| """ |
| Standalone CausalGame evaluator. |
| |
| Loads a scenario's SCM + config directly from the CausalGame repo (no HTTP server) |
| and evaluates a drone design on a fleet, returning the survival rate. This mirrors |
| exactly what api/modules/agent/action_space.AgentActionSpace.execute() does for the |
| Stage-2 final evaluation, so numbers match the live backend. |
| """ |
| import os |
| import sys |
| import random |
| from pathlib import Path |
|
|
| CG_ROOT = Path(os.environ.get("CG_ROOT", Path(__file__).resolve().parents[1] / "CausalGame")) |
| sys.path.insert(0, str(CG_ROOT)) |
|
|
| from api.app import load_experiment_config |
| from api.modules.environment.scm_registry import get_scm_for_experiment |
| from api.modules.agent.action_space import AgentActionSpace, SubmitAction |
|
|
| DEFAULT_DESIGN = { |
| "engine_def": 20, "cockpit_def": 20, "wing_def": 15, |
| "body_def": 15, "antenna_def": 10, "camera_def": 5, "gun_def": 5, |
| } |
|
|
|
|
| def make_action_space(experiment: str): |
| cfg = load_experiment_config(experiment) |
| try: |
| scm = get_scm_for_experiment(experiment, cfg) |
| except ValueError: |
| |
| |
| |
| |
| base = experiment |
| scm = None |
| while "_" in base: |
| base = base.rsplit("_", 1)[0] |
| try: |
| scm = get_scm_for_experiment(base, cfg) |
| break |
| except ValueError: |
| continue |
| if scm is None: |
| raise |
| return AgentActionSpace(scm, cfg), cfg |
|
|
|
|
| def evaluate(experiment: str, design: dict, equipment: dict = None, seed: int = None): |
| """Return dict(survival_rate, survived, fleet_size, victory, threshold).""" |
| if seed is not None: |
| random.seed(seed) |
| asp, cfg = make_action_space(experiment) |
| res = asp.execute(SubmitAction(design=design, equipment=equipment or {})) |
| if not getattr(res, "success", False): |
| return {"error": getattr(res, "error", "unknown")} |
| return { |
| "survival_rate": res.survival_rate, |
| "survived": res.survived, |
| "fleet_size": res.fleet_size, |
| "victory": res.victory, |
| "threshold": res.victory_threshold, |
| } |
|
|
|
|
| def evaluate_mean(experiment: str, design: dict, equipment: dict = None, seeds=range(3)): |
| rates = [] |
| for s in seeds: |
| r = evaluate(experiment, design, equipment, seed=s) |
| if "error" in r: |
| return r |
| rates.append(r["survival_rate"]) |
| import statistics |
| return { |
| "survival_mean": statistics.mean(rates), |
| "survival_std": statistics.pstdev(rates) if len(rates) > 1 else 0.0, |
| "rates": rates, |
| "threshold": r["threshold"], |
| "victory_mean": statistics.mean(rates) >= r["threshold"], |
| } |
|
|
|
|
| if __name__ == "__main__": |
| exp = sys.argv[1] if len(sys.argv) > 1 else "antenna_trap" |
| print("Scenario:", exp) |
| print("Default design:", evaluate_mean(exp, DEFAULT_DESIGN)) |
|
|