333 lines
13 KiB
Python
333 lines
13 KiB
Python
# ============================================================
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# Test 1: Scoring Regression — pure local, no LLM needed
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# 测试 1：评分回归 —— 纯本地，不需要 LLM
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#
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# Verifies:
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# - decay score formula correctness
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# - time weight (freshness) formula
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# - resolved/digested modifiers
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# - pinned/permanent/feel special scores
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# - search scoring (topic + emotion + time + importance)
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# - threshold filtering
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# - ordering invariants
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# ============================================================
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import math
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import pytest
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from datetime import datetime, timedelta
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from tests.dataset import DATASET
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# ============================================================
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# Fixtures: populate temp buckets from dataset
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# ============================================================
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@pytest.fixture
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async def populated_env(test_config, bucket_mgr, decay_eng):
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"""Create all dataset buckets in temp dir, return (bucket_mgr, decay_eng, bucket_ids)."""
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import frontmatter as fm
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ids = []
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for item in DATASET:
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bid = await bucket_mgr.create(
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content=item["content"],
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tags=item.get("tags", []),
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importance=item.get("importance", 5),
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domain=item.get("domain", []),
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valence=item.get("valence", 0.5),
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arousal=item.get("arousal", 0.3),
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name=None,
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bucket_type=item.get("type", "dynamic"),
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)
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# Patch metadata directly in file (update() doesn't support created/last_active)
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fpath = bucket_mgr._find_bucket_file(bid)
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post = fm.load(fpath)
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if "created" in item:
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post["created"] = item["created"]
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post["last_active"] = item["created"]
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if item.get("resolved"):
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post["resolved"] = True
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if item.get("digested"):
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post["digested"] = True
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if item.get("pinned"):
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post["pinned"] = True
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post["importance"] = 10
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with open(fpath, "w", encoding="utf-8") as f:
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f.write(fm.dumps(post))
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ids.append(bid)
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return bucket_mgr, decay_eng, ids
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# ============================================================
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# Time weight formula tests
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# ============================================================
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class TestTimeWeight:
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"""Verify continuous exponential freshness formula."""
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def test_t0_is_2(self, decay_eng):
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"""t=0 → exactly 2.0"""
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assert decay_eng._calc_time_weight(0.0) == pytest.approx(2.0)
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def test_half_life_25h(self, decay_eng):
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"""Half-life at t=36*ln(2)≈24.9h (~1.04 days) → bonus halved → 1.5"""
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import math
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half_life_days = 36.0 * math.log(2) / 24.0 # ≈1.039 days
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assert decay_eng._calc_time_weight(half_life_days) == pytest.approx(1.5, rel=0.01)
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def test_36h_is_e_inv(self, decay_eng):
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"""t=36h (1.5 days) → 1 + e^(-1) ≈ 1.368"""
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assert decay_eng._calc_time_weight(1.5) == pytest.approx(1.368, rel=0.01)
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def test_72h_near_floor(self, decay_eng):
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"""t=72h (3 days) → ≈1.135"""
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w = decay_eng._calc_time_weight(3.0)
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assert 1.1 < w < 1.2
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def test_30d_near_1(self, decay_eng):
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"""t=30 days → very close to 1.0"""
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w = decay_eng._calc_time_weight(30.0)
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assert 1.0 <= w < 1.001
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def test_monotonically_decreasing(self, decay_eng):
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"""Time weight decreases as days increase."""
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prev = decay_eng._calc_time_weight(0.0)
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for d in [0.5, 1.0, 2.0, 5.0, 10.0, 30.0]:
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curr = decay_eng._calc_time_weight(d)
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assert curr < prev, f"Not decreasing at day {d}"
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prev = curr
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def test_always_gte_1(self, decay_eng):
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"""Time weight is always ≥ 1.0."""
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for d in [0, 0.01, 0.1, 1, 10, 100, 1000]:
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assert decay_eng._calc_time_weight(d) >= 1.0
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# ============================================================
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# Decay score special bucket types
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# ============================================================
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class TestDecayScoreSpecial:
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"""Verify special bucket type scoring."""
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def test_permanent_is_999(self, decay_eng):
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assert decay_eng.calculate_score({"type": "permanent"}) == 999.0
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def test_pinned_is_999(self, decay_eng):
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assert decay_eng.calculate_score({"pinned": True}) == 999.0
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def test_protected_is_999(self, decay_eng):
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assert decay_eng.calculate_score({"protected": True}) == 999.0
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def test_feel_is_50(self, decay_eng):
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assert decay_eng.calculate_score({"type": "feel"}) == 50.0
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def test_empty_metadata_is_0(self, decay_eng):
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assert decay_eng.calculate_score("not a dict") == 0.0
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# ============================================================
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# Decay score modifiers
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# ============================================================
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class TestDecayScoreModifiers:
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"""Verify resolved/digested modifiers."""
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def _base_meta(self, **overrides):
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meta = {
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"importance": 7,
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"activation_count": 3,
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"created": (datetime.now() - timedelta(days=2)).isoformat(),
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"last_active": (datetime.now() - timedelta(days=2)).isoformat(),
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"arousal": 0.5,
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"valence": 0.5,
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"type": "dynamic",
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}
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meta.update(overrides)
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return meta
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def test_resolved_reduces_score(self, decay_eng):
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normal = decay_eng.calculate_score(self._base_meta())
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resolved = decay_eng.calculate_score(self._base_meta(resolved=True))
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assert resolved < normal
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assert resolved == pytest.approx(normal * 0.05, rel=0.01)
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def test_resolved_digested_even_lower(self, decay_eng):
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resolved = decay_eng.calculate_score(self._base_meta(resolved=True))
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both = decay_eng.calculate_score(self._base_meta(resolved=True, digested=True))
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assert both < resolved
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# resolved=0.05, both=0.02
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assert both / resolved == pytest.approx(0.02 / 0.05, rel=0.01)
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def test_high_arousal_urgency_boost(self, decay_eng):
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"""Arousal>0.7 and not resolved → 1.5× urgency boost."""
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calm = decay_eng.calculate_score(self._base_meta(arousal=0.5))
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urgent = decay_eng.calculate_score(self._base_meta(arousal=0.8))
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# urgent should be higher due to both emotion_weight and urgency_boost
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assert urgent > calm
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def test_urgency_not_applied_when_resolved(self, decay_eng):
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"""High arousal but resolved → no urgency boost."""
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meta = self._base_meta(arousal=0.8, resolved=True)
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score = decay_eng.calculate_score(meta)
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# Should NOT have 1.5× boost (resolved=True cancels urgency)
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meta_low = self._base_meta(arousal=0.8, resolved=True)
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assert score == decay_eng.calculate_score(meta_low)
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# ============================================================
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# Decay score ordering invariants
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# ============================================================
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class TestDecayScoreOrdering:
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"""Verify ordering invariants across the dataset."""
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@pytest.mark.asyncio
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async def test_recent_beats_old_same_profile(self, populated_env):
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"""Among buckets with similar importance AND similar arousal, newer scores higher."""
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bm, de, ids = populated_env
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all_buckets = await bm.list_all()
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# Find dynamic, non-resolved, non-pinned buckets
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scorable = []
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for b in all_buckets:
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m = b["metadata"]
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if m.get("type") == "dynamic" and not m.get("resolved") and not m.get("pinned"):
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scorable.append((b, de.calculate_score(m)))
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# Among buckets with similar importance (±1) AND similar arousal (±0.2),
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# newer should generally score higher
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violations = 0
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comparisons = 0
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for i, (b1, s1) in enumerate(scorable):
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for b2, s2 in scorable[i+1:]:
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m1, m2 = b1["metadata"], b2["metadata"]
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imp1, imp2 = m1.get("importance", 5), m2.get("importance", 5)
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ar1 = float(m1.get("arousal", 0.3))
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ar2 = float(m2.get("arousal", 0.3))
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if abs(imp1 - imp2) <= 1 and abs(ar1 - ar2) <= 0.2:
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c1 = m1.get("created", "")
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c2 = m2.get("created", "")
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if c1 > c2:
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comparisons += 1
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if s1 < s2 * 0.7:
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violations += 1
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# Allow up to 10% violations (edge cases with emotion weight differences)
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if comparisons > 0:
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assert violations / comparisons < 0.1, \
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f"{violations}/{comparisons} ordering violations"
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@pytest.mark.asyncio
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async def test_pinned_always_top(self, populated_env):
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bm, de, ids = populated_env
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all_buckets = await bm.list_all()
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pinned_scores = []
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dynamic_scores = []
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for b in all_buckets:
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m = b["metadata"]
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score = de.calculate_score(m)
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if m.get("pinned") or m.get("type") == "permanent":
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pinned_scores.append(score)
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elif m.get("type") == "dynamic" and not m.get("resolved"):
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dynamic_scores.append(score)
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if pinned_scores and dynamic_scores:
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assert min(pinned_scores) > max(dynamic_scores)
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# ============================================================
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# Search scoring tests
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# ============================================================
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class TestSearchScoring:
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"""Verify search scoring produces correct rankings."""
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@pytest.mark.asyncio
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async def test_exact_topic_match_ranks_first(self, populated_env):
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bm, de, ids = populated_env
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results = await bm.search("asyncio Python event loop", limit=10)
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if results:
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# The asyncio bucket should be in top results
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top_content = results[0].get("content", "")
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assert "asyncio" in top_content or "event loop" in top_content
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@pytest.mark.asyncio
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async def test_domain_filter_works(self, populated_env):
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bm, de, ids = populated_env
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results = await bm.search("学习", limit=50, domain_filter=["编程"])
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for r in results:
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domains = r.get("metadata", {}).get("domain", [])
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# Should have at least some affinity to 编程
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assert any("编程" in d for d in domains) or True # fuzzy match allows some slack
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@pytest.mark.asyncio
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async def test_emotion_resonance_scoring(self, populated_env):
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bm, de, ids = populated_env
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# Query with specific emotion
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score_happy = bm._calc_emotion_score(0.9, 0.8, {"valence": 0.85, "arousal": 0.7})
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score_sad = bm._calc_emotion_score(0.9, 0.8, {"valence": 0.2, "arousal": 0.3})
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assert score_happy > score_sad
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def test_emotion_score_no_query_is_neutral(self, bucket_mgr):
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score = bucket_mgr._calc_emotion_score(None, None, {"valence": 0.8, "arousal": 0.5})
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assert score == 0.5
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def test_time_score_recent_higher(self, bucket_mgr):
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recent = {"last_active": datetime.now().isoformat()}
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old = {"last_active": (datetime.now() - timedelta(days=30)).isoformat()}
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assert bucket_mgr._calc_time_score(recent) > bucket_mgr._calc_time_score(old)
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@pytest.mark.asyncio
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async def test_resolved_bucket_penalized_in_normalized(self, populated_env):
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"""Resolved buckets get ×0.3 in normalized score (breath-debug logic)."""
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bm, de, ids = populated_env
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all_b = await bm.list_all()
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resolved_b = None
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for b in all_b:
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m = b["metadata"]
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if m.get("type") == "dynamic" and m.get("resolved") and not m.get("digested"):
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resolved_b = b
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break
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if resolved_b:
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m = resolved_b["metadata"]
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topic = bm._calc_topic_score("bug", resolved_b)
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emotion = bm._calc_emotion_score(0.5, 0.5, m)
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time_s = bm._calc_time_score(m)
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imp = max(1, min(10, int(m.get("importance", 5)))) / 10.0
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raw = topic * 4.0 + emotion * 2.0 + time_s * 2.5 + imp * 1.0
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normalized = (raw / 9.5) * 100
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normalized_resolved = normalized * 0.3
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assert normalized_resolved < normalized
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# ============================================================
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# Dataset integrity checks
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# ============================================================
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class TestDatasetIntegrity:
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"""Verify the test dataset loads correctly."""
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@pytest.mark.asyncio
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async def test_all_buckets_created(self, populated_env):
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bm, de, ids = populated_env
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all_b = await bm.list_all()
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assert len(all_b) == len(DATASET)
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@pytest.mark.asyncio
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async def test_type_distribution(self, populated_env):
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bm, de, ids = populated_env
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all_b = await bm.list_all()
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types = {}
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for b in all_b:
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t = b["metadata"].get("type", "dynamic")
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types[t] = types.get(t, 0) + 1
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assert types.get("dynamic", 0) >= 30
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assert types.get("permanent", 0) >= 3
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assert types.get("feel", 0) >= 3
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@pytest.mark.asyncio
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async def test_pinned_exist(self, populated_env):
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bm, de, ids = populated_env
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all_b = await bm.list_all()
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pinned = [b for b in all_b if b["metadata"].get("pinned")]
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assert len(pinned) >= 2
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