score

ai-engine/tests/test_score_matrix.py

@@ -0,0 +1,84 @@
+"""Unit tests for V36 market-anchored score matrix (pure, no DB/model deps)."""
+
+import os
+import sys
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+from models.score_matrix import (
+    MAX_GOALS,
+    _raw_matrix,
+    _outcome_sums,
+    build_calibrated_score_package,
+    ipf_to_outcomes,
+    split_lambdas,
+    top_scores,
+    total_lambda_from_over25,
+)
+
+
+def _approx(a, b, tol=1e-6):
+    return abs(a - b) <= tol
+
+
+def test_total_lambda_solver_roundtrip():
+    import math
+    for t_true in (1.5, 2.4, 3.5):
+        p_over = 1.0 - math.exp(-t_true) * (1 + t_true + t_true * t_true / 2)
+        assert _approx(total_lambda_from_over25(p_over), t_true, 1e-3)
+
+
+def test_split_matches_win_gap_direction():
+    lh, la = split_lambdas(2.6, 0.60, 0.18)   # strong home side
+    assert lh > la
+    lh2, la2 = split_lambdas(2.6, 0.18, 0.60)  # strong away side
+    assert la2 > lh2
+
+
+def test_ipf_makes_matrix_exactly_consistent_with_1x2():
+    p1, px, p2 = 0.62, 0.21, 0.17
+    lh, la = split_lambdas(2.7, p1, p2)
+    mat = ipf_to_outcomes(_raw_matrix(lh, la), p1, px, p2)
+    w, d, l = _outcome_sums(mat)
+    assert _approx(w, p1, 1e-9) and _approx(d, px, 1e-9) and _approx(l, p2, 1e-9)
+
+
+def test_top_scores_sorted_and_shaped():
+    mat = _raw_matrix(1.6, 1.1)
+    top = top_scores(mat, 5)
+    assert len(top) == 5
+    probs = [t["prob"] for t in top]
+    assert probs == sorted(probs, reverse=True)
+    assert all("-" in t["score"] for t in top)
+
+
+def test_package_full_fields_and_consistency():
+    pkg = build_calibrated_score_package(0.526, 0.258, 0.216, 0.55)
+    assert pkg["ft"] and pkg["ht"]
+    assert pkg["xg_home"] > pkg["xg_away"]          # home is favourite
+    assert _approx(pkg["xg_total"], pkg["xg_home"] + pkg["xg_away"], 0.02)
+    assert len(pkg["scenario_top5"]) == 5
+    assert pkg["calibration_source"] == "market_anchor_v36_score"
+    # HT must be a lower-scoring line than FT on average
+    fh, fa = map(int, str(pkg["ft"]).split("-"))
+    hh, ha = map(int, str(pkg["ht"]).split("-"))
+    assert hh + ha <= fh + fa
+
+
+def test_ht_ipf_applied_when_probs_given():
+    base = build_calibrated_score_package(0.40, 0.30, 0.30, 0.50)
+    forced = build_calibrated_score_package(
+        0.40, 0.30, 0.30, 0.50, ht_probs=(0.05, 0.90, 0.05)
+    )
+    # forcing a near-certain HT draw must make the modal HT score a draw line
+    hh, ha = map(int, str(forced["ht"]).split("-"))
+    assert hh == ha
+    assert base["ft"] == forced["ft"]  # FT untouched by HT anchoring
+
+
+if __name__ == "__main__":
+    fns = [v for k, v in sorted(globals().items()) if k.startswith("test_")]
+    for fn in fns:
+        fn()
+        print(f"PASS {fn.__name__}")
+    print(f"\nAll {len(fns)} tests passed.")