mirror of
https://github.com/velocitatem/PHANTOM.git
synced 2026-05-31 16:43:36 +00:00
82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
from __future__ import annotations
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from typing import Any, Mapping
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import numpy as np
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def make_env(cfg: Mapping[str, Any]):
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from gymnasium.wrappers import FlattenObservation
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from ..lib.wrappers import EconomicMetricsWrapper
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from ..wrapper import PHANTOM
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env = PHANTOM(
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n_products=int(cfg["n_products"]),
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alpha=float(cfg["alpha"]),
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N=int(cfg["N"]),
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price_bounds=(float(cfg["price_low"]), float(cfg["price_high"])),
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lambda_coi=float(cfg["lambda_coi"]),
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robust_radius=float(cfg["robust_radius"]),
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robust_points=int(cfg["robust_points"]),
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info_value=float(cfg["info_value"]),
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action_levels=int(cfg["action_levels"]),
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action_scale_low=float(cfg["action_scale_low"]),
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action_scale_high=float(cfg["action_scale_high"]),
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max_steps=int(cfg.get("max_steps", 100)),
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margin_floor=float(cfg.get("margin_floor", 0.05)),
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margin_floor_patience=int(cfg.get("margin_floor_patience", 5)),
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render_mode=None,
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)
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env = EconomicMetricsWrapper(env)
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return FlattenObservation(env)
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def _action(agent: Any, obs: Any, deterministic: bool = True):
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out = agent.predict(obs, deterministic=deterministic)
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action = out[0] if isinstance(out, tuple) else out
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if isinstance(action, np.ndarray) and action.size == 1:
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return int(action.reshape(-1)[0])
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return action
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def evaluate(agent: Any, env: Any, episodes: int) -> dict[str, float]:
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rewards: list[float] = []
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revenues: list[float] = []
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margins: list[float] = []
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coi_levels: list[float] = []
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for _ in range(int(episodes)):
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obs, _ = env.reset()
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done = False
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ep_reward = 0.0
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ep_revenue = 0.0
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ep_margin = 0.0
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ep_coi = 0.0
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steps = 0
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while not done:
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obs, reward, term, trunc, info = env.step(_action(agent, obs, True))
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done = bool(term or trunc)
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econ = info.get("economics", {})
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ep_reward += float(reward)
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ep_revenue += float(econ.get("revenue", info.get("revenue", 0.0)))
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ep_margin += float(econ.get("margin", 0.0))
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ep_coi += float(econ.get("coi_level", 0.0))
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steps += 1
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rewards.append(ep_reward)
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revenues.append(ep_revenue)
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denom = max(steps, 1)
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margins.append(ep_margin / denom)
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coi_levels.append(ep_coi / denom)
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return {
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"eval/reward_mean": float(np.mean(rewards)) if rewards else 0.0,
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"eval/reward_std": float(np.std(rewards)) if rewards else 0.0,
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"eval/revenue_mean": float(np.mean(revenues)) if revenues else 0.0,
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"eval/revenue_std": float(np.std(revenues)) if revenues else 0.0,
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"eval/margin_mean": float(np.mean(margins)) if margins else 0.0,
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"eval/coi_level_mean": float(np.mean(coi_levels)) if coi_levels else 0.0,
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}
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