refactoring training spc setup and benchmarking

engine/backends/common.py

@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+from typing import Any, Mapping
+
+import numpy as np
+
+
+def make_env(cfg: Mapping[str, Any]):
+    from gymnasium.wrappers import FlattenObservation
+
+    from ..lib.wrappers import EconomicMetricsWrapper
+    from ..wrapper import PHANTOM
+
+    env = PHANTOM(
+        n_products=int(cfg["n_products"]),
+        alpha=float(cfg["alpha"]),
+        N=int(cfg["N"]),
+        price_bounds=(float(cfg["price_low"]), float(cfg["price_high"])),
+        lambda_coi=float(cfg["lambda_coi"]),
+        robust_radius=float(cfg["robust_radius"]),
+        robust_points=int(cfg["robust_points"]),
+        info_value=float(cfg["info_value"]),
+        action_levels=int(cfg["action_levels"]),
+        action_scale_low=float(cfg["action_scale_low"]),
+        action_scale_high=float(cfg["action_scale_high"]),
+        max_steps=int(cfg.get("max_steps", 100)),
+        margin_floor=float(cfg.get("margin_floor", 0.05)),
+        margin_floor_patience=int(cfg.get("margin_floor_patience", 5)),
+        render_mode=None,
+    )
+    env = EconomicMetricsWrapper(env)
+    return FlattenObservation(env)
+
+
+def _action(agent: Any, obs: Any, deterministic: bool = True):
+    out = agent.predict(obs, deterministic=deterministic)
+    action = out[0] if isinstance(out, tuple) else out
+    if isinstance(action, np.ndarray) and action.size == 1:
+        return int(action.reshape(-1)[0])
+    return action
+
+
+def evaluate(agent: Any, env: Any, episodes: int) -> dict[str, float]:
+    rewards: list[float] = []
+    revenues: list[float] = []
+    margins: list[float] = []
+    coi_levels: list[float] = []
+
+    for _ in range(int(episodes)):
+        obs, _ = env.reset()
+        done = False
+        ep_reward = 0.0
+        ep_revenue = 0.0
+        ep_margin = 0.0
+        ep_coi = 0.0
+        steps = 0
+
+        while not done:
+            obs, reward, term, trunc, info = env.step(_action(agent, obs, True))
+            done = bool(term or trunc)
+            econ = info.get("economics", {})
+            ep_reward += float(reward)
+            ep_revenue += float(econ.get("revenue", info.get("revenue", 0.0)))
+            ep_margin += float(econ.get("margin", 0.0))
+            ep_coi += float(econ.get("coi_level", 0.0))
+            steps += 1
+
+        rewards.append(ep_reward)
+        revenues.append(ep_revenue)
+        denom = max(steps, 1)
+        margins.append(ep_margin / denom)
+        coi_levels.append(ep_coi / denom)
+
+    return {
+        "eval/reward_mean": float(np.mean(rewards)) if rewards else 0.0,
+        "eval/reward_std": float(np.std(rewards)) if rewards else 0.0,
+        "eval/revenue_mean": float(np.mean(revenues)) if revenues else 0.0,
+        "eval/revenue_std": float(np.std(revenues)) if revenues else 0.0,
+        "eval/margin_mean": float(np.mean(margins)) if margins else 0.0,
+        "eval/coi_level_mean": float(np.mean(coi_levels)) if coi_levels else 0.0,
+    }