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acb327b | 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 | """
ECHO ULTIMATE — Main Gymnasium Environment.
Each episode = 1 question → 1 answer → 1 reward.
State includes running calibration metrics across all 7 domains.
"""
import logging
from typing import Any, Callable, Optional
import gymnasium as gym
import numpy as np
from gymnasium import spaces
from config import cfg
from env.parser import parse_response, format_prompt, ParseResult
from env.reward import compute_reward, RewardHistory, RewardBreakdown
from env.task_bank import TaskBank
logger = logging.getLogger(__name__)
_DOMAIN_INDEX = {d: i for i, d in enumerate(cfg.DOMAINS)}
class EchoEnv(gym.Env):
"""
ECHO ULTIMATE Gymnasium environment.
Observation: dict with task info + running calibration metrics.
Action: text string in <confidence>N</confidence><answer>X</answer> format.
Reward: weighted accuracy + Brier calibration + overconfidence penalties.
Each episode terminates after exactly one step.
"""
metadata = {"render_modes": ["human", "ansi"]}
def __init__(
self,
task_bank: Optional[TaskBank] = None,
reward_history: Optional[RewardHistory] = None,
phase: int = 1,
self_consistency: bool = False,
generate_fn: Optional[Callable[[str], str]] = None,
render_mode: Optional[str] = None,
) -> None:
super().__init__()
self.task_bank = task_bank or TaskBank()
self.task_bank.ensure_loaded()
self.reward_history = reward_history or RewardHistory()
self.phase = phase
self.self_consistency = self_consistency
self.generate_fn = generate_fn
self.render_mode = render_mode
self._current_task: Optional[dict] = None
self._last_result: Optional[RewardBreakdown] = None
self._last_parsed: Optional[ParseResult] = None
self._episode_step: int = 0
self._episode_reward: float = 0.0
# Gymnasium spaces (informational for text-based env)
self.action_space = spaces.Text(min_length=1, max_length=1024)
self.observation_space = spaces.Dict({
"task_id": spaces.Text(min_length=1, max_length=128),
"domain": spaces.Text(min_length=1, max_length=32),
"difficulty": spaces.Text(min_length=1, max_length=16),
"question": spaces.Text(min_length=1, max_length=4096),
"phase": spaces.Discrete(4),
"episode_step": spaces.Discrete(3),
"running_ece": spaces.Box(0, 1, shape=(1,), dtype=np.float32),
"running_accuracy": spaces.Box(0, 1, shape=(1,), dtype=np.float32),
"running_mean_confidence": spaces.Box(0, 100, shape=(1,), dtype=np.float32),
"domain_ece": spaces.Box(0, 1, shape=(len(cfg.DOMAINS),), dtype=np.float32),
})
# ── Gymnasium API ─────────────────────────────────────────────────────────
def reset(
self,
seed: Optional[int] = None,
options: Optional[dict] = None,
) -> tuple[dict, dict]:
super().reset(seed=seed)
task_id = (options or {}).get("task_id")
if task_id:
task = self.task_bank.get_task_by_id(task_id) or \
self.task_bank.get_batch(1, self.phase)[0]
elif (options or {}).get("adversarial"):
task = self.task_bank.get_adversarial_batch(1)[0]
else:
task = self.task_bank.get_batch(1, self.phase)[0]
self._current_task = task
self._episode_step = 0
self._episode_reward = 0.0
self._last_result = None
self._last_parsed = None
prompt = format_prompt(
task["question"], task["domain"], task["difficulty"],
show_difficulty=(self.phase == 1),
)
obs = self._build_obs()
info = {"task": task, "formatted_prompt": prompt}
return obs, info
def step(self, action: str) -> tuple[dict, float, bool, bool, dict]:
if self._current_task is None:
logger.warning("step() called before reset() — auto-resetting")
self.reset()
task = self._current_task
# Self-consistency check (demo mode only)
if self.self_consistency and self.generate_fn is not None:
from env.self_consistency import SelfConsistencyChecker
checker = SelfConsistencyChecker()
prompt = format_prompt(task["question"], task["domain"], task["difficulty"])
result = checker.check(prompt, self.generate_fn)
# Override confidence from consistency check
action = cfg.CONFIDENCE_FORMAT.format(
conf=result.final_confidence, ans=result.final_answer
)
parsed = parse_response(action)
rb = compute_reward(
confidence=parsed.confidence,
predicted=parsed.answer,
ground_truth=task["answer"],
aliases=task.get("answer_aliases", []),
domain=task["domain"],
)
self.reward_history.append(
confidence=parsed.confidence,
was_correct=rb.was_correct,
domain=task["domain"],
difficulty=task["difficulty"],
reward=rb.total,
is_abstention=parsed.is_abstention,
)
self._last_result = rb
self._last_parsed = parsed
self._episode_step = 1
self._episode_reward = rb.total
obs = self._build_obs()
info = {
"accuracy": rb.accuracy_score,
"brier_reward": rb.brier_reward_val,
"overconfidence_penalty": rb.overconfidence_penalty_val,
"underconfidence_penalty": rb.underconfidence_penalty_val,
"parsed_confidence": parsed.confidence,
"parsed_answer": parsed.answer,
"true_answer": task["answer"],
"was_correct": rb.was_correct,
"parse_success": parsed.parse_success,
"is_abstention": parsed.is_abstention,
"task_id": task["id"],
"domain": task["domain"],
"difficulty": task["difficulty"],
"breakdown": rb.breakdown_str,
}
if self.render_mode == "human":
self.render()
return obs, rb.total, True, False, info # terminated=True (single step)
def render(self) -> None:
if self._current_task is None:
print("[EchoEnv] No active episode.")
return
task = self._current_task
rb = self._last_result
p = self._last_parsed
snap = self.reward_history.get_training_snapshot(last_n=100)
icon = "✅" if (rb and rb.was_correct) else "❌"
conf = p.confidence if p else "—"
ans = p.answer[:40] if p else "—"
rew = f"{rb.total:+.3f}" if rb else "—"
ece = f"{snap['ece']:.3f}"
print(f"\n┌{'─'*37}┐")
print(f"│ {'ECHO Episode Summary':<35} │")
print(f"├{'─'*37}┤")
print(f"│ {'Domain:':<12} {task['domain']} ({task['difficulty']}){'':<10}│"[:40])
print(f"│ {'Q:':<5} {task['question'][:30]+'…':<32} │")
print(f"│ {'Confidence:':<12} {conf}%{'':<22}│"[:40])
print(f"│ {'Answer:':<12} {ans:<25} │"[:40])
print(f"│ {'Correct:':<12} {icon:<25} │"[:40])
print(f"│ {'Reward:':<12} {rew:<25} │"[:40])
print(f"│ {'ECE (100ep):':<12} {ece:<25} │"[:40])
print(f"└{'─'*37}┘")
# ── Metrics helpers ───────────────────────────────────────────────────────
def get_metrics(self, domain: Optional[str] = None):
return self.reward_history.get_calibration_report(domain=domain)
def set_phase(self, phase: int) -> None:
self.phase = max(1, min(3, phase))
def get_formatted_prompt(self) -> str:
if self._current_task is None:
return ""
t = self._current_task
return format_prompt(t["question"], t["domain"], t["difficulty"],
show_difficulty=(self.phase == 1))
# ── Internal ──────────────────────────────────────────────────────────────
def _build_obs(self) -> dict:
task = self._current_task or {}
snap = self.reward_history.get_training_snapshot(last_n=100)
profiles = self.reward_history.get_domain_profiles()
domain_ece = np.array(
[profiles.get(d).ece if profiles.get(d) and profiles[d].n_samples > 0 else 0.5
for d in cfg.DOMAINS],
dtype=np.float32,
)
return {
"task_id": task.get("id", ""),
"domain": task.get("domain", ""),
"difficulty": task.get("difficulty", ""),
"question": task.get("question", ""),
"phase": self.phase,
"episode_step": self._episode_step,
"running_ece": float(snap["ece"]),
"running_accuracy": float(snap["accuracy"]),
"running_mean_confidence": float(snap["mean_confidence"]),
"domain_ece": [float(x) for x in domain_ece],
}
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