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