Celes Renata
137 lines
4.0 KiB
Python
137 lines
4.0 KiB
Python
"""Tests for evidence ranking — composite scoring for supporting/opposing docs.
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Requirements: 6.5
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"""
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from datetime import datetime, timedelta, timezone
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from services.aggregation.evidence import (
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EvidenceRankConfig,
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compute_evidence_rank,
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rank_evidence,
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rank_evidence_detailed,
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)
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from services.aggregation.scoring import WeightedSignal, compute_signal_weight
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NOW = datetime(2026, 4, 11, 12, 0, 0, tzinfo=timezone.utc)
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def _sw(
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doc_id: str = "doc-1",
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sentiment: float = 1.0,
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impact: float = 0.7,
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credibility: float = 0.8,
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confidence: float = 0.8,
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age_hours: float = 1.0,
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) -> WeightedSignal:
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published = NOW - timedelta(hours=age_hours)
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weight = compute_signal_weight(
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published_at=published,
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reference_time=NOW,
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window="7d",
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source_credibility=credibility,
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extraction_confidence=confidence,
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)
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return WeightedSignal(
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document_id=doc_id,
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weight=weight,
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sentiment_value=sentiment,
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impact_score=impact,
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)
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# ---------------------------------------------------------------------------
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# compute_evidence_rank
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# ---------------------------------------------------------------------------
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def test_rank_score_positive():
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sig = _sw("d1", sentiment=1.0, impact=0.9, credibility=1.0)
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ranked = compute_evidence_rank(sig)
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assert ranked.rank_score > 0
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assert ranked.document_id == "d1"
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assert ranked.sentiment_value == 1.0
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def test_higher_impact_ranks_higher():
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low = _sw("low", impact=0.3)
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high = _sw("high", impact=0.9)
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r_low = compute_evidence_rank(low)
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r_high = compute_evidence_rank(high)
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assert r_high.rank_score > r_low.rank_score
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def test_fresher_doc_ranks_higher():
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old = _sw("old", age_hours=100.0)
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fresh = _sw("fresh", age_hours=1.0)
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r_old = compute_evidence_rank(old)
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r_fresh = compute_evidence_rank(fresh)
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assert r_fresh.rank_score > r_old.rank_score
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def test_higher_credibility_ranks_higher():
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low_cred = _sw("low", credibility=0.2)
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high_cred = _sw("high", credibility=1.0)
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r_low = compute_evidence_rank(low_cred)
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r_high = compute_evidence_rank(high_cred)
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assert r_high.rank_score > r_low.rank_score
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# ---------------------------------------------------------------------------
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# rank_evidence
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# ---------------------------------------------------------------------------
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def test_rank_evidence_separates_sides():
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signals = [
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_sw("pos1", sentiment=1.0, impact=0.9),
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_sw("pos2", sentiment=1.0, impact=0.3),
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_sw("neg1", sentiment=-1.0, impact=0.7),
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_sw("neutral", sentiment=0.0, impact=0.5),
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]
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supporting, opposing = rank_evidence(signals)
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assert "pos1" in supporting
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assert "pos2" in supporting
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assert "neg1" in opposing
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assert "neutral" not in supporting and "neutral" not in opposing
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def test_rank_evidence_ordered_by_composite():
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signals = [
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_sw("weak", sentiment=1.0, impact=0.2, credibility=0.3),
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_sw("strong", sentiment=1.0, impact=0.9, credibility=1.0),
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]
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supporting, _ = rank_evidence(signals)
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assert supporting[0] == "strong"
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def test_rank_evidence_respects_max_refs():
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signals = [_sw(f"d{i}", sentiment=1.0) for i in range(20)]
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cfg = EvidenceRankConfig(max_refs=3)
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supporting, opposing = rank_evidence(signals, config=cfg)
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assert len(supporting) == 3
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assert len(opposing) == 0
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def test_rank_evidence_empty():
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supporting, opposing = rank_evidence([])
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assert supporting == []
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assert opposing == []
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# ---------------------------------------------------------------------------
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# rank_evidence_detailed
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# ---------------------------------------------------------------------------
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def test_detailed_returns_ranked_evidence_objects():
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signals = [
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_sw("pos1", sentiment=1.0, impact=0.9),
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_sw("neg1", sentiment=-1.0, impact=0.7),
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]
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sup, opp = rank_evidence_detailed(signals)
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assert len(sup) == 1
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assert sup[0].document_id == "pos1"
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assert sup[0].rank_score > 0
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assert len(opp) == 1
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assert opp[0].document_id == "neg1"
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