mirror of https://github.com/gsi-upm/soil
WIP: removed stats
J. Fernando Sánchez
2 years ago
parent
3dc56892c1
commit
0a9c6d8b19
@ -1,264 +0,0 @@
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from pydantic import BaseModel, ValidationError, validator
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import yaml
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import os
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import sys
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import networkx as nx
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import collections.abc
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from . import serialization, utils, basestring, agents
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class Config(collections.abc.Mapping):
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"""
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1) agent type can be specified by name or by class.
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2) instead of just one type, a network agents distribution can be used.
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The distribution specifies the weight (or probability) of each
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agent type in the topology. This is an example distribution: ::
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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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3) if no initial state is given, each node's state will be set
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to `{'id': 0}`.
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Parameters
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---------
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name : str, optional
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name of the Simulation
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group : str, optional
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a group name can be used to link simulations
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topology (optional): networkx.Graph instance or Node-Link topology as a dict or string (will be loaded with `json_graph.node_link_graph(topology`).
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network_params : dict
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parameters used to create a topology with networkx, if no topology is given
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network_agents : dict
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definition of agents to populate the topology with
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agent_type : NetworkAgent subclass, optional
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Default type of NetworkAgent to use for nodes not specified in network_agents
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states : list, optional
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List of initial states corresponding to the nodes in the topology. Basic form is a list of integers
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whose value indicates the state
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dir_path: str, optional
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Directory path to load simulation assets (files, modules...)
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seed : str, optional
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Seed to use for the random generator
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num_trials : int, optional
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Number of independent simulation runs
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max_time : int, optional
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Maximum step/time for each simulation
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environment_params : dict, optional
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Dictionary of globally-shared environmental parameters
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environment_agents: dict, optional
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Similar to network_agents. Distribution of Agents that control the environment
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environment_class: soil.environment.Environment subclass, optional
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Class for the environment. It defailts to soil.environment.Environment
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"""
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__slots__ = 'name', 'agent_type', 'group', 'description', 'network_agents', 'environment_agents', 'states', 'default_state', 'interval', 'network_params', 'seed', 'num_trials', 'max_time', 'topology', 'schedule', 'initial_time', 'environment_params', 'environment_class', 'dir_path', '_added_to_path', 'visualization_params'
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def __init__(self, name=None,
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group=None,
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agent_type='BaseAgent',
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network_agents=None,
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environment_agents=None,
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states=None,
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description=None,
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default_state=None,
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interval=1,
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network_params=None,
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seed=None,
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num_trials=1,
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max_time=None,
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topology=None,
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schedule=None,
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initial_time=0,
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environment_params={},
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environment_class='soil.Environment',
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dir_path=None,
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visualization_params=None,
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):
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self.network_params = network_params
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self.name = name or 'Unnamed'
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self.description = description or 'No simulation description available'
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self.seed = str(seed or name)
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self.group = group or ''
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self.num_trials = num_trials
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self.max_time = max_time
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self.default_state = default_state or {}
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self.dir_path = dir_path or os.getcwd()
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self.interval = interval
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self.visualization_params = visualization_params or {}
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self._added_to_path = list(x for x in [os.getcwd(), self.dir_path] if x not in sys.path)
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sys.path += self._added_to_path
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self.topology = topology
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self.schedule = schedule
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self.initial_time = initial_time
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self.environment_class = environment_class
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self.environment_params = dict(environment_params)
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#TODO: Check agent distro vs fixed agents
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self.environment_agents = environment_agents or []
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self.agent_type = agent_type
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self.network_agents = network_agents or {}
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self.states = states or {}
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def validate(self):
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agents._validate_states(self.states,
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self._topology)
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def calculate(self):
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return CalculatedConfig(self)
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def restore_path(self):
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for added in self._added_to_path:
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sys.path.remove(added)
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def to_yaml(self):
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return yaml.dump(self.to_dict())
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def dump_yaml(self, f=None, outdir=None):
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if not f and not outdir:
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raise ValueError('specify a file or an output directory')
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if not f:
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f = os.path.join(outdir, '{}.dumped.yml'.format(self.name))
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with utils.open_or_reuse(f, 'w') as f:
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f.write(self.to_yaml())
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def to_yaml(self):
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return yaml.dump(self.to_dict())
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# TODO: See note on getstate
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def to_dict(self):
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return dict(self)
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def __repr__(self):
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return self.to_yaml()
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def dump_yaml(self, f=None, outdir=None):
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if not f and not outdir:
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raise ValueError('specify a file or an output directory')
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if not f:
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f = os.path.join(outdir, '{}.dumped.yml'.format(self.name))
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with utils.open_or_reuse(f, 'w') as f:
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f.write(self.to_yaml())
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def __getitem__(self, key):
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return getattr(self, key)
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def __iter__(self):
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return (k for k in self.__slots__ if k[0] != '_')
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def __len__(self):
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return len(self.__slots__)
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def dump_pickle(self, f=None, outdir=None):
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if not outdir and not f:
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raise ValueError('specify a file or an output directory')
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if not f:
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f = os.path.join(outdir,
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'{}.simulation.pickle'.format(self.name))
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with utils.open_or_reuse(f, 'wb') as f:
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pickle.dump(self, f)
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# TODO: remove this. A config should be sendable regardless. Non-pickable objects could be computed via properties and the like
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# def __getstate__(self):
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# state={}
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# for k, v in self.__dict__.items():
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# if k[0] != '_':
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# state[k] = v
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# state['topology'] = json_graph.node_link_data(self.topology)
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# state['network_agents'] = agents.serialize_definition(self.network_agents,
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# known_modules = [])
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# state['environment_agents'] = agents.serialize_definition(self.environment_agents,
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# known_modules = [])
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# state['environment_class'] = serialization.serialize(self.environment_class,
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# known_modules=['soil.environment'])[1] # func, name
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# if state['load_module'] is None:
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# del state['load_module']
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# return state
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# # TODO: remove, same as __getstate__
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# def __setstate__(self, state):
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# self.__dict__ = state
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# self.load_module = getattr(self, 'load_module', None)
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# if self.dir_path not in sys.path:
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# sys.path += [self.dir_path, os.getcwd()]
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# self.topology = json_graph.node_link_graph(state['topology'])
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# self.network_agents = agents.calculate_distribution(agents._convert_agent_types(self.network_agents))
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# self.environment_agents = agents._convert_agent_types(self.environment_agents,
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# known_modules=[self.load_module])
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# self.environment_class = serialization.deserialize(self.environment_class,
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# known_modules=[self.load_module,
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# 'soil.environment', ]) # func, name
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class CalculatedConfig(Config):
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def __init__(self, config):
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"""
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Returns a configuration object that replaces some "plain" attributes (e.g., `environment_class` string) into
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a Python object (`soil.environment.Environment` class).
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"""
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self._config = config
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values = dict(config)
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values['environment_class'] = self._environment_class()
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values['environment_agents'] = self._environment_agents()
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values['topology'] = self._topology()
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values['network_agents'] = self._network_agents()
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values['agent_type'] = serialization.deserialize(self.agent_type, known_modules=['soil.agents'])
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return values
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def _topology(self):
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topology = self._config.topology
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if topology is None:
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topology = serialization.load_network(self._config.network_params,
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dir_path=self._config.dir_path)
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elif isinstance(topology, basestring) or isinstance(topology, dict):
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topology = json_graph.node_link_graph(topology)
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return nx.Graph(topology)
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def _environment_class(self):
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return serialization.deserialize(self._config.environment_class,
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known_modules=['soil.environment', ]) or Environment
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def _environment_agents(self):
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return agents._convert_agent_types(self._config.environment_agents)
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def _network_agents(self):
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distro = agents.calculate_distribution(self._config.network_agents,
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self._config.agent_type)
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return agents._convert_agent_types(distro)
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def _environment_class(self):
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return serialization.deserialize(self._config.environment_class,
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known_modules=['soil.environment', ]) # func, name
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@ -1,111 +0,0 @@
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import pandas as pd
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from collections import Counter
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class Stats:
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'''
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Interface for all stats. It is not necessary, but it is useful
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if you don't plan to implement all the methods.
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'''
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def __init__(self, simulation, name=None):
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self.name = name or type(self).__name__
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self.simulation = simulation
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def sim_start(self):
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'''Method to call when the simulation starts'''
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pass
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def sim_end(self):
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'''Method to call when the simulation ends'''
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return {}
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def trial_start(self, env):
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'''Method to call when a trial starts'''
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return {}
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def trial_end(self, env):
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'''Method to call when a trial ends'''
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return {}
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class distribution(Stats):
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'''
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Calculate the distribution of agent states at the end of each trial,
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the mean value, and its deviation.
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'''
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def sim_start(self):
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self.means = []
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self.counts = []
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def trial_end(self, env):
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df = pd.DataFrame(env.state_to_tuples())
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df = df.drop('SEED', axis=1)
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ix = df.index[-1]
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attrs = df.columns.get_level_values(0)
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vc = {}
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stats = {
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'mean': {},
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'count': {},
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}
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for a in attrs:
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t = df.loc[(ix, a)]
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try:
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stats['mean'][a] = t.mean()
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self.means.append(('mean', a, t.mean()))
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except TypeError:
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pass
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for name, count in t.value_counts().iteritems():
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if a not in stats['count']:
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stats['count'][a] = {}
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stats['count'][a][name] = count
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self.counts.append(('count', a, name, count))
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return stats
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def sim_end(self):
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dfm = pd.DataFrame(self.means, columns=['metric', 'key', 'value'])
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dfc = pd.DataFrame(self.counts, columns=['metric', 'key', 'value', 'count'])
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count = {}
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mean = {}
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if self.means:
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res = dfm.groupby(by=['key']).agg(['mean', 'std', 'count', 'median', 'max', 'min'])
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mean = res['value'].to_dict()
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if self.counts:
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res = dfc.groupby(by=['key', 'value']).agg(['mean', 'std', 'count', 'median', 'max', 'min'])
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for k,v in res['count'].to_dict().items():
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if k not in count:
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count[k] = {}
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for tup, times in v.items():
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subkey, subcount = tup
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if subkey not in count[k]:
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count[k][subkey] = {}
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count[k][subkey][subcount] = times
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return {'count': count, 'mean': mean}
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class defaultStats(Stats):
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def trial_end(self, env):
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c = Counter()
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c.update(a.__class__.__name__ for a in env.network_agents)
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c2 = Counter()
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c2.update(a['id'] for a in env.network_agents)
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return {
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'network ': {
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'n_nodes': env.G.number_of_nodes(),
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'n_edges': env.G.number_of_edges(),
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},
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'agents': {
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'model_count': dict(c),
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'state_count': dict(c2),
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}
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}
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@ -1,34 +0,0 @@
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from unittest import TestCase
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from soil import simulation, stats
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from soil.utils import unflatten_dict
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class Stats(TestCase):
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def test_distribution(self):
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'''The distribution exporter should write the number of agents in each state'''
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config = {
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'name': 'exporter_sim',
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'network_params': {
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'generator': 'complete_graph',
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'n': 4
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},
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'agent_type': 'CounterModel',
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'max_time': 2,
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'num_trials': 5,
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'environment_params': {}
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}
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s = simulation.from_config(config)
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|
||||||
for env in s.run_simulation(stats=[stats.distribution]):
|
|
||||||
pass
|
|
||||||
# stats_res = unflatten_dict(dict(env._history['stats', -1, None]))
|
|
||||||
allstats = s.get_stats()
|
|
||||||
for stat in allstats:
|
|
||||||
assert 'count' in stat
|
|
||||||
assert 'mean' in stat
|
|
||||||
if 'trial_id' in stat:
|
|
||||||
assert stat['mean']['neighbors'] == 3
|
|
||||||
assert stat['count']['total']['4'] == 4
|
|
||||||
else:
|
|
||||||
assert stat['count']['count']['neighbors']['3'] == 20
|
|
||||||
assert stat['mean']['min']['neighbors'] == stat['mean']['max']['neighbors']
|
|
||||||
Loading…
Reference in New Issue