|
|
|
|
|
for idx in indices:
|
|
d = distro[idx]
|
|
agent = d.state.copy()
|
|
cls = classes[idx]
|
|
agent['agent_class'] = cls
|
|
if default:
|
|
agent.update(default.state)
|
|
topology = d.topology if ('topology' in d.__fields_set__) else topology or default.topology
|
|
if topology:
|
|
agent['topology'] = topology
|
|
agents.append(agent)
|
|
|
|
return agents
|
|
|
|
|
|
from .BassModel import *
|
|
from .BigMarketModel import *
|
|
from .IndependentCascadeModel import *
|
|
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)
|