mirror of
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58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
from __future__ import annotations
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from typing import Any, Mapping
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from ..spec import TrainSpec
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_ALIASES = {
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"train/reward": "train/reward_mean",
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"train/revenue": "train/revenue_mean",
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"train/dqn_loss": "train/loss",
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"eval/reward": "eval/reward_mean",
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"eval/revenue": "eval/revenue_mean",
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"train/steps_per_second": "runtime/steps_per_second",
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}
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def _as_float(value: Any, default: float | None = None) -> float | None:
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if value is None:
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return default
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try:
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return float(value)
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except (TypeError, ValueError):
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return default
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def canonicalize_metrics(raw: Mapping[str, Any], spec: TrainSpec) -> dict[str, Any]:
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metrics: dict[str, Any] = {}
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for key, value in raw.items():
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canonical = _ALIASES.get(str(key), str(key))
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if canonical in metrics and canonical != key:
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continue
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metrics[canonical] = value
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metrics.setdefault("train/global_step", spec.runtime.total_timesteps)
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eval_reward = _as_float(metrics.get("eval/reward_mean"), 0.0) or 0.0
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eval_revenue = _as_float(metrics.get("eval/revenue_mean"), 0.0) or 0.0
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metrics["objective/score"] = eval_reward + spec.study.revenue_weight * eval_revenue
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margin_mean = _as_float(metrics.get("eval/margin_mean"), None)
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if margin_mean is not None:
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metrics["objective/constraint_margin"] = margin_mean - spec.env.margin_floor
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coi_level = _as_float(metrics.get("eval/coi_level_mean"), None)
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metrics["objective/coi_preserved"] = 0.0 if coi_level is None else coi_level
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metrics["study/alpha"] = spec.study.alpha
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metrics["study/lambda_coi"] = spec.study.lambda_coi
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metrics["study/robust_radius"] = spec.study.robust_radius
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metrics["study/info_value"] = spec.study.info_value
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metrics["runtime/backend"] = spec.runtime.backend
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metrics["runtime/device"] = spec.runtime.device
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metrics["runtime/seed"] = spec.runtime.seed
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return metrics
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