mirror of
https://github.com/velocitatem/PHANTOM.git
synced 2026-05-31 08:33:36 +00:00
updating engine training for training
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@@ -15,15 +15,19 @@ class MetricsCallback(BaseCallback):
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self,
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log_histograms: bool = False,
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log_freq: int = 100,
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hist_freq: int = 500,
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step_offset: int = 0,
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verbose: int = 0,
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):
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super().__init__(verbose)
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self.log_histograms = log_histograms
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self.log_freq = max(1, int(log_freq))
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self.hist_freq = max(1, int(hist_freq))
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self.step_offset = max(0, int(step_offset))
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self._wandb = get_wandb_module()
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self._wandb_live = bool(self._wandb is not None and self._wandb.run is not None)
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self._price_samples: list[float] = []
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self._demand_samples: list[float] = []
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self._window_sums = {
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"train/revenue_mean": 0.0,
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"train/margin_mean": 0.0,
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@@ -74,35 +78,100 @@ class MetricsCallback(BaseCallback):
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)
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self._window_count += 1
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def _flush(self, step: int) -> None:
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if self._window_count <= 0:
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def _accumulate_histograms(self, info: dict[str, Any]) -> None:
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if not self.log_histograms:
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return
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denom = float(self._window_count)
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payload = {
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key: (value / denom)
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for key, value in self._window_sums.items()
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if value != 0.0
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or key
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in {
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"train/revenue_mean",
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"train/margin_mean",
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"train/coi_level_mean",
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"train/regret_mean",
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for key in ("effective_prices", "prices"):
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if key not in info:
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continue
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try:
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values = np.asarray(info.get(key), dtype=float).reshape(-1)
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except Exception:
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continue
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if values.size <= 0:
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continue
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finite_values = values[np.isfinite(values)]
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if finite_values.size > 0:
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self._price_samples.extend(finite_values.tolist())
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break
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if "demand" in info:
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try:
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demand_values = np.asarray(info.get("demand"), dtype=float).reshape(-1)
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except Exception:
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demand_values = np.array([], dtype=float)
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if demand_values.size > 0:
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finite_demand = demand_values[np.isfinite(demand_values)]
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if finite_demand.size > 0:
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self._demand_samples.extend(finite_demand.tolist())
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def _flush_histograms(self, step: int, force: bool = False) -> None:
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if not self.log_histograms:
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return
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if not force and step % self.hist_freq != 0:
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return
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if not self._price_samples and not self._demand_samples:
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return
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if self._wandb is None:
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self._price_samples.clear()
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self._demand_samples.clear()
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return
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payload: dict[str, Any] = {}
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if self._price_samples:
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payload["train/price_dist"] = self._wandb.Histogram(
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np.asarray(self._price_samples, dtype=np.float32)
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)
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if self._demand_samples:
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payload["train/demand_dist"] = self._wandb.Histogram(
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np.asarray(self._demand_samples, dtype=np.float32)
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)
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if payload and self._wandb_live:
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try:
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self._wandb.log(payload, step=self.step_offset + int(step))
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except Exception:
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self._wandb_live = False
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self._price_samples.clear()
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self._demand_samples.clear()
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def _flush(self, step: int, *, force_hist: bool = False) -> None:
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if self._window_count > 0:
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denom = float(self._window_count)
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payload = {
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key: (value / denom)
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for key, value in self._window_sums.items()
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if value != 0.0
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or key
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in {
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"train/revenue_mean",
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"train/margin_mean",
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"train/coi_level_mean",
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"train/regret_mean",
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}
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}
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}
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payload["train/global_step"] = int(step)
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if self._wandb_live:
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self._wandb.log(dict(payload), step=self.step_offset + int(step))
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else:
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self.events.append(payload)
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for key in self._window_sums:
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self._window_sums[key] = 0.0
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self._window_count = 0
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payload["train/global_step"] = int(step)
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if self._wandb_live:
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try:
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self._wandb.log(dict(payload), step=self.step_offset + int(step))
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except Exception:
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self._wandb_live = False
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self.events.append(payload)
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else:
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self.events.append(payload)
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for key in self._window_sums:
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self._window_sums[key] = 0.0
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self._window_count = 0
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self._flush_histograms(step=step, force=force_hist)
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def _on_step(self) -> bool:
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for info in self.locals.get("infos", []):
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if isinstance(info, dict):
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self._accumulate(info)
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self._accumulate_histograms(info)
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if self.num_timesteps % self.log_freq == 0:
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self._flush(step=self.num_timesteps)
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@@ -110,39 +179,81 @@ class MetricsCallback(BaseCallback):
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return True
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def _on_training_end(self) -> None:
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self._flush(step=self.num_timesteps)
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self._flush(step=self.num_timesteps, force_hist=True)
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class EvalMetricsCallback(EvalCallback):
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"""Deterministic evaluation collector detached from logging backends."""
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def __init__(
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self, eval_env, eval_freq: int = 1000, n_eval_episodes: int = 5, **kwargs
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self,
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eval_env,
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eval_freq: int = 1000,
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n_eval_episodes: int = 5,
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step_offset: int = 0,
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**kwargs,
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):
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super().__init__(
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eval_env, eval_freq=eval_freq, n_eval_episodes=n_eval_episodes, **kwargs
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)
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self._eval_revenues: list[float] = []
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self.step_offset = max(0, int(step_offset))
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self._wandb = get_wandb_module()
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self._wandb_live = bool(self._wandb is not None and self._wandb.run is not None)
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self._eval_stats: dict[str, list[float]] = {
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"eval/revenue_mean": [],
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"eval/margin_mean": [],
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"eval/coi_level_mean": [],
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"eval/coi_leakage_mean": [],
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"eval/volatility_mean": [],
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"eval/agent_prob_mean": [],
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}
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self.events: list[dict[str, float | int]] = []
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def _on_step(self) -> bool:
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result = super()._on_step()
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if self.n_calls % self.eval_freq == 0 and hasattr(self, "last_mean_reward"):
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self.events.append(
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{
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"eval/reward_mean": float(self.last_mean_reward),
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"eval/revenue_mean": float(np.mean(self._eval_revenues))
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if self._eval_revenues
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else 0.0,
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"train/global_step": int(self.num_timesteps),
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}
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)
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self._eval_revenues = []
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payload: dict[str, float | int] = {
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"eval/reward_mean": float(self.last_mean_reward),
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"train/global_step": int(self.num_timesteps),
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}
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for key, values in self._eval_stats.items():
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payload[key] = float(np.mean(values)) if values else 0.0
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if self._wandb_live:
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try:
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self._wandb.log(
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dict(payload),
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step=self.step_offset + int(self.num_timesteps),
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)
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except Exception:
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self._wandb_live = False
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self.events.append(payload)
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else:
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self.events.append(payload)
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for values in self._eval_stats.values():
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values.clear()
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return result
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def _log_success_callback(self, locals_: dict, globals_: dict) -> None:
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# called after each eval episode
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info = locals_.get("info", {})
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if "economics" in info:
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self._eval_revenues.append(info["economics"]["revenue"])
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econ = info.get("economics") if isinstance(info, dict) else None
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if not isinstance(econ, dict):
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return
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self._eval_stats["eval/revenue_mean"].append(float(econ.get("revenue", 0.0)))
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self._eval_stats["eval/margin_mean"].append(float(econ.get("margin", 0.0)))
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self._eval_stats["eval/coi_level_mean"].append(
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float(econ.get("coi_level", 0.0))
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)
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self._eval_stats["eval/coi_leakage_mean"].append(
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float(econ.get("coi_leakage", 0.0))
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)
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self._eval_stats["eval/volatility_mean"].append(
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float(econ.get("volatility", 0.0))
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)
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self._eval_stats["eval/agent_prob_mean"].append(
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float(econ.get("agent_prob", 0.0))
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)
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