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updating engine training for training

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This commit is contained in:
Daniel Rosel
2026-03-15 21:14:11 +01:00
parent 19b47aa699
commit 52b4dcdce3
13 changed files with 544 additions and 160 deletions
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193
engine/benchmark.py
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@@ -45,6 +45,10 @@ def _log(message: str) -> None:
logger.info(message)
def _wandb_run_active() -> bool:
return bool(HAS_WANDB and getattr(wandb, "run", None) is not None)
def _parse_list(raw: str) -> list[str]:
return [x.strip().lower() for x in str(raw).split(",") if x.strip()]
@@ -61,6 +65,10 @@ def _truthy(value: str | bool | None) -> bool:
return str(value).strip().lower() in {"1", "true", "yes", "on"}
def _mode_label_from_baseline(is_baseline: bool) -> str:
return "baseline" if bool(is_baseline) else "defended"
def _action(policy, obs: np.ndarray):
out = policy.predict(obs, deterministic=True)
action = out[0] if isinstance(out, tuple) else out
@@ -166,7 +174,7 @@ def _log_train_events(
alpha: float,
step_offset: int,
) -> int:
if not (HAS_WANDB and wandb.run is not None):
if not _wandb_run_active():
return int(step_offset)
if not events:
return int(step_offset)
@@ -187,11 +195,14 @@ def _log_train_events(
"run.kind": "benchmark",
"runtime/backend": tier_name,
"study/mode": mode_label,
"study/no_robust": float(mode_label == "no_robust"),
"study/baseline_mode": float(mode_label == "baseline"),
"study/alpha": float(alpha),
}
)
wandb.log(payload, step=cursor + rel_step)
try:
wandb.log(payload, step=cursor + rel_step)
except Exception:
return int(step_offset)
max_rel = max(max(1, int(evt.get("train/global_step", 0))) for evt in ordered)
return cursor + max_rel + 1
@@ -203,6 +214,7 @@ def run_benchmark(
n_episodes: int,
mode_label: str,
step_cursor_start: int = 0,
eval_alpha_values: list[float] | None = None,
):
from .backends.common import make_env
@@ -239,62 +251,80 @@ def run_benchmark(
"dqn",
}:
wandb_step_cursor += max(1, int(cfg.get("total_timesteps", 1))) + 1
env = make_env({**cfg, "alpha": float(alpha)})
eps = [_run_eval_episode(env, policy) for _ in range(int(n_episodes))]
env.close()
row = {
"tier": tier_name,
"mode": mode_label,
"alpha": float(alpha),
"episodes": int(n_episodes),
"mean_reward": float(np.mean([e["reward"] for e in eps])),
"mean_revenue": float(np.mean([e["revenue"] for e in eps])),
"mean_margin": float(np.mean([e["mean_margin"] for e in eps])),
"mean_coi": float(np.mean([e["mean_coi"] for e in eps])),
"std_revenue": float(np.std([e["revenue"] for e in eps])),
}
row["objective_score"] = row["mean_reward"]
rows.append(row)
_log(
f"[{run_index}/{total_runs}] alpha={float(alpha):.2f} tier={tier_name}: "
f"reward={row['mean_reward']:.3f} revenue={row['mean_revenue']:.3f} "
f"coi={row['mean_coi']:.4f} score={row['objective_score']:.3f}"
eval_targets = (
[float(value) for value in eval_alpha_values]
if eval_alpha_values
else [float(alpha)]
)
for eval_alpha in eval_targets:
env = make_env({**cfg, "alpha": float(eval_alpha)})
eps = [_run_eval_episode(env, policy) for _ in range(int(n_episodes))]
env.close()
max_len = max((len(e["price_trace"]) for e in eps), default=0)
step_means = []
for step in range(max_len):
vals = [
e["price_trace"][step] for e in eps if step < len(e["price_trace"])
]
step_means.append(float(np.mean(vals)) if vals else np.nan)
traces.append(
{
row = {
"tier": tier_name,
"alpha": float(alpha),
"mean_price_trace": step_means,
"mode": mode_label,
"alpha": float(eval_alpha),
"train_alpha": float(alpha),
"eval_alpha": float(eval_alpha),
"episodes": int(n_episodes),
"mean_reward": float(np.mean([e["reward"] for e in eps])),
"mean_revenue": float(np.mean([e["revenue"] for e in eps])),
"mean_margin": float(np.mean([e["mean_margin"] for e in eps])),
"mean_coi": float(np.mean([e["mean_coi"] for e in eps])),
"std_revenue": float(np.std([e["revenue"] for e in eps])),
}
)
if HAS_WANDB and wandb.run is not None:
wandb.log(
{
"run.kind": "benchmark",
"runtime/backend": tier_name,
"study/mode": mode_label,
"study/no_robust": float(mode_label == "no_robust"),
"study/alpha": float(alpha),
"eval/reward_mean": row["mean_reward"],
"eval/revenue_mean": row["mean_revenue"],
"eval/margin_mean": row["mean_margin"],
"eval/coi_level_mean": row["mean_coi"],
"objective/score": row["objective_score"],
"objective/coi_preserved": row["mean_coi"],
},
step=wandb_step_cursor,
row["objective_score"] = row["mean_reward"]
rows.append(row)
_log(
f"[{run_index}/{total_runs}] train_alpha={float(alpha):.2f} "
f"eval_alpha={float(eval_alpha):.2f} tier={tier_name}: "
f"reward={row['mean_reward']:.3f} revenue={row['mean_revenue']:.3f} "
f"coi={row['mean_coi']:.4f} score={row['objective_score']:.3f}"
)
wandb_step_cursor += 1
max_len = max((len(e["price_trace"]) for e in eps), default=0)
step_means = []
for step in range(max_len):
vals = [
e["price_trace"][step]
for e in eps
if step < len(e["price_trace"])
]
step_means.append(float(np.mean(vals)) if vals else np.nan)
traces.append(
{
"tier": tier_name,
"alpha": float(eval_alpha),
"train_alpha": float(alpha),
"eval_alpha": float(eval_alpha),
"mean_price_trace": step_means,
}
)
if _wandb_run_active():
try:
wandb.log(
{
"run.kind": "benchmark",
"runtime/backend": tier_name,
"study/mode": mode_label,
"study/baseline_mode": float(mode_label == "baseline"),
"study/alpha": float(eval_alpha),
"study/train_alpha": float(alpha),
"study/eval_alpha": float(eval_alpha),
"eval/reward_mean": row["mean_reward"],
"eval/revenue_mean": row["mean_revenue"],
"eval/margin_mean": row["mean_margin"],
"eval/coi_level_mean": row["mean_coi"],
"objective/score": row["objective_score"],
"objective/coi_preserved": row["mean_coi"],
},
step=wandb_step_cursor,
)
except Exception:
pass
wandb_step_cursor += 1
return pd.DataFrame(rows), traces, int(wandb_step_cursor)
@@ -378,7 +408,7 @@ def _run_with_args(args, compare_robust_override: bool | None = None):
if compare_robust_override is not None
else _truthy(os.environ.get("PHANTOM_BENCHMARK_COMPARE_ROBUST"))
)
robust_modes = [False, True] if compare_robust else [bool(args.no_robust)]
baseline_modes = [False, True] if compare_robust else [bool(args.no_robust)]
base_overrides = {
"seed": args.seed,
@@ -389,6 +419,7 @@ def _run_with_args(args, compare_robust_override: bool | None = None):
"robust_radius": args.robust_radius,
"robust_points": args.robust_points,
"robust_rollouts": args.robust_rollouts,
"margin_floor": args.margin_floor,
"eta_ux": args.eta_ux,
"reward_profit_weight": args.reward_profit_weight,
"price_low": args.price_low,
@@ -405,12 +436,20 @@ def _run_with_args(args, compare_robust_override: bool | None = None):
}
tiers = _parse_list(args.tiers)
alpha_values = _parse_float_list(args.alpha_values)
eval_alpha_values = (
_parse_float_list(args.eval_alpha_values)
if str(getattr(args, "eval_alpha_values", "")).strip()
else []
)
_log(
"starting run "
+ json.dumps(
{
"tiers": tiers,
"alpha_values": alpha_values,
"eval_alpha_values": (
eval_alpha_values if eval_alpha_values else alpha_values
),
"episodes": int(args.episodes),
"total_timesteps": int(args.total_timesteps),
"device": str(args.device),
@@ -421,14 +460,14 @@ def _run_with_args(args, compare_robust_override: bool | None = None):
all_frames: list[pd.DataFrame] = []
all_traces: list[dict] = []
wandb_step_cursor = 0
for no_robust in robust_modes:
for baseline_mode in baseline_modes:
overrides = dict(base_overrides)
overrides["no_robust"] = bool(no_robust)
overrides["baseline_mode"] = bool(baseline_mode)
cfg = TrainSpec.from_flat(
{k: v for k, v in overrides.items() if v is not None}
).to_flat_dict()
cfg["linear_warmup_steps"] = int(args.linear_warmup_steps)
mode_label = "no_robust" if no_robust else "robust"
mode_label = _mode_label_from_baseline(bool(baseline_mode))
_log(f"mode={mode_label}: begin")
df_mode, traces_mode, wandb_step_cursor = run_benchmark(
cfg,
@@ -437,6 +476,7 @@ def _run_with_args(args, compare_robust_override: bool | None = None):
args.episodes,
mode_label=mode_label,
step_cursor_start=wandb_step_cursor,
eval_alpha_values=eval_alpha_values,
)
_log(f"mode={mode_label}: complete ({len(df_mode)} rows)")
for trace in traces_mode:
@@ -465,7 +505,7 @@ def _run_with_args(args, compare_robust_override: bool | None = None):
+ json.dumps(
{
"tier": best["tier"],
"mode": best.get("mode", "robust"),
"mode": best.get("mode", "defended"),
"alpha": float(best["alpha"]),
"objective_score": float(best["objective_score"]),
"mean_revenue": float(best["mean_revenue"]),
@@ -486,6 +526,7 @@ def run_cli(raw_args: list[str] | None = None):
parser.add_argument("--project", default="capstone")
parser.add_argument("--tiers", default="static,surge,linear,qtable,ppo")
parser.add_argument("--alpha-values", default="0.0,0.3,0.6")
parser.add_argument("--eval-alpha-values", default="")
parser.add_argument("--episodes", type=int, default=10)
parser.add_argument("--output-dir", default="engine/studies/results")
parser.add_argument("--seed", type=int, default=42)
@@ -496,6 +537,7 @@ def run_cli(raw_args: list[str] | None = None):
parser.add_argument("--robust-radius", type=float, default=0.15)
parser.add_argument("--robust-points", type=int, default=5)
parser.add_argument("--robust-rollouts", type=int, default=1)
parser.add_argument("--margin-floor", type=float, default=0.85)
parser.add_argument("--eta-ux", type=float, default=0.5)
parser.add_argument("--reward-profit-weight", type=float, default=1.0)
parser.add_argument("--price-low", type=float, default=10.0)
@@ -529,35 +571,47 @@ def run_cli(raw_args: list[str] | None = None):
key_to_attr = {
"tiers": "tiers",
"alpha_values": "alpha_values",
"eval_alpha_values": "eval_alpha_values",
"episodes": "episodes",
"total_timesteps": "total_timesteps",
"lambda_coi": "lambda_coi",
"robust_radius": "robust_radius",
"robust_points": "robust_points",
"robust_rollouts": "robust_rollouts",
"ambiguity_radius": "robust_radius",
"ambiguity_points": "robust_points",
"ambiguity_rollouts": "robust_rollouts",
"eta_ux": "eta_ux",
"reward_profit_weight": "reward_profit_weight",
"learning_rate": "learning_rate",
"batch_size": "batch_size",
"n_steps": "n_steps",
"baseline_mode": "no_robust",
"no_robust": "no_robust",
"margin_floor": "margin_floor",
"device": "device",
}
for key in (
"tiers",
"alpha_values",
"eval_alpha_values",
"episodes",
"total_timesteps",
"lambda_coi",
"robust_radius",
"robust_points",
"robust_rollouts",
"ambiguity_radius",
"ambiguity_points",
"ambiguity_rollouts",
"eta_ux",
"reward_profit_weight",
"learning_rate",
"batch_size",
"n_steps",
"baseline_mode",
"no_robust",
"margin_floor",
"device",
):
if key in wandb.config:
@@ -582,16 +636,16 @@ def run_cli(raw_args: list[str] | None = None):
alpha_values = _parse_float_list(args.alpha_values)
run_stamp = datetime.now(timezone.utc).strftime("%m%d-%H%M%S")
compare_enabled = _truthy(os.environ.get("PHANTOM_BENCHMARK_COMPARE_ROBUST"))
compare_tag = "robust-compare" if compare_enabled else "single-mode"
compare_tag = "defended-compare" if compare_enabled else "single-mode"
modes = (
[("no_robust", True), ("robust", False)]
[("baseline", True), ("defended", False)]
if compare_enabled
else [("no_robust" if bool(args.no_robust) else "robust", bool(args.no_robust))]
else [(_mode_label_from_baseline(bool(args.no_robust)), bool(args.no_robust))]
)
run_idx = 0
for tier in tiers:
for mode_label, no_robust in modes:
for mode_label, baseline_mode in modes:
for alpha in alpha_values:
run_idx += 1
alpha_token = (
@@ -600,7 +654,7 @@ def run_cli(raw_args: list[str] | None = None):
tier_args = argparse.Namespace(**vars(args))
tier_args.tiers = tier
tier_args.alpha_values = str(float(alpha))
tier_args.no_robust = bool(no_robust)
tier_args.no_robust = bool(baseline_mode)
run = wandb.init(
project=args.project,
name=(
@@ -617,16 +671,19 @@ def run_cli(raw_args: list[str] | None = None):
"run.kind": "benchmark",
"runtime/backend": tier,
"study/mode": mode_label,
"study/no_robust": float(no_robust),
"study/baseline_mode": float(baseline_mode),
"study/alpha": float(alpha),
"tiers": tier,
"alpha_values": str(float(alpha)),
"eval_alpha_values": args.eval_alpha_values,
"episodes": args.episodes,
"total_timesteps": args.total_timesteps,
"lambda_coi": args.lambda_coi,
"robust_radius": args.robust_radius,
"robust_points": args.robust_points,
"robust_rollouts": args.robust_rollouts,
"ambiguity_radius": args.robust_radius,
"ambiguity_points": args.robust_points,
"ambiguity_rollouts": args.robust_rollouts,
"margin_floor": args.margin_floor,
"baseline_mode": float(baseline_mode),
"eta_ux": args.eta_ux,
"reward_profit_weight": args.reward_profit_weight,
"learning_rate": args.learning_rate,