feat(harness): batching.py — FFD + round-robin mini-batch (#10 算法保真)
This commit is contained in:
@@ -0,0 +1,242 @@
|
||||
"""混合 mini-batch 切分：大类打散、小类整锁，供 runner 每 step 处理一个 batch。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import math
|
||||
import random
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from core.types import GeneratedQuestion
|
||||
|
||||
|
||||
def build_batches(
|
||||
items: list[GeneratedQuestion],
|
||||
correctness: dict[str, bool],
|
||||
batch_size: int,
|
||||
min_class_per_batch: int,
|
||||
seed: int,
|
||||
correct_ratio: float = 0.0,
|
||||
) -> tuple[list[list[GeneratedQuestion]], int]:
|
||||
"""把诊断池里的题目切成多个混合 mini-batch。
|
||||
|
||||
当 ``correct_ratio > 0`` 时，按题型为每组错题配比一定数量的正确题，使 batch
|
||||
包含正误混合样本（"动量"机制）；``correct_ratio <= 0`` 时退化为纯错题模式。
|
||||
|
||||
参数:
|
||||
items: 候选题目全集。
|
||||
correctness: question_id -> 基线是否答对。
|
||||
batch_size: 单个 batch 的样本数上限（> 0）。
|
||||
min_class_per_batch: 小类判定阈值——题目数 ≤ 此值的题型整组锁进单一
|
||||
batch（> 0）。
|
||||
seed: 随机种子，保证相同输入产出完全一致的切分。
|
||||
correct_ratio: 正确题占比（0.0 ~ 1.0）。0.0 = 纯错题；0.5 = 错题:正确题 = 1:1。
|
||||
返回:
|
||||
(非空 mini-batch 列表, selected_count)；无错题时返回 ([], 0)。
|
||||
selected_count 是所有 batch 中题目总数。
|
||||
异常:
|
||||
ValueError: batch_size 或 min_class_per_batch < 1, 或
|
||||
min_class_per_batch >= batch_size（破坏小类整组装箱不超容的前提）。
|
||||
关键实现细节:
|
||||
装箱顺序为「先小类后大类」。小类整组用 first-fit-decreasing 装箱：按组大小
|
||||
降序处理（同大小再按 task_type 排序保证确定性），每组放进第一个剩余容量足够
|
||||
的 batch；若现有 batch 都装不下就新开一个空 batch——因小类组大小
|
||||
≤ min_class_per_batch < batch_size，新空 batch 必能容纳，故小类装箱永不抛
|
||||
ValueError，且保证整组不拆。再把大类样本（seed 确定性 shuffle 后）round-robin
|
||||
分发到所有现存 batch 填充剩余容量。这样小类聚集于单 batch、大类散布多 batch
|
||||
且与小类共箱，自然产生多类混合 batch（纯类切片会被 multiclass 断言拒绝）。
|
||||
nb = ceil(总题数/batch_size) 是初始 batch 数下界估计而非硬上限：小类装箱可能
|
||||
新开 bin 使实际 batch 数超过 nb。每次新开 bin 都意味着总容量随之增加，故总容量
|
||||
恒 ≥ 总题数，大类 round-robin 跳过满箱后仍能放下全部样本，不会违反 batch_size
|
||||
上限。题型按名称排序处理以保证跨运行确定性，不依赖 dict 遍历顺序。
|
||||
"""
|
||||
_validate_params(batch_size, min_class_per_batch)
|
||||
|
||||
rng = random.Random(seed)
|
||||
grouped = _select_mixed_by_task_type(items, correctness, correct_ratio, rng)
|
||||
total = sum(len(g) for g in grouped.values())
|
||||
if total == 0:
|
||||
return [], 0
|
||||
|
||||
nb = max(1, math.ceil(total / batch_size))
|
||||
batches: list[list[GeneratedQuestion]] = [[] for _ in range(nb)]
|
||||
|
||||
small, large = _split_by_size(grouped, min_class_per_batch)
|
||||
for group in _small_groups_decreasing(small):
|
||||
_pack_small_class(batches, group, batch_size)
|
||||
_distribute_large_classes(batches, large, batch_size, rng)
|
||||
|
||||
result = [b for b in batches if b]
|
||||
selected_count = sum(len(b) for b in result)
|
||||
return result, selected_count
|
||||
|
||||
|
||||
def _validate_params(batch_size: int, min_class_per_batch: int) -> None:
|
||||
"""校验切分参数，非法值直接报错而非用默认值掩盖。
|
||||
|
||||
除各自 >= 1 外，强制 min_class_per_batch < batch_size：小类组大小 ≤
|
||||
min_class_per_batch，唯有此前提成立才能保证小类整组放入单一 batch 而不超容；否则
|
||||
_pack_small_class 新开的 bin 会装入超 batch_size 的整组，静默违反容量合约。此约束
|
||||
与 config._validate_minibatch 一致，是 build_batches 对自身前提的防御性自校验（P5）。
|
||||
"""
|
||||
if batch_size < 1:
|
||||
raise ValueError(f"batch_size 必须 >= 1, 实为 {batch_size}")
|
||||
if min_class_per_batch < 1:
|
||||
raise ValueError(f"min_class_per_batch 必须 >= 1, 实为 {min_class_per_batch}")
|
||||
if min_class_per_batch >= batch_size:
|
||||
raise ValueError(
|
||||
f"min_class_per_batch 必须严格 < batch_size, 否则无法保证小类整组放入单一 "
|
||||
f"batch 不超容; 实为 min_class_per_batch={min_class_per_batch}, "
|
||||
f"batch_size={batch_size}"
|
||||
)
|
||||
|
||||
|
||||
def _split_by_size(
|
||||
grouped: dict[str, list[GeneratedQuestion]],
|
||||
min_class_per_batch: int,
|
||||
) -> tuple[dict[str, list[GeneratedQuestion]], dict[str, list[GeneratedQuestion]]]:
|
||||
"""按错题数把题型分为小类（≤ 阈值）与大类（> 阈值）两组。"""
|
||||
small = {t: g for t, g in grouped.items() if len(g) <= min_class_per_batch}
|
||||
large = {t: g for t, g in grouped.items() if len(g) > min_class_per_batch}
|
||||
return small, large
|
||||
|
||||
|
||||
def _select_mixed_by_task_type(
|
||||
items: list[GeneratedQuestion],
|
||||
correctness: dict[str, bool],
|
||||
correct_ratio: float,
|
||||
rng: random.Random,
|
||||
) -> dict[str, list[GeneratedQuestion]]:
|
||||
"""按题型分组，为每组错题按比例采样正确题混入。
|
||||
|
||||
只对有错题的题型做混合——无错题的题型不进 batch，即使有正确题。
|
||||
``correct_ratio <= 0`` 时退化为纯错题模式（向后兼容）。
|
||||
|
||||
参数:
|
||||
items: 候选题目全集。
|
||||
correctness: question_id -> 基线是否答对。
|
||||
correct_ratio: 正确题占比（0.0 ~ 1.0）。
|
||||
rng: 随机数发生器，用于采样正确题。
|
||||
返回:
|
||||
task_type -> 该题型的混合题目列表（错题全部 + 按比例采样的正确题）。
|
||||
"""
|
||||
errors_by_type: dict[str, list[GeneratedQuestion]] = {}
|
||||
correct_by_type: dict[str, list[GeneratedQuestion]] = {}
|
||||
for q in items:
|
||||
qid = q.question_id
|
||||
if correctness.get(qid) is False:
|
||||
errors_by_type.setdefault(q.task_type, []).append(q)
|
||||
elif correctness.get(qid, False):
|
||||
correct_by_type.setdefault(q.task_type, []).append(q)
|
||||
|
||||
if correct_ratio <= 0:
|
||||
return errors_by_type
|
||||
|
||||
# 为每个有错题的 task_type 混入正确题
|
||||
grouped: dict[str, list[GeneratedQuestion]] = {}
|
||||
for task_type in sorted(errors_by_type):
|
||||
errs = errors_by_type[task_type]
|
||||
n_correct = round(len(errs) * correct_ratio / (1 - correct_ratio))
|
||||
available = correct_by_type.get(task_type, [])
|
||||
sampled = (
|
||||
list(available)
|
||||
if len(available) <= n_correct
|
||||
else rng.sample(available, n_correct)
|
||||
)
|
||||
grouped[task_type] = errs + sampled
|
||||
|
||||
return grouped
|
||||
|
||||
|
||||
def _small_groups_decreasing(
|
||||
small: dict[str, list[GeneratedQuestion]],
|
||||
) -> list[list[GeneratedQuestion]]:
|
||||
"""按组大小降序、同大小按 task_type 升序排出小类组（first-fit-decreasing 顺序）。
|
||||
|
||||
参数:
|
||||
small: task_type -> 小类错题列表。
|
||||
返回:
|
||||
排好序的小类组列表；降序处理可降低碎片，确定性 tie-break 保证跨运行一致。
|
||||
"""
|
||||
return [small[t] for t in sorted(small, key=lambda t: (-len(small[t]), t))]
|
||||
|
||||
|
||||
def _pack_small_class(
|
||||
batches: list[list[GeneratedQuestion]],
|
||||
group: list[GeneratedQuestion],
|
||||
batch_size: int,
|
||||
) -> None:
|
||||
"""用 first-fit 把一个小类整组放入首个容得下的 batch，装不下则新开 bin（就地修改）。
|
||||
|
||||
因小类组大小 ≤ min_class_per_batch < batch_size，新开的空 batch 必能容纳整组，
|
||||
故此函数永不抛 ValueError，且整组不拆。
|
||||
|
||||
参数:
|
||||
batches: 当前各 batch（就地追加，必要时 append 新空 batch）。
|
||||
group: 待锁定的小类错题（整组不拆）。
|
||||
batch_size: 单 batch 容量上限。
|
||||
"""
|
||||
for b in batches:
|
||||
if len(b) + len(group) <= batch_size:
|
||||
b.extend(group)
|
||||
return
|
||||
batches.append(list(group))
|
||||
|
||||
|
||||
def _distribute_large_classes(
|
||||
batches: list[list[GeneratedQuestion]],
|
||||
large: dict[str, list[GeneratedQuestion]],
|
||||
batch_size: int,
|
||||
rng: random.Random,
|
||||
) -> None:
|
||||
"""将各大类样本 shuffle 后 round-robin 分发到所有现存 batch（就地修改）。
|
||||
|
||||
参数:
|
||||
batches: 当前各 batch（含小类装箱可能新开的 bin，就地追加）。
|
||||
large: task_type -> 大类错题列表。
|
||||
batch_size: 单 batch 容量上限。
|
||||
rng: 复用的随机数发生器，保证 shuffle 确定性。
|
||||
异常:
|
||||
ValueError: 所有 batch 均满仍有样本未放置（总容量估算异常，合法输入不可达）。
|
||||
关键实现细节:
|
||||
轮转范围是「所有现存 batch」而非固定 nb 个——小类装箱新开的 bin 也参与分发。
|
||||
总容量 = 现存 batch 数 × batch_size，每次新开 bin 都同步抬高总容量，故总容量恒
|
||||
≥ 总错题数，防御性 ValueError 在合法输入下不可达。全局指针在所有大类样本间持续
|
||||
轮转（不为每类重置），满箱即跳过，使大类充分散布并与已锁定的小类共箱。题型按名称
|
||||
排序以保证分发顺序确定。
|
||||
"""
|
||||
nb = len(batches)
|
||||
pointer = 0
|
||||
for task_type in sorted(large):
|
||||
group = list(large[task_type])
|
||||
rng.shuffle(group)
|
||||
for q in group:
|
||||
pointer = _place_round_robin(batches, q, pointer, batch_size, nb)
|
||||
|
||||
|
||||
def _place_round_robin(
|
||||
batches: list[list[GeneratedQuestion]],
|
||||
q: GeneratedQuestion,
|
||||
pointer: int,
|
||||
batch_size: int,
|
||||
nb: int,
|
||||
) -> int:
|
||||
"""从 pointer 起找第一个未满 batch 放入 q，返回下一次起始指针。
|
||||
|
||||
参数:
|
||||
batches: 当前各 batch（就地追加）。
|
||||
q: 待放置的样本。
|
||||
pointer: 本次轮转起始 batch 下标。
|
||||
batch_size: 单 batch 容量上限。
|
||||
nb: batch 总数。
|
||||
返回:
|
||||
下一次轮转的起始指针（已前移一位）。
|
||||
异常:
|
||||
ValueError: 扫描一轮所有 batch 均满（总容量估算异常）。
|
||||
"""
|
||||
for offset in range(nb):
|
||||
idx = (pointer + offset) % nb
|
||||
if len(batches[idx]) < batch_size:
|
||||
batches[idx].append(q)
|
||||
return (idx + 1) % nb
|
||||
raise ValueError("所有 batch 均满仍有样本待放置, 总容量估算异常")
|
||||
@@ -0,0 +1,267 @@
|
||||
"""app/harness/batching.py 的单元测试。
|
||||
|
||||
覆盖 FFD + round-robin mini-batch 构建的核心场景：
|
||||
确定性、小类不拆、大类 round-robin、正确率混合、空错题、参数校验、
|
||||
correctness False vs None 精确匹配。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import pytest
|
||||
|
||||
from core.types import GeneratedQuestion
|
||||
from app.harness.batching import (
|
||||
build_batches,
|
||||
_validate_params,
|
||||
_select_mixed_by_task_type,
|
||||
)
|
||||
|
||||
import random
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 辅助构造
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _make_q(
|
||||
qid: str,
|
||||
task_type: str = "default",
|
||||
video_id: str = "v1",
|
||||
) -> GeneratedQuestion:
|
||||
"""构造最小 GeneratedQuestion 用于测试。"""
|
||||
return GeneratedQuestion(
|
||||
question_id=qid,
|
||||
video_id=video_id,
|
||||
task_type=task_type,
|
||||
question=f"question_{qid}",
|
||||
options=("A. a", "B. b", "C. c", "D. d"),
|
||||
answer="A",
|
||||
source_nodes=("n1",),
|
||||
difficulty="medium",
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# test_build_batches_deterministic
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestBuildBatchesDeterministic:
|
||||
"""相同输入 + 相同 seed 产出完全一致的切分。"""
|
||||
|
||||
def test_same_seed_same_result(self) -> None:
|
||||
items = [_make_q(f"q{i}", task_type=f"type_{i % 3}") for i in range(20)]
|
||||
correctness = {f"q{i}": False for i in range(20)}
|
||||
r1 = build_batches(items, correctness, batch_size=5, min_class_per_batch=2, seed=42)
|
||||
r2 = build_batches(items, correctness, batch_size=5, min_class_per_batch=2, seed=42)
|
||||
assert r1 == r2
|
||||
|
||||
def test_different_seed_may_differ(self) -> None:
|
||||
"""不同 seed 结果应不同（极大概率，用大量样本保证）。"""
|
||||
items = [_make_q(f"q{i}", task_type=f"type_{i % 5}") for i in range(50)]
|
||||
correctness = {f"q{i}": False for i in range(50)}
|
||||
r1 = build_batches(items, correctness, batch_size=10, min_class_per_batch=3, seed=1)
|
||||
r2 = build_batches(items, correctness, batch_size=10, min_class_per_batch=3, seed=99)
|
||||
# 至少 batch 内容不同（不比较结构，只比较 selected_count 一致性）
|
||||
assert r1[1] == r2[1] # 总题数一致
|
||||
# 但 batch 内部排列几乎必然不同
|
||||
flat1 = [q.question_id for b in r1[0] for q in b]
|
||||
flat2 = [q.question_id for b in r2[0] for q in b]
|
||||
assert flat1 != flat2
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# test_small_class_not_split
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestSmallClassNotSplit:
|
||||
"""小类（≤ min_class_per_batch）整组不拆，锁在同一 batch。"""
|
||||
|
||||
def test_small_group_stays_together(self) -> None:
|
||||
# 2 道题属于 small_type（≤ min_class=3），应在同一 batch
|
||||
items = [
|
||||
_make_q("s1", task_type="small_type"),
|
||||
_make_q("s2", task_type="small_type"),
|
||||
# 大类 8 道题
|
||||
*[_make_q(f"big{i}", task_type="big_type") for i in range(8)],
|
||||
]
|
||||
correctness = {q.question_id: False for q in items}
|
||||
batches, count = build_batches(
|
||||
items, correctness, batch_size=5, min_class_per_batch=3, seed=0
|
||||
)
|
||||
assert count == 10
|
||||
# 找到包含 small_type 的 batch
|
||||
small_batch = [
|
||||
b for b in batches
|
||||
if any(q.task_type == "small_type" for q in b)
|
||||
]
|
||||
assert len(small_batch) == 1 # 整组在同一个 batch
|
||||
small_ids = {q.question_id for q in small_batch[0] if q.task_type == "small_type"}
|
||||
assert small_ids == {"s1", "s2"}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# test_large_class_round_robin
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestLargeClassRoundRobin:
|
||||
"""大类样本 round-robin 散布到多个 batch，不集中于单一 batch。"""
|
||||
|
||||
def test_large_group_distributed(self) -> None:
|
||||
# 12 道大类题，batch_size=4，min_class=2 → 大类 > 2 → round-robin
|
||||
items = [_make_q(f"q{i}", task_type="large_type") for i in range(12)]
|
||||
correctness = {q.question_id: False for q in items}
|
||||
batches, count = build_batches(
|
||||
items, correctness, batch_size=4, min_class_per_batch=2, seed=7
|
||||
)
|
||||
assert count == 12
|
||||
assert len(batches) >= 3 # ceil(12/4) = 3
|
||||
# 每个 batch 不超过 batch_size
|
||||
for b in batches:
|
||||
assert len(b) <= 4
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# test_correct_ratio_mixing
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestCorrectRatioMixing:
|
||||
"""correct_ratio > 0 时混入正确题。"""
|
||||
|
||||
def test_mixed_includes_correct(self) -> None:
|
||||
items = [
|
||||
_make_q("e1", task_type="t1"),
|
||||
_make_q("e2", task_type="t1"),
|
||||
_make_q("c1", task_type="t1"),
|
||||
_make_q("c2", task_type="t1"),
|
||||
_make_q("c3", task_type="t1"),
|
||||
]
|
||||
correctness = {"e1": False, "e2": False, "c1": True, "c2": True, "c3": True}
|
||||
batches, count = build_batches(
|
||||
items, correctness, batch_size=10, min_class_per_batch=2, seed=0,
|
||||
correct_ratio=0.5,
|
||||
)
|
||||
# correct_ratio=0.5 → 错:正 = 1:1 → 2 错 + 2 正 = 4 题
|
||||
assert count == 4
|
||||
all_ids = {q.question_id for b in batches for q in b}
|
||||
assert {"e1", "e2"}.issubset(all_ids) # 错题全部
|
||||
correct_in = all_ids - {"e1", "e2"}
|
||||
assert len(correct_in) == 2 # 采样 2 个正确题
|
||||
assert correct_in.issubset({"c1", "c2", "c3"})
|
||||
|
||||
def test_ratio_zero_pure_errors(self) -> None:
|
||||
items = [
|
||||
_make_q("e1", task_type="t1"),
|
||||
_make_q("c1", task_type="t1"),
|
||||
]
|
||||
correctness = {"e1": False, "c1": True}
|
||||
batches, count = build_batches(
|
||||
items, correctness, batch_size=10, min_class_per_batch=2, seed=0,
|
||||
correct_ratio=0.0,
|
||||
)
|
||||
assert count == 1
|
||||
assert batches[0][0].question_id == "e1"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# test_no_wrong_answers_empty
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestNoWrongAnswersEmpty:
|
||||
"""无错题时返回空列表。"""
|
||||
|
||||
def test_all_correct_returns_empty(self) -> None:
|
||||
items = [_make_q(f"q{i}") for i in range(5)]
|
||||
correctness = {f"q{i}": True for i in range(5)}
|
||||
batches, count = build_batches(
|
||||
items, correctness, batch_size=3, min_class_per_batch=1, seed=0,
|
||||
)
|
||||
assert batches == []
|
||||
assert count == 0
|
||||
|
||||
def test_empty_items_returns_empty(self) -> None:
|
||||
batches, count = build_batches(
|
||||
[], {}, batch_size=3, min_class_per_batch=1, seed=0,
|
||||
)
|
||||
assert batches == []
|
||||
assert count == 0
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# test_validate_params_strict
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestValidateParamsStrict:
|
||||
"""参数校验：batch_size < 1、min_class < 1、min_class >= batch_size 都报错。"""
|
||||
|
||||
def test_batch_size_zero(self) -> None:
|
||||
with pytest.raises(ValueError, match="batch_size 必须 >= 1"):
|
||||
_validate_params(0, 1)
|
||||
|
||||
def test_batch_size_negative(self) -> None:
|
||||
with pytest.raises(ValueError, match="batch_size 必须 >= 1"):
|
||||
_validate_params(-1, 1)
|
||||
|
||||
def test_min_class_zero(self) -> None:
|
||||
with pytest.raises(ValueError, match="min_class_per_batch 必须 >= 1"):
|
||||
_validate_params(5, 0)
|
||||
|
||||
def test_min_class_equals_batch_size(self) -> None:
|
||||
with pytest.raises(ValueError, match="min_class_per_batch 必须严格 < batch_size"):
|
||||
_validate_params(5, 5)
|
||||
|
||||
def test_min_class_exceeds_batch_size(self) -> None:
|
||||
with pytest.raises(ValueError, match="min_class_per_batch 必须严格 < batch_size"):
|
||||
_validate_params(3, 5)
|
||||
|
||||
def test_valid_params_no_error(self) -> None:
|
||||
_validate_params(5, 3) # 不抛异常
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# test_correctness_false_vs_none
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestCorrectnessFalseVsNone:
|
||||
"""correctness.get(qid) is False 精确匹配：None（未知题）不算错题。"""
|
||||
|
||||
def test_none_excluded_from_errors(self) -> None:
|
||||
items = [
|
||||
_make_q("wrong", task_type="t1"),
|
||||
_make_q("right", task_type="t1"),
|
||||
_make_q("unknown", task_type="t1"),
|
||||
]
|
||||
# wrong=False（错题），right=True（正确题），unknown 不在 correctness（None）
|
||||
correctness: dict[str, bool] = {"wrong": False, "right": True}
|
||||
batches, count = build_batches(
|
||||
items, correctness, batch_size=10, min_class_per_batch=2, seed=0,
|
||||
correct_ratio=0.0,
|
||||
)
|
||||
# 仅 wrong 进入 batch，unknown 不算错题
|
||||
assert count == 1
|
||||
assert batches[0][0].question_id == "wrong"
|
||||
|
||||
def test_explicit_false_only(self) -> None:
|
||||
"""直接测试 _select_mixed_by_task_type 内部逻辑。"""
|
||||
items = [
|
||||
_make_q("f1", task_type="t1"),
|
||||
_make_q("n1", task_type="t1"), # None（未知）
|
||||
_make_q("t1", task_type="t1"), # True（正确）
|
||||
]
|
||||
correctness: dict[str, bool] = {"f1": False, "t1": True}
|
||||
rng = random.Random(0)
|
||||
result = _select_mixed_by_task_type(items, correctness, 0.0, rng)
|
||||
assert "t1" in result
|
||||
assert len(result["t1"]) == 1
|
||||
assert result["t1"][0].question_id == "f1"
|
||||
|
||||
def test_none_not_treated_as_correct(self) -> None:
|
||||
"""None（未知）不进正确组，不被 correct_ratio 采样。"""
|
||||
items = [
|
||||
_make_q("err", task_type="t1"),
|
||||
_make_q("unk", task_type="t1"),
|
||||
]
|
||||
correctness: dict[str, bool] = {"err": False}
|
||||
rng = random.Random(0)
|
||||
result = _select_mixed_by_task_type(items, correctness, 0.5, rng)
|
||||
# 只有 err 一题错题，unk 不在 correctness 中 → get 返回 None → 不进 correct 组
|
||||
assert len(result["t1"]) == 1 # 只有错题，无正确题可混入
|
||||
Reference in New Issue
Block a user