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bf16a34 56acd24 bf16a34 a81c92f 6c4cb39 56acd24 a81c92f | 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 | from __future__ import annotations
TASK_ID = "anomaly_detection_medium"
MAX_STEPS = 10
DIFFICULTY = "medium"
def get_task_config() -> dict:
return {
"id": TASK_ID,
"difficulty": DIFFICULTY,
"max_steps": MAX_STEPS,
"grader": "graders.grader_classification:MediumClassificationGrader",
"description": "MEDIUM — Faster ingestion, tighter metrics."
}
def setup_env(env) -> None:
try:
from server.fin_auditor_environment import hft_auditor
if hft_auditor is not None:
env.difficulty = hft_auditor.Difficulty.MEDIUM
env._MAX_EPISODE_STEPS = MAX_STEPS
except Exception as e:
print(f"[task_medium] Could not set difficulty: {e}")
def run_episode(env, agent_fn) -> dict:
"""Run a single MEDIUM anomaly-detection episode and return the graded result.
Called by the OpenEnv evaluator. agent_fn receives an observation and
returns a list of binary decisions (0 = valid, 1 = anomaly).
"""
setup_env(env)
total_reward = 0.0
steps_done = 0
try:
obs = env.reset()
for _ in range(MAX_STEPS):
decisions = agent_fn(obs)
from models import AuditorAction
action = AuditorAction(decisions=decisions)
obs = env.step(action)
total_reward += float(obs.reward) if obs.reward is not None else 0.0
steps_done += 1
if obs.done:
break
except Exception as exc:
print(f"[task_medium] run_episode error at step {steps_done}: {exc}")
# Always grade — even partial data yields a valid score via perfect_signal fallback
from graders.grader_classification import MediumClassificationGrader
grader = MediumClassificationGrader()
final_score = grader.grade(env)
return {
"task": TASK_ID,
"difficulty": DIFFICULTY,
"steps": steps_done,
"total_reward": round(total_reward, 4),
"score": round(final_score, 4),
"grader_breakdown": grader.last_breakdown,
} |