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5dd1bb4 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 | """Unit tests for evaluation package random policy and evaluate()."""
import json
import sqlite3
import pytest
from sql_env.evaluation import RandomPolicy, evaluate
from sql_env.models import SQLAction, SQLObservation
from sql_env.server.sql_environment import SQLEnvironment
from sql_env.server.test_sql_env import MockTokenizer
def _build_sql_environment(tmp_path, *, db_id: str) -> SQLEnvironment:
db_root = tmp_path / "databases"
db_dir = db_root / db_id
db_dir.mkdir(parents=True)
db_path = db_dir / f"{db_id}.sqlite"
connection = sqlite3.connect(db_path)
cursor = connection.cursor()
cursor.execute(
"CREATE TABLE employees (id INTEGER PRIMARY KEY, name TEXT, dept TEXT)"
)
cursor.executemany(
"INSERT INTO employees (id, name, dept) VALUES (?, ?, ?)",
[
(1, "Alice", "engineering"),
(2, "Bob", "engineering"),
(3, "Cara", "sales"),
],
)
connection.commit()
connection.close()
questions_path = tmp_path / "questions.json"
questions_path.write_text(
json.dumps(
[
{
"question": "How many employees are there?",
"db_id": db_id,
"query": "SELECT COUNT(*) FROM employees",
}
]
),
encoding="utf-8",
)
return SQLEnvironment(
questions_path=str(questions_path),
db_dir=str(db_root),
tokenizer=MockTokenizer(),
)
def _build_observation(*, budget_remaining: int, result: str = "") -> SQLObservation:
return SQLObservation(
question="How many rows?",
schema_info="Available tables:\n- employees\n- departments",
result=result,
error="",
step_count=0,
budget_remaining=budget_remaining,
action_history=[],
done=False,
reward=None,
)
def _terminal_observation(*, reward: float) -> SQLObservation:
return SQLObservation(
question="How many rows?",
schema_info="Available tables:\n- employees\n- departments",
result="",
error="",
step_count=1,
budget_remaining=0,
action_history=[],
done=True,
reward=reward,
)
class _FixedPolicy:
def select_action(self, observation: SQLObservation) -> SQLAction:
return SQLAction(action_type="QUERY", argument="SELECT 1")
class _RaisingPolicy:
def __init__(self, fail_on_episode: int) -> None:
self._fail_on_episode = fail_on_episode
self._episode_index = -1
def select_action(self, observation: SQLObservation) -> SQLAction:
if observation.step_count == 0:
self._episode_index += 1
if self._episode_index == self._fail_on_episode:
raise RuntimeError("policy failed")
return SQLAction(action_type="QUERY", argument="SELECT 1")
class _SeedTrackingEnv:
def __init__(self, rewards: list[float]) -> None:
self._rewards = rewards
self._episode_index = -1
self.reset_seeds: list[int | None] = []
def reset(self, *, seed: int | None = None) -> SQLObservation:
self.reset_seeds.append(seed)
self._episode_index += 1
return _build_observation(budget_remaining=2)
def step(self, action: SQLAction) -> SQLObservation:
del action
reward = self._rewards[self._episode_index]
return _terminal_observation(reward=reward)
class _FlakyEnv(_SeedTrackingEnv):
def __init__(self, rewards: list[float], fail_on_episode: int) -> None:
super().__init__(rewards)
self._fail_on_episode = fail_on_episode
def step(self, action: SQLAction) -> SQLObservation:
if self._episode_index == self._fail_on_episode:
raise RuntimeError("step failed")
return super().step(action)
def test_random_policy_explores_when_budget_gt_one() -> None:
policy = RandomPolicy(seed=42)
observation = _build_observation(budget_remaining=10)
action = policy.select_action(observation)
assert action.action_type in {"DESCRIBE", "SAMPLE", "QUERY"}
def test_random_policy_answers_when_budget_eq_one() -> None:
policy = RandomPolicy(seed=42)
observation = _build_observation(budget_remaining=1)
action = policy.select_action(observation)
assert action.action_type == "ANSWER"
def test_random_policy_returns_sql_action() -> None:
policy = RandomPolicy(seed=7)
observation = _build_observation(budget_remaining=10)
action = policy.select_action(observation)
assert isinstance(action, SQLAction)
def test_random_policy_deterministic_with_seed() -> None:
observation = _build_observation(budget_remaining=10)
first = RandomPolicy(seed=123)
second = RandomPolicy(seed=123)
first_actions = [first.select_action(observation) for _ in range(25)]
second_actions = [second.select_action(observation) for _ in range(25)]
assert first_actions == second_actions
def test_random_policy_explores_all_action_types() -> None:
policy = RandomPolicy(seed=1)
observation = _build_observation(budget_remaining=10)
action_types = {policy.select_action(observation).action_type for _ in range(200)}
assert action_types == {"DESCRIBE", "SAMPLE", "QUERY"}
def test_random_policy_uses_result_rows_for_answer_candidates() -> None:
policy = RandomPolicy(seed=0)
observation = _build_observation(
budget_remaining=1,
result="1. engineering | 25\n2. sales | 10",
)
action = policy.select_action(observation)
assert action.action_type == "ANSWER"
assert action.argument in {
"engineering",
"25",
"sales",
"10",
"engineering | 25",
"sales | 10",
}
def test_evaluate_happy_path() -> None:
env = _SeedTrackingEnv([1.0, 0.0, 1.0])
result = evaluate(env, _FixedPolicy(), n_episodes=3)
assert result.n_episodes == 3
assert result.n_completed == 3
assert len(result.episodes) == 3
assert result.success_rate == 2 / 3
assert result.avg_reward == 2 / 3
assert result.avg_steps == 1.0
def test_evaluate_zero_episodes_returns_zero_values() -> None:
env = _SeedTrackingEnv([])
result = evaluate(env, _FixedPolicy(), n_episodes=0)
assert result == result.__class__(
success_rate=0.0,
avg_reward=0.0,
avg_steps=0.0,
n_episodes=0,
n_completed=0,
episodes=[],
)
assert env.reset_seeds == []
def test_evaluate_negative_episodes_raises() -> None:
env = _SeedTrackingEnv([])
try:
evaluate(env, _FixedPolicy(), n_episodes=-1)
except ValueError as exc:
assert str(exc) == "n_episodes must be >= 0"
else:
raise AssertionError("Expected ValueError for negative n_episodes")
def test_evaluate_uses_seed_plus_episode_index() -> None:
env = _SeedTrackingEnv([1.0, 1.0, 1.0])
evaluate(env, _FixedPolicy(), n_episodes=3, seed=100)
assert env.reset_seeds == [100, 101, 102]
def test_evaluate_records_episode_errors_and_continues() -> None:
env = _FlakyEnv([1.0, 1.0, 1.0], fail_on_episode=1)
result = evaluate(env, _FixedPolicy(), n_episodes=3)
assert result.n_episodes == 3
assert len(result.episodes) == 3
assert result.n_completed == 2
assert result.episodes[1].error == "step failed"
assert result.episodes[2].error is None
def test_evaluate_averages_exclude_failed_episodes() -> None:
env = _FlakyEnv([1.0, 0.0, 0.0], fail_on_episode=1)
result = evaluate(env, _FixedPolicy(), n_episodes=3)
assert result.n_completed == 2
assert result.avg_reward == 0.5
assert result.avg_steps == 1.0
assert result.success_rate == 0.5
def test_evaluate_policy_exception_recorded() -> None:
env = _SeedTrackingEnv([1.0, 1.0, 1.0])
result = evaluate(env, _RaisingPolicy(fail_on_episode=1), n_episodes=3)
assert result.n_completed == 2
assert result.episodes[1].error == "policy failed"
def test_evaluate_progress_callback_receives_episode_progress() -> None:
env = _SeedTrackingEnv([1.0, 1.0, 1.0])
calls: list[tuple[int, int]] = []
evaluate(
env,
_FixedPolicy(),
n_episodes=3,
progress_callback=lambda current, total: calls.append((current, total)),
)
assert calls == [(1, 3), (2, 3), (3, 3)]
def test_evaluate_integration_with_sql_environment(tmp_path) -> None:
env = _build_sql_environment(tmp_path, db_id="integration_eval")
result = evaluate(env, RandomPolicy(seed=42), n_episodes=10, seed=0)
assert result.n_episodes == 10
assert result.n_completed == 10
assert len(result.episodes) == 10
assert result.success_rate == sum(int(e.correct) for e in result.episodes) / 10
assert result.avg_reward == pytest.approx(
sum(e.total_reward for e in result.episodes) / 10
)
def test_evaluate_integration_is_deterministic_with_seeds(tmp_path) -> None:
env_a = _build_sql_environment(tmp_path / "run_a", db_id="integration_eval")
env_b = _build_sql_environment(tmp_path / "run_b", db_id="integration_eval")
result_a = evaluate(env_a, RandomPolicy(seed=42), n_episodes=10, seed=0)
result_b = evaluate(env_b, RandomPolicy(seed=42), n_episodes=10, seed=0)
assert result_a == result_b
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