130 lines
4.0 KiB
Python
130 lines
4.0 KiB
Python
from __future__ import annotations
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from datetime import datetime, timezone
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import pytest
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from app.features.personalized_reco.observability.builder import RecoMetaBuilder
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from app.features.personalized_reco.observability.utils import compute_empty_reason, compute_missing_fields
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from app.features.user_profile_scoring.types import ProfileAnswered, UserProfileV1_2, UserStageOneHot
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def _u(*, need: dict | None = None, context: dict | None = None, emotion_score=None, conf_u: float = 0.9) -> UserProfileV1_2:
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now = datetime.now(tz=timezone.utc)
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return UserProfileV1_2(
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profile_generated_at=now,
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profile_confidence=conf_u,
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profile_answered=ProfileAnswered(stage=True, emotion=True, context=True, need=True),
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stage=UserStageOneHot(unknown=1),
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emotion_score=emotion_score,
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context=context or {},
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need=need or {},
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)
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def test_compute_missing_fields() -> None:
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u1 = _u(need={}, context={}, emotion_score=None)
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m1 = compute_missing_fields(u1)
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assert m1.need is True
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assert m1.context is True
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assert m1.emotion is True
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u2 = _u(need={"x": 1}, context={"y": 1}, emotion_score=0.6)
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m2 = compute_missing_fields(u2)
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assert m2.need is False
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assert m2.context is False
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assert m2.emotion is False
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def test_compute_empty_reason_branches() -> None:
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assert (
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compute_empty_reason(
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served_k=1,
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candidate_pool_size_raw=0,
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candidate_pool_size_after_hard_filter=0,
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candidate_pool_size_after_freqcap=0,
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)
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is None
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)
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assert (
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compute_empty_reason(
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served_k=0,
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candidate_pool_size_raw=0,
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candidate_pool_size_after_hard_filter=0,
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candidate_pool_size_after_freqcap=0,
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)
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== "pool_empty"
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)
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assert (
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compute_empty_reason(
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served_k=0,
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candidate_pool_size_raw=10,
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candidate_pool_size_after_hard_filter=0,
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candidate_pool_size_after_freqcap=0,
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)
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== "hard_filter_all"
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)
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assert (
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compute_empty_reason(
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served_k=0,
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candidate_pool_size_raw=10,
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candidate_pool_size_after_hard_filter=5,
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candidate_pool_size_after_freqcap=0,
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)
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== "freqcap_all"
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)
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assert (
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compute_empty_reason(
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served_k=0,
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candidate_pool_size_raw=10,
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candidate_pool_size_after_hard_filter=5,
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candidate_pool_size_after_freqcap=3,
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)
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== "unknown"
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)
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def test_builder_outputs_stable_fields_and_monotonic_counts() -> None:
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u = _u(need={"emotional_support": 1}, context={}, emotion_score=None, conf_u=0.2)
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# 故意设置“非单调”的输入，验证 builder 的防御修正
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meta = (
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RecoMetaBuilder(scene="feed", user_profile=u, k=30)
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.set_candidate_pool_size_raw(10)
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.set_after_hard_filter(12) # 非法：大于 raw
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.set_after_dedup(20) # 非法：大于 after_hard
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.set_after_freqcap(15) # 非法：大于 after_dedup（修正后会与 after_dedup 对齐）
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.set_served_k(99) # 非法：大于 after_freqcap
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.set_fallback_level_final(1, reason="freqcap_all")
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.build()
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)
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d = meta.model_dump()
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for k in [
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"scene",
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"candidate_pool_size_raw",
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"candidate_pool_size_after_hard_filter",
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"candidate_pool_size_after_dedup",
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"candidate_pool_size_after_freqcap",
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"fallback_level_final",
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"served_k",
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"empty_reason",
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"conf_U",
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"missing_fields",
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]:
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assert k in d
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assert meta.candidate_pool_size_raw == 10
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assert meta.candidate_pool_size_after_hard_filter == 10
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assert meta.candidate_pool_size_after_dedup == 10
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assert meta.candidate_pool_size_after_freqcap == 10
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assert meta.served_k == 10
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assert meta.conf_U == pytest.approx(0.2)
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assert meta.missing_fields.context is True
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assert meta.missing_fields.emotion is True
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