新功能：个性化推荐算法

server/tests/test_scoring.py

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+from __future__ import annotations
+
+from datetime import datetime, timezone
+
+import pytest
+
+from app.features.personalized_reco.content_repository.types import ContentProfileDTO
+from app.features.personalized_reco.scoring.defaults import get_default_config
+from app.features.personalized_reco.scoring.score import score_content
+from app.features.personalized_reco.scoring.types import ExternalTerms, ScoreConfig
+from app.features.user_profile_scoring.types import ProfileAnswered, UserProfileV1_2, UserStageOneHot
+
+
+def _u(
+    *,
+    stage: str = "unknown",
+    emotion_score: float | None = None,
+    need: dict[str, int] | None = None,
+    context: dict[str, int] | None = None,
+    profile_confidence: float = 1.0,
+) -> UserProfileV1_2:
+    now = datetime.now(tz=timezone.utc)
+    if stage == "expecting":
+        s = UserStageOneHot(expecting=1, parenting=0, unknown=0)
+    elif stage == "parenting":
+        s = UserStageOneHot(expecting=0, parenting=1, unknown=0)
+    else:
+        s = UserStageOneHot(unknown=1)
+
+    return UserProfileV1_2(
+        profile_generated_at=now,
+        profile_confidence=profile_confidence,
+        profile_answered=ProfileAnswered(stage=True, emotion=True, context=True, need=True),
+        stage=s,
+        emotion_score=emotion_score,
+        context=context or {},
+        need=need or {},
+    )
+
+
+def _c(
+    *,
+    stage: str = "general",
+    emotion_score: float | None = None,
+    need_key: str = "emotional_support",
+    context_key: str = "family",
+    need_value: float = 1.0,
+    context_value: float = 1.0,
+    personalization_power: float = 1.0,
+    review_confidence: float = 0.7,
+) -> ContentProfileDTO:
+    return ContentProfileDTO(
+        content_id=1,
+        text="hello",
+        stage=stage,  # type: ignore[arg-type]
+        emotion_score=emotion_score,
+        context_suitability={context_key: context_value},
+        need_suitability={need_key: need_value},
+        personalization_power=personalization_power,
+        risk_flags=[],
+        review_confidence=review_confidence,
+    )
+
+
+def test_missing_fields_defaults_are_applied() -> None:
+    u = _u(emotion_score=None, need={}, context={})
+    c = _c(stage="general", emotion_score=None)
+    r = score_content(scene="feed", user_profile=u, content_profile=c)
+
+    assert r.breakdown.S_need == pytest.approx(0.5)
+    assert r.breakdown.S_context == pytest.approx(0.5)
+    assert r.breakdown.S_emotion == pytest.approx(0.8)
+    assert set(r.breakdown.missing_fields) == {"need", "context", "emotion"}
+
+
+def test_uncertainty_penalty_enabled_for_push_by_default_and_can_be_disabled() -> None:
+    u = _u(stage="unknown", emotion_score=0.6, need={"emotional_support": 1}, context={"family": 1}, profile_confidence=0.2)
+    c = _c(stage="general", emotion_score=0.6, personalization_power=1.0, review_confidence=0.2)
+
+    r_on = score_content(scene="push", user_profile=u, content_profile=c)
+    assert r_on.breakdown.P_uncertainty > 0
+
+    cfg_off = ScoreConfig.model_validate(get_default_config("push").model_dump() | {"enable_uncertainty_penalty": False})
+    r_off = score_content(scene="push", user_profile=u, content_profile=c, config=cfg_off)
+    assert r_off.breakdown.P_uncertainty == pytest.approx(0.0)
+    assert r_off.final_score > r_on.final_score
+
+
+def test_widget_emotion_soft_penalty_is_applied_outside_range() -> None:
+    u = _u(stage="unknown", emotion_score=0.6, need={"emotional_support": 1}, context={"family": 1})
+
+    cfg = get_default_config("widget")
+    assert cfg.widget_emotion_soft_range == (0.4, 0.8)
+    assert cfg.widget_emotion_penalty_gamma == pytest.approx(0.25)
+
+    c_in = _c(stage="general", emotion_score=0.6, personalization_power=0.0)
+    r_in = score_content(scene="widget", user_profile=u, content_profile=c_in, config=cfg)
+    assert r_in.breakdown.P_widget_emotion_out_of_range == pytest.approx(0.0)
+
+    c_out = _c(stage="general", emotion_score=0.0, personalization_power=0.0)
+    r_out = score_content(scene="widget", user_profile=u, content_profile=c_out, config=cfg)
+    assert r_out.breakdown.P_widget_emotion_out_of_range == pytest.approx(0.25)
+    assert r_out.final_score < r_in.final_score
+
+
+def test_pass_false_forces_final_score_zero_but_breakdown_is_present() -> None:
+    u = _u(stage="unknown", emotion_score=0.6, need={"emotional_support": 1}, context={"family": 1})
+    c = _c(stage="general", emotion_score=0.6, personalization_power=1.0)
+    r = score_content(scene="feed", user_profile=u, content_profile=c, pass_filters=False, external_terms=ExternalTerms())
+
+    assert r.final_score == pytest.approx(0.0)
+    assert r.breakdown.passed is False
+    # breakdown 字段集合稳定（至少包含关键分解项）
+    d = r.breakdown.model_dump(by_alias=True)
+    for k in [
+        "scene",
+        "pass",
+        "S_need",
+        "S_context",
+        "S_stage",
+        "S_emotion",
+        "S_core",
+        "S_personal",
+        "S_fresh",
+        "P_fatigue",
+        "P_repeat",
+        "P_risk",
+        "P_uncertainty",
+        "P_widget_emotion_out_of_range",
+        "missing_fields",
+    ]:
+        assert k in d
+