File size: 3,421 Bytes
d5b51d5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | """LES-1.0 helpers for LoopNet records."""
from __future__ import annotations
from typing import Any
from loopnet.constants import LES_CATEGORIES, LES_WEIGHTS
def clamp(value: float, low: float = 0.0, high: float = 1.0) -> float:
return max(low, min(high, value))
def composite_les(categories: dict[str, float]) -> float:
return sum(LES_WEIGHTS[cat] * clamp(categories[cat]) for cat in LES_CATEGORIES)
def les_from_trajectory(
trajectory: list[dict[str, Any]],
*,
goal_target: float,
outcome: str,
failure_mode: str | None = None,
max_iterations_budget: int,
) -> dict[str, Any]:
"""Derive heuristic LES category scores from trajectory telemetry."""
goal_final = trajectory[-1]["goal_score"]
goal_0 = trajectory[0]["goal_score"]
iteration_count = len(trajectory)
cost_total = sum(step["cost_usd"] for step in trajectory)
latencies = [step["latency_seconds"] for step in trajectory]
median_latency = sorted(latencies)[len(latencies) // 2]
regressions = sum(
1
for i in range(1, len(trajectory))
if trajectory[i]["goal_score"] < trajectory[i - 1]["goal_score"]
)
safety_events = sum(step.get("safety_events", 0) for step in trajectory)
human_steps = sum(1 for step in trajectory if step.get("human_intervention"))
effectiveness = clamp(goal_final / max(goal_target, 0.01))
if outcome == "failure" and goal_final < goal_target:
effectiveness *= 0.6
speed = clamp(1.0 / (1.0 + median_latency / 30.0))
if any(lat > 3 * median_latency for lat in latencies):
speed *= 0.85
delta_g = goal_final - goal_0
cost_eff = (delta_g / cost_total) if cost_total > 0 and delta_g > 0 else 0.0
cost_score = clamp(min(cost_eff * 2.0, 1.0))
robustness = clamp(0.75 - 0.05 * regressions)
scalability = clamp(0.55 + 0.05 * min(iteration_count, 6))
safety = 0.0 if failure_mode == "fail.safety_bypass" else clamp(1.0 - min(safety_events * 0.2, 0.8))
adaptability = clamp(0.45 + 0.1 * (goal_final - goal_0))
autonomy = clamp(1.0 - min(human_steps / max(iteration_count, 1), 0.6))
categories = {
"effectiveness": round(effectiveness, 4),
"speed": round(speed, 4),
"cost": round(cost_score, 4),
"robustness": round(robustness, 4),
"scalability": round(scalability, 4),
"safety": round(safety, 4),
"adaptability": round(adaptability, 4),
"autonomy": round(autonomy, 4),
}
if iteration_count < 2 and outcome == "failure":
categories = {k: round(v * 0.5, 4) if k != "safety" else v for k, v in categories.items()}
les_normalized = round(composite_les(categories), 4)
return {
"les_normalized": les_normalized,
"les_display": round(les_normalized * 100, 1),
"categories": categories,
"partial": iteration_count < max_iterations_budget and outcome != "success",
}
def trajectory_diagnostics(trajectory: list[dict[str, Any]]) -> dict[str, int | float]:
regressions = sum(
1
for i in range(1, len(trajectory))
if trajectory[i]["goal_score"] < trajectory[i - 1]["goal_score"]
)
return {
"regression_count": regressions,
"iteration_count": len(trajectory),
"cost_total_usd": round(sum(step["cost_usd"] for step in trajectory), 4),
"goal_final": trajectory[-1]["goal_score"],
}
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