feat(insta-lab): selection.py 순수 선별 점수(4신호)

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

insta-lab/tests/test_selection.py

@@ -0,0 +1,47 @@
+from app.selection import score_candidates
+
+NOW = "2026-06-11T00:00:00Z"
+
+
+def _cand(kid, kw, cat, score, suggested_at):
+    return {"id": kid, "keyword": kw, "category": cat, "score": score, "suggested_at": suggested_at}
+
+
+def test_dedup_excludes_recent_issued():
+    cands = [_cand(1, "금리", "economy", 0.9, "2026-06-11T00:00:00Z")]
+    issued = [{"keyword": "금리", "category": "economy"}]
+    out = score_candidates(cands, issued, prefs={}, claude_scores=None, threshold=0.0, now_iso=NOW)
+    assert out[0]["eligible"] is False
+
+
+def test_freshness_recent_higher():
+    fresh = _cand(1, "A", "economy", 0.5, "2026-06-11T00:00:00Z")
+    stale = _cand(2, "B", "economy", 0.5, "2026-06-04T00:00:00Z")
+    out = {c["id"]: c for c in score_candidates([fresh, stale], [], {}, None, threshold=0.0, now_iso=NOW)}
+    assert out[1]["breakdown"]["freshness"] > out[2]["breakdown"]["freshness"]
+
+
+def test_account_fit_uses_weight():
+    cands = [_cand(1, "A", "economy", 0.8, NOW), _cand(2, "B", "psychology", 0.8, NOW)]
+    prefs = {"economy": 2.0, "psychology": 1.0}
+    out = {c["id"]: c for c in score_candidates(cands, [], prefs, None, threshold=0.0, now_iso=NOW)}
+    assert out[1]["breakdown"]["account_fit"] > out[2]["breakdown"]["account_fit"]
+
+
+def test_threshold_gate():
+    cands = [_cand(1, "A", "economy", 0.1, "2026-06-01T00:00:00Z")]
+    out = score_candidates(cands, [], {}, None, threshold=0.6, now_iso=NOW)
+    assert out[0]["eligible"] is False
+
+
+def test_claude_missing_renormalizes():
+    cands = [_cand(1, "A", "economy", 1.0, NOW)]
+    out = score_candidates(cands, [], {"economy": 1.0}, None, threshold=0.0, now_iso=NOW)
+    assert out[0]["breakdown"]["claude"] is None
+    assert 0.0 <= out[0]["final_score"] <= 1.0
+
+
+def test_claude_included_when_provided():
+    cands = [_cand(1, "A", "economy", 0.5, NOW)]
+    out = score_candidates(cands, [], {"economy": 1.0}, {1: 1.0}, threshold=0.0, now_iso=NOW)
+    assert out[0]["breakdown"]["claude"] == 1.0