phase 14-15: docker build validation and helm deployment

tests/test_contradiction.py

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+"""Tests for contradiction detection and disagreement representation.
+
+Requirements: 6.4, 6.5
+"""
+from datetime import datetime, timezone
+
+from services.aggregation.contradiction import (
+    CatalystEntry,
+    ContradictionResult,
+    detect_contradictions,
+)
+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, sentiment: float, impact: float = 0.5) -> WeightedSignal:
+    """Helper to build a WeightedSignal with default scoring."""
+    w = compute_signal_weight(NOW, NOW, "7d", 0.8, extraction_confidence=0.8)
+    return WeightedSignal(doc_id, w, sentiment_value=sentiment, impact_score=impact)
+
+
+# ---------------------------------------------------------------------------
+# Overall score (backward compat with compute_contradiction_score)
+# ---------------------------------------------------------------------------
+
+
+def test_no_signals_returns_zero():
+    result = detect_contradictions([])
+    assert result.score == 0.0
+    assert result.details == []
+
+
+def test_all_positive_no_contradiction():
+    signals = [_sw("d1", 1.0), _sw("d2", 1.0)]
+    result = detect_contradictions(signals)
+    assert result.score == 0.0
+    assert len(result.details) == 0
+
+
+def test_equal_opposing_gives_half():
+    signals = [_sw("d1", 1.0, 0.5), _sw("d2", -1.0, 0.5)]
+    result = detect_contradictions(signals)
+    assert abs(result.score - 0.5) < 1e-4
+
+
+def test_neutral_signals_ignored():
+    signals = [_sw("d1", 0.0), _sw("d2", 0.0)]
+    result = detect_contradictions(signals)
+    assert result.score == 0.0
+    assert result.details == []
+
+
+# ---------------------------------------------------------------------------
+# Sentiment disagreement detail
+# ---------------------------------------------------------------------------
+
+
+def test_sentiment_disagreement_detail_present():
+    signals = [_sw("d1", 1.0, 0.6), _sw("d2", -1.0, 0.4)]
+    result = detect_contradictions(signals)
+    sentiments = [d for d in result.details if d.dimension == "sentiment"]
+    assert len(sentiments) == 1
+    detail = sentiments[0]
+    assert detail.positive_doc_ids == ["d1"]
+    assert detail.negative_doc_ids == ["d2"]
+    assert detail.positive_weight > 0
+    assert detail.negative_weight > 0
+    assert "positive" in detail.description.lower() or "sentiment" in detail.description.lower()
+
+
+def test_no_sentiment_detail_when_all_agree():
+    signals = [_sw("d1", 1.0), _sw("d2", 1.0)]
+    result = detect_contradictions(signals)
+    sentiments = [d for d in result.details if d.dimension == "sentiment"]
+    assert len(sentiments) == 0
+
+
+# ---------------------------------------------------------------------------
+# Catalyst disagreement detail
+# ---------------------------------------------------------------------------
+
+
+def test_catalyst_disagreement_detected():
+    signals = [_sw("d1", 1.0, 0.7), _sw("d2", -1.0, 0.5)]
+    entries = [
+        CatalystEntry("d1", "earnings"),
+        CatalystEntry("d2", "earnings"),
+    ]
+    result = detect_contradictions(signals, entries)
+    catalyst_details = [d for d in result.details if d.dimension.startswith("catalyst:")]
+    assert len(catalyst_details) == 1
+    assert catalyst_details[0].dimension == "catalyst:earnings"
+    assert catalyst_details[0].positive_doc_ids == ["d1"]
+    assert catalyst_details[0].negative_doc_ids == ["d2"]
+
+
+def test_no_catalyst_disagreement_when_same_sentiment():
+    signals = [_sw("d1", 1.0), _sw("d2", 1.0)]
+    entries = [
+        CatalystEntry("d1", "earnings"),
+        CatalystEntry("d2", "earnings"),
+    ]
+    result = detect_contradictions(signals, entries)
+    catalyst_details = [d for d in result.details if d.dimension.startswith("catalyst:")]
+    assert len(catalyst_details) == 0
+
+
+def test_catalyst_disagreement_across_types():
+    """Different catalyst types with internal disagreement each get a detail."""
+    signals = [
+        _sw("d1", 1.0, 0.5),
+        _sw("d2", -1.0, 0.5),
+        _sw("d3", 1.0, 0.5),
+        _sw("d4", -1.0, 0.5),
+    ]
+    entries = [
+        CatalystEntry("d1", "earnings"),
+        CatalystEntry("d2", "earnings"),
+        CatalystEntry("d3", "product"),
+        CatalystEntry("d4", "product"),
+    ]
+    result = detect_contradictions(signals, entries)
+    catalyst_details = [d for d in result.details if d.dimension.startswith("catalyst:")]
+    dims = {d.dimension for d in catalyst_details}
+    assert "catalyst:earnings" in dims
+    assert "catalyst:product" in dims
+
+
+# ---------------------------------------------------------------------------
+# Integration with assemble_trend_summary
+# ---------------------------------------------------------------------------
+
+
+def test_trend_summary_includes_disagreement_details():
+    """assemble_trend_summary should populate disagreement_details."""
+    from datetime import timedelta
+
+    from services.aggregation.worker import (
+        ImpactRow,
+        assemble_trend_summary,
+        build_weighted_signals,
+    )
+
+    impacts = [
+        ImpactRow(
+            document_id="d1", confidence=0.8, novelty_score=0.5,
+            source_credibility=0.8, sentiment="positive", impact_score=0.7,
+            catalyst_type="earnings", key_facts=[], risks=[],
+            published_at=NOW - timedelta(hours=1),
+        ),
+        ImpactRow(
+            document_id="d2", confidence=0.8, novelty_score=0.5,
+            source_credibility=0.8, sentiment="negative", impact_score=0.7,
+            catalyst_type="earnings", key_facts=[], risks=[],
+            published_at=NOW - timedelta(hours=2),
+        ),
+    ]
+    signals = build_weighted_signals(impacts, NOW, "7d")
+    summary = assemble_trend_summary("AAPL", "7d", signals, impacts, reference_time=NOW)
+
+    assert summary.contradiction_score > 0
+    assert len(summary.disagreement_details) > 0
+    dims = {d.dimension for d in summary.disagreement_details}
+    assert "sentiment" in dims