"""Tests for evidence ranking — composite scoring for supporting/opposing docs.

Requirements: 6.5
"""
from datetime import datetime, timedelta, timezone

from services.aggregation.evidence import (
    EvidenceRankConfig,
    compute_evidence_rank,
    rank_evidence,
    rank_evidence_detailed,
)
from services.aggregation.scoring import WeightedSignal, compute_signal_weight

NOW = datetime(2026, 4, 11, 12, 0, 0, tzinfo=timezone.utc)


def _sw(
    doc_id: str = "doc-1",
    sentiment: float = 1.0,
    impact: float = 0.7,
    credibility: float = 0.8,
    confidence: float = 0.8,
    age_hours: float = 1.0,
) -> WeightedSignal:
    published = NOW - timedelta(hours=age_hours)
    weight = compute_signal_weight(
        published_at=published,
        reference_time=NOW,
        window="7d",
        source_credibility=credibility,
        extraction_confidence=confidence,
    )
    return WeightedSignal(
        document_id=doc_id,
        weight=weight,
        sentiment_value=sentiment,
        impact_score=impact,
    )


# ---------------------------------------------------------------------------
# compute_evidence_rank
# ---------------------------------------------------------------------------


def test_rank_score_positive():
    sig = _sw("d1", sentiment=1.0, impact=0.9, credibility=1.0)
    ranked = compute_evidence_rank(sig)
    assert ranked.rank_score > 0
    assert ranked.document_id == "d1"
    assert ranked.sentiment_value == 1.0


def test_higher_impact_ranks_higher():
    low = _sw("low", impact=0.3)
    high = _sw("high", impact=0.9)
    r_low = compute_evidence_rank(low)
    r_high = compute_evidence_rank(high)
    assert r_high.rank_score > r_low.rank_score


def test_fresher_doc_ranks_higher():
    old = _sw("old", age_hours=100.0)
    fresh = _sw("fresh", age_hours=1.0)
    r_old = compute_evidence_rank(old)
    r_fresh = compute_evidence_rank(fresh)
    assert r_fresh.rank_score > r_old.rank_score


def test_higher_credibility_ranks_higher():
    low_cred = _sw("low", credibility=0.2)
    high_cred = _sw("high", credibility=1.0)
    r_low = compute_evidence_rank(low_cred)
    r_high = compute_evidence_rank(high_cred)
    assert r_high.rank_score > r_low.rank_score


# ---------------------------------------------------------------------------
# rank_evidence
# ---------------------------------------------------------------------------


def test_rank_evidence_separates_sides():
    signals = [
        _sw("pos1", sentiment=1.0, impact=0.9),
        _sw("pos2", sentiment=1.0, impact=0.3),
        _sw("neg1", sentiment=-1.0, impact=0.7),
        _sw("neutral", sentiment=0.0, impact=0.5),
    ]
    supporting, opposing = rank_evidence(signals)
    assert "pos1" in supporting
    assert "pos2" in supporting
    assert "neg1" in opposing
    assert "neutral" not in supporting and "neutral" not in opposing


def test_rank_evidence_ordered_by_composite():
    signals = [
        _sw("weak", sentiment=1.0, impact=0.2, credibility=0.3),
        _sw("strong", sentiment=1.0, impact=0.9, credibility=1.0),
    ]
    supporting, _ = rank_evidence(signals)
    assert supporting[0] == "strong"


def test_rank_evidence_respects_max_refs():
    signals = [_sw(f"d{i}", sentiment=1.0) for i in range(20)]
    cfg = EvidenceRankConfig(max_refs=3)
    supporting, opposing = rank_evidence(signals, config=cfg)
    assert len(supporting) == 3
    assert len(opposing) == 0


def test_rank_evidence_empty():
    supporting, opposing = rank_evidence([])
    assert supporting == []
    assert opposing == []


# ---------------------------------------------------------------------------
# rank_evidence_detailed
# ---------------------------------------------------------------------------


def test_detailed_returns_ranked_evidence_objects():
    signals = [
        _sw("pos1", sentiment=1.0, impact=0.9),
        _sw("neg1", sentiment=-1.0, impact=0.7),
    ]
    sup, opp = rank_evidence_detailed(signals)
    assert len(sup) == 1
    assert sup[0].document_id == "pos1"
    assert sup[0].rank_score > 0
    assert len(opp) == 1
    assert opp[0].document_id == "neg1"