mirror of https://github.com/gsi-upm/soil
Large set of changes for v0.30
The examples weren't being properly tested in the last commit. When we fixed that a lot of bugs in the new implementation of environment and agent were found, which accounts for most of these changes. The main difference is the mechanism to load simulations from a configuration file. For that to work, we had to rework our module loading code in `serialization` and add a `source_file` attribute to configurations (and simulations, for that matter).mesa
J. Fernando Sánchez
1 year ago
parent
73282530fd
commit
feab0ba79e
@ -1,7 +1,7 @@
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from soil import Simulation
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from social_wealth import MoneyEnv, graph_generator
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sim = Simulation(name="mesa_sim", dry_run=True, max_steps=10, interval=2, model=MoneyEnv, model_params=dict(generator=graph_generator, N=10, width=50, height=50))
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sim = Simulation(name="mesa_sim", dump=False, max_steps=10, interval=2, model=MoneyEnv, model_params=dict(generator=graph_generator, N=10, width=50, height=50))
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if __name__ == "__main__":
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sim.run()
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File diff suppressed because one or more lines are too long
@ -1,267 +1,2 @@
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from __future__ import annotations
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from enum import Enum
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from pydantic import BaseModel, ValidationError, validator, root_validator
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import yaml
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import os
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import sys
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from typing import Any, Callable, Dict, List, Optional, Union, Type
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from pydantic import BaseModel, Extra
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from . import environment, utils
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import networkx as nx
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# Could use TypeAlias in python >= 3.10
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nodeId = int
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class Node(BaseModel):
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id: nodeId
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state: Optional[Dict[str, Any]] = {}
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class Edge(BaseModel):
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source: nodeId
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target: nodeId
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value: Optional[float] = 1
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class Topology(BaseModel):
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nodes: List[Node]
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directed: bool
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links: List[Edge]
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class NetConfig(BaseModel):
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params: Optional[Dict[str, Any]]
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fixed: Optional[Union[Topology, nx.Graph]]
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path: Optional[str]
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class Config:
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arbitrary_types_allowed = True
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@staticmethod
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def default():
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return NetConfig(topology=None, params=None)
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@root_validator
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def validate_all(cls, values):
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if "params" not in values and "topology" not in values:
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raise ValueError(
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"You must specify either a topology or the parameters to generate a graph"
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)
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return values
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class EnvConfig(BaseModel):
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@staticmethod
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def default():
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return EnvConfig()
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class SingleAgentConfig(BaseModel):
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agent_class: Optional[Union[Type, str]] = None
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unique_id: Optional[int] = None
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topology: Optional[bool] = False
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node_id: Optional[Union[int, str]] = None
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state: Optional[Dict[str, Any]] = {}
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class FixedAgentConfig(SingleAgentConfig):
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n: Optional[int] = 1
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hidden: Optional[bool] = False # Do not count this agent towards total agent count
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@root_validator
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def validate_all(cls, values):
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if values.get("unique_id", None) is not None and values.get("n", 1) > 1:
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raise ValueError(
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f"An unique_id can only be provided when there is only one agent ({values.get('n')} given)"
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)
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return values
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class OverrideAgentConfig(FixedAgentConfig):
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filter: Optional[Dict[str, Any]] = None
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class Strategy(Enum):
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topology = "topology"
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total = "total"
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class AgentDistro(SingleAgentConfig):
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weight: Optional[float] = 1
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strategy: Strategy = Strategy.topology
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class AgentConfig(SingleAgentConfig):
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n: Optional[int] = None
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distribution: Optional[List[AgentDistro]] = None
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fixed: Optional[List[FixedAgentConfig]] = None
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override: Optional[List[OverrideAgentConfig]] = None
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@staticmethod
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def default():
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return AgentConfig()
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@root_validator
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def validate_all(cls, values):
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if "distribution" in values and (
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"n" not in values and "topology" not in values
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):
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raise ValueError(
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"You need to provide the number of agents or a topology to extract the value from."
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)
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return values
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class Config(BaseModel, extra=Extra.allow):
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version: Optional[str] = "1"
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name: str = "Unnamed Simulation"
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description: Optional[str] = None
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group: str = None
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dir_path: Optional[str] = None
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num_trials: int = 1
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max_time: float = 100
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max_steps: int = -1
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num_processes: int = 1
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interval: float = 1
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seed: str = ""
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dry_run: bool = False
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skip_test: bool = False
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model_class: Union[Type, str] = environment.Environment
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model_params: Optional[Dict[str, Any]] = {}
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visualization_params: Optional[Dict[str, Any]] = {}
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@classmethod
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def from_raw(cls, cfg):
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if isinstance(cfg, Config):
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return cfg
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if cfg.get("version", "1") == "1" and any(
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k in cfg for k in ["agents", "agent_class", "topology", "environment_class"]
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):
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return convert_old(cfg)
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return Config(**cfg)
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def convert_old(old, strict=True):
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"""
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Try to convert old style configs into the new format.
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This is still a work in progress and might not work in many cases.
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"""
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utils.logger.warning(
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"The old configuration format is deprecated. The converted file MAY NOT yield the right results"
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)
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new = old.copy()
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network = {}
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if "topology" in old:
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del new["topology"]
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network["topology"] = old["topology"]
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if "network_params" in old and old["network_params"]:
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del new["network_params"]
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for (k, v) in old["network_params"].items():
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if k == "path":
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network["path"] = v
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else:
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network.setdefault("params", {})[k] = v
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topology = None
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if network:
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topology = network
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agents = {"fixed": [], "distribution": []}
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def updated_agent(agent):
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"""Convert an agent definition"""
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newagent = dict(agent)
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return newagent
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by_weight = []
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fixed = []
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override = []
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if "environment_agents" in new:
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for agent in new["environment_agents"]:
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agent.setdefault("state", {})["group"] = "environment"
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if "agent_id" in agent:
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agent["state"]["name"] = agent["agent_id"]
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del agent["agent_id"]
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agent["hidden"] = True
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agent["topology"] = False
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fixed.append(updated_agent(agent))
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del new["environment_agents"]
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if "agent_class" in old:
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del new["agent_class"]
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agents["agent_class"] = old["agent_class"]
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if "default_state" in old:
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del new["default_state"]
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agents["state"] = old["default_state"]
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if "network_agents" in old:
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agents["topology"] = True
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agents.setdefault("state", {})["group"] = "network"
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for agent in new["network_agents"]:
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agent = updated_agent(agent)
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if "agent_id" in agent:
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agent["state"]["name"] = agent["agent_id"]
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del agent["agent_id"]
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fixed.append(agent)
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else:
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by_weight.append(agent)
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del new["network_agents"]
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if "agent_class" in old and (not fixed and not by_weight):
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agents["topology"] = True
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by_weight = [{"agent_class": old["agent_class"], "weight": 1}]
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# TODO: translate states properly
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if "states" in old:
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del new["states"]
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states = old["states"]
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if isinstance(states, dict):
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states = states.items()
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else:
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states = enumerate(states)
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for (k, v) in states:
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override.append({"filter": {"node_id": k}, "state": v})
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agents["override"] = override
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agents["fixed"] = fixed
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agents["distribution"] = by_weight
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model_params = {}
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if "environment_params" in new:
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del new["environment_params"]
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model_params = dict(old["environment_params"])
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if "environment_class" in old:
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del new["environment_class"]
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new["model_class"] = old["environment_class"]
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if "dump" in old:
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del new["dump"]
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new["dry_run"] = not old["dump"]
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model_params["topology"] = topology
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model_params["agents"] = agents
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return Config(version="2", model_params=model_params, **new)
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def load_config(cfg):
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return cfg
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@ -1,4 +1,6 @@
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def report(f: property):
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print(f.fget)
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setattr(f.fget, "add_to_report", True)
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if isinstance(f, property):
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setattr(f.fget, "add_to_report", True)
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else:
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setattr(f, "add_to_report", True)
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return f
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@ -1,49 +0,0 @@
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---
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version: '2'
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name: simple
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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: "CompleteSeed!"
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model_class: Environment
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model_params:
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topology:
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params:
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generator: complete_graph
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n: 4
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agents:
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agent_class: CounterModel
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state:
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group: network
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times: 1
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topology: true
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distribution:
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- agent_class: CounterModel
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weight: 0.25
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state:
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state_id: 0
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times: 1
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- agent_class: AggregatedCounter
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weight: 0.5
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state:
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times: 2
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override:
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- filter:
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node_id: 1
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state:
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name: 'Node 1'
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- filter:
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node_id: 2
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state:
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name: 'Node 2'
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fixed:
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- agent_class: BaseAgent
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hidden: true
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topology: false
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state:
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name: 'Environment Agent 1'
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times: 10
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group: environment
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am_i_complete: true
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@ -1,37 +0,0 @@
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---
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name: simple
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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: "CompleteSeed!"
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network_params:
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generator: complete_graph
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n: 4
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network_agents:
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- agent_class: CounterModel
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weight: 0.25
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state:
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state_id: 0
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times: 1
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- agent_class: AggregatedCounter
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weight: 0.5
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state:
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times: 2
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environment_agents:
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- agent_id: 'Environment Agent 1'
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agent_class: BaseAgent
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state:
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times: 10
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environment_class: Environment
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environment_params:
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am_i_complete: true
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agent_class: CounterModel
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default_state:
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times: 1
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states:
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1:
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name: 'Node 1'
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2:
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name: 'Node 2'
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@ -0,0 +1,5 @@
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---
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source_file: "../examples/torvalds_sim.py"
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model: "TorvaldsEnv"
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max_steps: 10
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interval: 2
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