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| """Agent-safe evaluation protocol helpers for pathway_analysis_env.""" |
|
|
| from __future__ import annotations |
|
|
| from copy import deepcopy |
| from typing import Any, Dict, List, Optional |
|
|
| from ..models import PathwayObservation |
|
|
| |
| _AGENT_METADATA_BLOCKLIST = frozenset( |
| { |
| "correct", |
| "static_top_genes", |
| "static_top_pathways", |
| "true_pathway", |
| "ground_truth", |
| "episode_score", |
| } |
| ) |
|
|
|
|
| def default_max_steps(case: Dict[str, Any]) -> int: |
| return max(5, int(case.get("max_steps", 30))) |
|
|
|
|
| def resolve_eval_mode(case: Dict[str, Any], reset_kwargs: Dict[str, Any]) -> bool: |
| """Eval mode is on unless reset(eval_mode=False) or case sets eval_mode: false.""" |
| if "eval_mode" in reset_kwargs: |
| return bool(reset_kwargs["eval_mode"]) |
| return bool(case.get("eval_mode", True)) |
|
|
|
|
| def resolve_orchestrator_mode(case: Dict[str, Any], reset_kwargs: Dict[str, Any]) -> bool: |
| """Expose scoring details in metadata (for in-repo harnesses only).""" |
| if "orchestrator_mode" in reset_kwargs: |
| return bool(reset_kwargs["orchestrator_mode"]) |
| return bool(case.get("orchestrator_mode", False)) |
|
|
|
|
| def shaping_reward(eval_mode: bool, nominal: float) -> float: |
| """Zero intermediate shaping in eval mode; terminal scoring is separate.""" |
| if eval_mode: |
| return 0.0 |
| return nominal |
|
|
|
|
| def sanitize_metadata_for_agent( |
| metadata: Optional[Dict[str, Any]], *, eval_mode: bool |
| ) -> Dict[str, Any]: |
| if not metadata: |
| return {} |
| if not eval_mode: |
| return dict(metadata) |
| out = {k: v for k, v in metadata.items() if k not in _AGENT_METADATA_BLOCKLIST} |
| return out |
|
|
|
|
| def sanitize_observation_for_agent( |
| obs: PathwayObservation, |
| *, |
| eval_mode: bool, |
| orchestrator_mode: bool, |
| reward_override: Optional[float] = None, |
| ) -> PathwayObservation: |
| if not eval_mode: |
| return obs |
| meta = sanitize_metadata_for_agent(obs.metadata, eval_mode=True) |
| if orchestrator_mode and obs.metadata and "correct" in obs.metadata: |
| meta["correct"] = obs.metadata["correct"] |
| if orchestrator_mode and obs.metadata and "episode_score" in obs.metadata: |
| meta["episode_score"] = obs.metadata["episode_score"] |
| reward = obs.reward if reward_override is None else reward_override |
| if eval_mode and not orchestrator_mode: |
| |
| if obs.done and reward and reward > 0: |
| reward = 0.0 |
| elif not obs.done: |
| reward = 0.0 |
| return obs.model_copy( |
| update={ |
| "metadata": meta, |
| "reward": reward, |
| } |
| ) |
|
|
|
|
| def strip_legacy_answer_leaks( |
| inspect_meta: Dict[str, Any], *, eval_mode: bool |
| ) -> Dict[str, Any]: |
| if not eval_mode: |
| return inspect_meta |
| out = dict(inspect_meta) |
| out.pop("static_top_genes", None) |
| out.pop("static_top_pathways", None) |
| return out |
|
|
