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from typing import Any
import numpy as np
from solarchain_eval.actions import sanitize_actual_action
from solarchain_eval.config import BenchmarkConfig
from .context import build_step_context
from .prompts import auditor_messages
from .schemas import (
ActionValues,
AuditorOutput,
PlannerOutput,
RiskAssessment,
approve_original_action,
validate_auditor_output,
)
class NoOpAuditor:
def __init__(self, config: BenchmarkConfig, audit_trigger: str = "event"):
self.config = config
self.audit_trigger = audit_trigger
self.last_valid = True
self.failure_count = 0
def should_audit(
self,
obs: np.ndarray,
proposed_action: np.ndarray,
previous_action: np.ndarray,
plan: PlannerOutput,
) -> bool:
return False
def audit(self, step_context: dict[str, Any]) -> AuditorOutput:
return approve_original_action(_action_from_context(step_context), self.config, reason="NoOpAuditor approved.")
class RuleAuditor:
def __init__(self, config: BenchmarkConfig, audit_trigger: str = "event"):
self.config = config
self.audit_trigger = audit_trigger
self.last_valid = True
self.failure_count = 0
def should_audit(
self,
obs: np.ndarray,
proposed_action: np.ndarray,
previous_action: np.ndarray,
plan: PlannerOutput,
) -> bool:
if self.audit_trigger == "always":
return True
return _event_triggered(obs, proposed_action, previous_action, plan)
def audit(self, step_context: dict[str, Any]) -> AuditorOutput:
obs = step_context["observation"]
action = _action_from_context(step_context)
plan = PlannerOutput.model_validate(step_context["plan"])
physics_risky = obs["violation_rate"] > plan.audit_policy.force_audit_if_violation_rate_above
liquidity_risky = obs["gap"] < plan.audit_policy.force_audit_if_gap_below
jitter_risky = step_context["action_jitter"] > plan.audit_policy.force_audit_if_action_jitter_above
revised = np.asarray(action, dtype=np.float32).copy()
if physics_risky:
revised[0] *= 0.85
revised[1] *= 0.90
revised[2] = max(revised[2], min(0.12, self.config.market.max_burn_rate))
if liquidity_risky:
revised[1] = max(revised[1], min(0.80, self.config.market.max_liquidity_ratio))
revised[0] *= 0.90
if jitter_risky:
previous = _previous_action_from_context(step_context)
revised = 0.60 * previous + 0.40 * revised
revised = sanitize_actual_action(revised, self.config)
changed = bool(np.linalg.norm(revised - action, ord=1) > 1e-6)
return AuditorOutput(
decision="revise" if changed else "approve",
final_action=ActionValues(
reward_ratio=float(revised[0]),
liquidity_ratio=float(revised[1]),
burn_rate=float(revised[2]),
),
risk_assessment=RiskAssessment(
physics_risk="high" if physics_risky else "low",
liquidity_risk="high" if liquidity_risky else "low",
jitter_risk="high" if jitter_risky else "low",
fairness_risk="unknown",
),
reason="Rule auditor revised risky action." if changed else "Rule auditor approved bounded action.",
)
class LLMAuditor:
def __init__(self, llm_client: Any, config: BenchmarkConfig, audit_trigger: str = "event"):
self.llm_client = llm_client
self.config = config
self.audit_trigger = audit_trigger
self.last_valid = True
self.failure_count = 0
def should_audit(
self,
obs: np.ndarray,
proposed_action: np.ndarray,
previous_action: np.ndarray,
plan: PlannerOutput,
) -> bool:
if self.audit_trigger == "always":
return True
return _event_triggered(obs, proposed_action, previous_action, plan)
def audit(self, step_context: dict[str, Any]) -> AuditorOutput:
original = _action_from_context(step_context)
payload = self.llm_client.audit_structured(auditor_messages(step_context))
audit, valid, error = validate_auditor_output(payload, self.config, original)
self.last_valid = valid
if not valid:
self.failure_count += 1
self.last_valid = False
raise RuntimeError(f"LLM auditor structured output validation failed: {error}")
return audit
def should_audit_context(
auditor: NoOpAuditor | RuleAuditor | LLMAuditor,
obs: np.ndarray,
proposed_action: np.ndarray,
previous_action: np.ndarray,
plan: PlannerOutput,
) -> bool:
return auditor.should_audit(obs, proposed_action, previous_action, plan)
def build_audit_context(
obs: np.ndarray,
proposed_action: np.ndarray,
previous_action: np.ndarray,
plan: PlannerOutput,
info: dict[str, Any] | None = None,
) -> dict[str, Any]:
return build_step_context(obs, proposed_action, previous_action, plan, info)
def _event_triggered(
obs: np.ndarray,
proposed_action: np.ndarray,
previous_action: np.ndarray,
plan: PlannerOutput,
) -> bool:
arr = np.asarray(obs, dtype=np.float32)
action = np.asarray(proposed_action, dtype=np.float32)
previous = np.asarray(previous_action, dtype=np.float32)
policy = plan.audit_policy
return bool(
arr[8] > policy.force_audit_if_violation_rate_above
or arr[5] < policy.force_audit_if_gap_below
or np.linalg.norm(action - previous, ord=1) > policy.force_audit_if_action_jitter_above
or arr[9] > policy.force_audit_if_static_slippage_above
)
def hard_safety_triggered(obs: np.ndarray, plan: PlannerOutput) -> bool:
arr = np.asarray(obs, dtype=np.float32)
policy = plan.audit_policy
return bool(
arr[8] > policy.force_audit_if_violation_rate_above
or arr[5] < policy.force_audit_if_gap_below
or arr[9] > policy.force_audit_if_static_slippage_above
)
def _action_from_context(step_context: dict[str, Any]) -> np.ndarray:
action = step_context["proposed_action"]
return np.array([action["reward_ratio"], action["liquidity_ratio"], action["burn_rate"]], dtype=np.float32)
def _previous_action_from_context(step_context: dict[str, Any]) -> np.ndarray:
action = step_context["previous_action"]
return np.array([action["reward_ratio"], action["liquidity_ratio"], action["burn_rate"]], dtype=np.float32)
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