新功能：个性化推荐算法

server/tests/test_observability.py

@@ -0,0 +1,129 @@
+from __future__ import annotations
+
+from datetime import datetime, timezone
+
+import pytest
+
+from app.features.personalized_reco.observability.builder import RecoMetaBuilder
+from app.features.personalized_reco.observability.utils import compute_empty_reason, compute_missing_fields
+from app.features.user_profile_scoring.types import ProfileAnswered, UserProfileV1_2, UserStageOneHot
+
+
+def _u(*, need: dict | None = None, context: dict | None = None, emotion_score=None, conf_u: float = 0.9) -> UserProfileV1_2:
+    now = datetime.now(tz=timezone.utc)
+    return UserProfileV1_2(
+        profile_generated_at=now,
+        profile_confidence=conf_u,
+        profile_answered=ProfileAnswered(stage=True, emotion=True, context=True, need=True),
+        stage=UserStageOneHot(unknown=1),
+        emotion_score=emotion_score,
+        context=context or {},
+        need=need or {},
+    )
+
+
+def test_compute_missing_fields() -> None:
+    u1 = _u(need={}, context={}, emotion_score=None)
+    m1 = compute_missing_fields(u1)
+    assert m1.need is True
+    assert m1.context is True
+    assert m1.emotion is True
+
+    u2 = _u(need={"x": 1}, context={"y": 1}, emotion_score=0.6)
+    m2 = compute_missing_fields(u2)
+    assert m2.need is False
+    assert m2.context is False
+    assert m2.emotion is False
+
+
+def test_compute_empty_reason_branches() -> None:
+    assert (
+        compute_empty_reason(
+            served_k=1,
+            candidate_pool_size_raw=0,
+            candidate_pool_size_after_hard_filter=0,
+            candidate_pool_size_after_freqcap=0,
+        )
+        is None
+    )
+
+    assert (
+        compute_empty_reason(
+            served_k=0,
+            candidate_pool_size_raw=0,
+            candidate_pool_size_after_hard_filter=0,
+            candidate_pool_size_after_freqcap=0,
+        )
+        == "pool_empty"
+    )
+
+    assert (
+        compute_empty_reason(
+            served_k=0,
+            candidate_pool_size_raw=10,
+            candidate_pool_size_after_hard_filter=0,
+            candidate_pool_size_after_freqcap=0,
+        )
+        == "hard_filter_all"
+    )
+
+    assert (
+        compute_empty_reason(
+            served_k=0,
+            candidate_pool_size_raw=10,
+            candidate_pool_size_after_hard_filter=5,
+            candidate_pool_size_after_freqcap=0,
+        )
+        == "freqcap_all"
+    )
+
+    assert (
+        compute_empty_reason(
+            served_k=0,
+            candidate_pool_size_raw=10,
+            candidate_pool_size_after_hard_filter=5,
+            candidate_pool_size_after_freqcap=3,
+        )
+        == "unknown"
+    )
+
+
+def test_builder_outputs_stable_fields_and_monotonic_counts() -> None:
+    u = _u(need={"emotional_support": 1}, context={}, emotion_score=None, conf_u=0.2)
+
+    # 故意设置“非单调”的输入，验证 builder 的防御修正
+    meta = (
+        RecoMetaBuilder(scene="feed", user_profile=u, k=30)
+        .set_candidate_pool_size_raw(10)
+        .set_after_hard_filter(12)  # 非法：大于 raw
+        .set_after_dedup(20)  # 非法：大于 after_hard
+        .set_after_freqcap(15)  # 非法：大于 after_dedup（修正后会与 after_dedup 对齐）
+        .set_served_k(99)  # 非法：大于 after_freqcap
+        .set_fallback_level_final(1, reason="freqcap_all")
+        .build()
+    )
+
+    d = meta.model_dump()
+    for k in [
+        "scene",
+        "candidate_pool_size_raw",
+        "candidate_pool_size_after_hard_filter",
+        "candidate_pool_size_after_dedup",
+        "candidate_pool_size_after_freqcap",
+        "fallback_level_final",
+        "served_k",
+        "empty_reason",
+        "conf_U",
+        "missing_fields",
+    ]:
+        assert k in d
+
+    assert meta.candidate_pool_size_raw == 10
+    assert meta.candidate_pool_size_after_hard_filter == 10
+    assert meta.candidate_pool_size_after_dedup == 10
+    assert meta.candidate_pool_size_after_freqcap == 10
+    assert meta.served_k == 10
+    assert meta.conf_U == pytest.approx(0.2)
+    assert meta.missing_fields.context is True
+    assert meta.missing_fields.emotion is True
+