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846683d | 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 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 | from __future__ import annotations
import json
import time
from dataclasses import dataclass, asdict
from pathlib import Path
from statistics import mean
from baselines.heuristics import greedy_queue_policy
from workflow_twin.environment import WorkflowTwinEnv
from workflow_twin.memory import MemoryBoundedEnv
@dataclass
class EvalResult:
test_name: str
mode: str
episodes: int
avg_reward: float
success_rate: float
avg_sla_violations: float
avg_memory_used: float
avg_memory_budget: float
memory_compliance_rate: float
steps_per_sec: float
def run_control_test(mode: str, episodes: int = 100) -> EvalResult:
rewards = []
success_ratios = []
sla_violations = []
memory_used = []
memory_budget = []
compliance = []
total_steps = 0
start = time.perf_counter()
for i in range(episodes):
base_env = WorkflowTwinEnv(level=1, seed=100 + i)
env = MemoryBoundedEnv(base_env, memory_budget=100_000, bits=3, mode=mode)
obs = env.reset()
done = False
ep_reward = 0.0
steps = 0
last_info = {}
while not done and steps < 10:
action = greedy_queue_policy(obs)
obs, reward, done, info = env.step(action.model_dump())
ep_reward += reward
steps += 1
total_steps += 1
last_info = info
rewards.append(ep_reward)
state = base_env.state()
completed = state["queue"]["completed"]
waiting = state["queue"]["waiting"]
current = 1 if state["queue"]["current_ticket"] else 0
total = max(completed + waiting + current, 1)
success_ratios.append(completed / total)
sla_violations.append(last_info.get("sla_violations", 0))
memory_used.append(last_info.get("memory", {}).get("memory_used", 0))
memory_budget.append(last_info.get("memory", {}).get("memory_budget", 0))
compliance.append(memory_used[-1] <= memory_budget[-1])
elapsed = time.perf_counter() - start
return EvalResult(
test_name="control_no_memory_pressure",
mode=mode,
episodes=episodes,
avg_reward=round(mean(rewards), 4),
success_rate=round(mean(success_ratios), 4),
avg_sla_violations=round(mean(sla_violations), 4),
avg_memory_used=round(mean(memory_used), 2),
avg_memory_budget=round(mean(memory_budget), 2),
memory_compliance_rate=round(mean(1.0 if v else 0.0 for v in compliance), 4),
steps_per_sec=round(total_steps / max(elapsed, 1e-6), 2),
)
def run_memory_test(mode: str, episodes: int = 100) -> EvalResult:
rewards = []
success_ratios = []
sla_violations = []
memory_used = []
memory_budget = []
compliance = []
total_steps = 0
start = time.perf_counter()
for i in range(episodes):
base_env = WorkflowTwinEnv(level=5, seed=500 + i)
env = MemoryBoundedEnv(base_env, memory_budget=3500, bits=3, mode=mode)
obs = env.reset()
done = False
ep_reward = 0.0
steps = 0
last_info = {}
while not done and steps < 80:
action = greedy_queue_policy(obs)
obs, reward, done, info = env.step(action.model_dump())
ep_reward += reward
steps += 1
total_steps += 1
last_info = info
rewards.append(ep_reward)
state = base_env.state()
completed = state["queue"]["completed"]
waiting = state["queue"]["waiting"]
current = 1 if state["queue"]["current_ticket"] else 0
total = max(completed + waiting + current, 1)
success_ratios.append(completed / total)
sla_violations.append(last_info.get("sla_violations", 0))
memory_used.append(last_info.get("memory", {}).get("memory_used", 0))
memory_budget.append(last_info.get("memory", {}).get("memory_budget", 0))
compliance.append(memory_used[-1] <= memory_budget[-1])
elapsed = time.perf_counter() - start
return EvalResult(
test_name="critical_memory_constrained_long_horizon",
mode=mode,
episodes=episodes,
avg_reward=round(mean(rewards), 4),
success_rate=round(mean(success_ratios), 4),
avg_sla_violations=round(mean(sla_violations), 4),
avg_memory_used=round(mean(memory_used), 2),
avg_memory_budget=round(mean(memory_budget), 2),
memory_compliance_rate=round(mean(1.0 if v else 0.0 for v in compliance), 4),
steps_per_sec=round(total_steps / max(elapsed, 1e-6), 2),
)
def run_memory_budget_sweep(
budgets: list[int] | None = None,
episodes: int = 80,
) -> list[dict]:
if budgets is None:
budgets = [2000, 3000, 4000, 6000]
sweep_results: list[dict] = []
for budget in budgets:
for mode in ["baseline", "quant"]:
rewards = []
success_ratios = []
compliance = []
start = time.perf_counter()
total_steps = 0
for i in range(episodes):
base_env = WorkflowTwinEnv(level=5, seed=1200 + budget + i)
env = MemoryBoundedEnv(base_env, memory_budget=budget, bits=3, mode=mode)
obs = env.reset()
done = False
ep_reward = 0.0
last_info = {}
steps = 0
while not done and steps < 80:
action = greedy_queue_policy(obs)
obs, reward, done, info = env.step(action.model_dump())
ep_reward += reward
last_info = info
steps += 1
total_steps += 1
rewards.append(ep_reward)
state = base_env.state()
completed = state["queue"]["completed"]
waiting = state["queue"]["waiting"]
current = 1 if state["queue"]["current_ticket"] else 0
total = max(completed + waiting + current, 1)
success_ratios.append(completed / total)
mem_used = last_info.get("memory", {}).get("memory_used", 0)
mem_budget = last_info.get("memory", {}).get("memory_budget", budget)
compliance.append(mem_used <= mem_budget)
elapsed = time.perf_counter() - start
sweep_results.append(
{
"test_name": "memory_budget_sweep",
"budget": budget,
"mode": mode,
"episodes": episodes,
"avg_reward": round(mean(rewards), 4),
"success_rate": round(mean(success_ratios), 4),
"memory_compliance_rate": round(mean(1.0 if ok else 0.0 for ok in compliance), 4),
"steps_per_sec": round(total_steps / max(elapsed, 1e-6), 2),
}
)
return sweep_results
def export_compliance_figure(sweep_rows: list[dict], out_path: Path) -> None:
budgets = sorted({row["budget"] for row in sweep_rows})
baseline = {row["budget"]: row["memory_compliance_rate"] for row in sweep_rows if row["mode"] == "baseline"}
quant = {row["budget"]: row["memory_compliance_rate"] for row in sweep_rows if row["mode"] == "quant"}
width, height = 760, 440
left, right, top, bottom = 80, 40, 40, 70
plot_w = width - left - right
plot_h = height - top - bottom
min_b, max_b = min(budgets), max(budgets)
def x_pos(budget: int) -> float:
if max_b == min_b:
return left + plot_w / 2
return left + (budget - min_b) / (max_b - min_b) * plot_w
def y_pos(rate: float) -> float:
rate = max(0.0, min(1.0, rate))
return top + (1.0 - rate) * plot_h
def polyline(points: list[tuple[float, float]], color: str) -> str:
pts = " ".join(f"{x:.2f},{y:.2f}" for x, y in points)
circles = "\n".join(
f'<circle cx="{x:.2f}" cy="{y:.2f}" r="4" fill="{color}" />' for x, y in points
)
return (
f'<polyline fill="none" stroke="{color}" stroke-width="3" points="{pts}" />\n'
f"{circles}"
)
baseline_points = [(x_pos(b), y_pos(baseline.get(b, 0.0))) for b in budgets]
quant_points = [(x_pos(b), y_pos(quant.get(b, 0.0))) for b in budgets]
y_ticks = [0.0, 0.25, 0.5, 0.75, 1.0]
y_grid = "\n".join(
f'<line x1="{left}" y1="{y_pos(v):.2f}" x2="{left+plot_w}" y2="{y_pos(v):.2f}" '
f'stroke="#e5e7eb" stroke-width="1" />\n'
f'<text x="{left-10}" y="{y_pos(v)+5:.2f}" text-anchor="end" font-size="12" fill="#374151">{v:.2f}</text>'
for v in y_ticks
)
x_labels = "\n".join(
f'<text x="{x_pos(b):.2f}" y="{top+plot_h+24}" text-anchor="middle" font-size="12" fill="#374151">{b}</text>'
for b in budgets
)
svg = f'''<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}" viewBox="0 0 {width} {height}">
<rect x="0" y="0" width="{width}" height="{height}" fill="white"/>
<text x="{width/2}" y="24" text-anchor="middle" font-size="18" font-family="Arial" fill="#111827">Memory Budget vs Compliance Rate</text>
{y_grid}
<line x1="{left}" y1="{top}" x2="{left}" y2="{top+plot_h}" stroke="#6b7280" stroke-width="1.5" />
<line x1="{left}" y1="{top+plot_h}" x2="{left+plot_w}" y2="{top+plot_h}" stroke="#6b7280" stroke-width="1.5" />
{x_labels}
<text x="{left + plot_w/2}" y="{height-18}" text-anchor="middle" font-size="13" fill="#111827">Memory Budget</text>
<text x="18" y="{top + plot_h/2}" text-anchor="middle" font-size="13" fill="#111827" transform="rotate(-90,18,{top + plot_h/2})">Compliance Rate</text>
{polyline(baseline_points, '#ef4444')}
{polyline(quant_points, '#2563eb')}
<rect x="{width-190}" y="50" width="160" height="54" fill="#f9fafb" stroke="#d1d5db" />
<line x1="{width-176}" y1="68" x2="{width-146}" y2="68" stroke="#ef4444" stroke-width="3" />
<text x="{width-140}" y="72" font-size="12" fill="#111827">baseline</text>
<line x1="{width-176}" y1="88" x2="{width-146}" y2="88" stroke="#2563eb" stroke-width="3" />
<text x="{width-140}" y="92" font-size="12" fill="#111827">quant</text>
</svg>
'''
out_path.write_text(svg, encoding="utf-8")
def main() -> None:
results = []
for mode in ["baseline", "quant"]:
results.append(run_control_test(mode=mode))
for mode in ["baseline", "quant"]:
results.append(run_memory_test(mode=mode))
sweep_rows = run_memory_budget_sweep()
results.extend(sweep_rows)
figure_path = Path(__file__).resolve().parent / "figures" / "memory_budget_vs_compliance.svg"
export_compliance_figure(sweep_rows, figure_path)
payload = []
for row in results:
if isinstance(row, EvalResult):
payload.append(asdict(row))
else:
payload.append(row)
print(json.dumps(payload, indent=2))
if __name__ == "__main__":
main()
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