wow

ai-engine/tests/test_live_matrix.py

@@ -0,0 +1,80 @@
+"""Unit tests for V38 live-conditioned projection (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.live_matrix import (
+    build_live_projection,
+    estimate_minute,
+    state_multiplier,
+)
+
+
+def _approx(a, b, tol=1e-6):
+    return abs(a - b) <= tol
+
+
+def test_probs_form_distribution():
+    proj = build_live_projection(0.50, 0.27, 0.23, 0.55, 1, 0, 60)
+    p = proj["probs"]
+    assert _approx(p["1"] + p["X"] + p["2"], 1.0, 1e-3)
+    assert 0.0 <= proj["p_away_scores_again"] <= 1.0
+
+
+def test_minute_one_roughly_matches_prematch():
+    # at 0-0 minute 1 the projection must stay close to the anchored numbers
+    proj = build_live_projection(0.50, 0.27, 0.23, 0.55, 0, 0, 1)
+    assert abs(proj["probs"]["1"] - 0.50) < 0.06
+    assert abs(proj["probs"]["2"] - 0.23) < 0.06
+
+
+def test_one_goal_lead_at_80():
+    # the user's exact case: 1-0 at 80' (OOS-validated: said 21.7 / actual 23.0)
+    proj = build_live_projection(0.50, 0.27, 0.23, 0.55, 1, 0, 80)
+    assert proj["probs"]["1"] > 0.72                 # leader is now strong fav
+    assert 0.08 <= proj["p_away_scores_again"] <= 0.30
+    assert _approx(
+        proj["p_comeback"], proj["probs"]["X"] + proj["probs"]["2"], 1e-9
+    )
+
+
+def test_less_time_means_fewer_chances():
+    early = build_live_projection(0.50, 0.27, 0.23, 0.55, 1, 0, 60)
+    late = build_live_projection(0.50, 0.27, 0.23, 0.55, 1, 0, 85)
+    assert late["p_away_scores_again"] < early["p_away_scores_again"]
+    assert late["probs"]["1"] > early["probs"]["1"]
+
+
+def test_trailing_team_pushes_late():
+    assert state_multiplier(-1, 80) > 1.05    # trailing by one, late: pushes
+    assert state_multiplier(1, 80) < 1.0      # leading by one, late: parks bus
+    assert state_multiplier(-1, 80) > state_multiplier(-1, 30)
+
+
+def test_score_consistency_with_current_score():
+    proj = build_live_projection(0.50, 0.27, 0.23, 0.55, 2, 1, 75)
+    # every scenario must be reachable from the current score
+    for s in proj["scenario_top5"]:
+        fh, fa = map(int, str(s["score"]).split("-"))
+        assert fh >= 2 and fa >= 1
+    assert proj["current_score"] == "2-1"
+
+
+def test_estimate_minute_approximation():
+    now = 1_700_000_000_000
+    assert estimate_minute(None, now) is None
+    assert estimate_minute(now + 60_000, now) is None          # not kicked off
+    assert estimate_minute(now - 30 * 60_000, now) == 30        # mid 1H
+    assert estimate_minute(now - 55 * 60_000, now) == 46        # HT break
+    assert estimate_minute(now - 80 * 60_000, now) == 65        # 2H, break folded
+    assert estimate_minute(now - 200 * 60_000, now) == 94       # capped
+
+
+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.")

ai-engine/tests/test_market_anchor.py

@@ -5,6 +5,9 @@ import sys
 
 sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
 
+# tests must be deterministic: never consult the DB source for corrections
+os.environ["MARKET_ANCHOR_DB"] = "0"
+
 from models.market_anchor import devig, home_favorite_delta, apply_home_correction
 
 
@@ -51,6 +54,83 @@ def test_apply_home_correction_keeps_distribution_valid():
     assert _approx(q[0], 0.30)
 
 
+def test_corrections_artifact_loaded_and_fallback():
+    import json
+    import tempfile
+    from models import market_anchor as ma
+
+    # 1) valid artifact -> values come from the file
+    with tempfile.NamedTemporaryFile(
+        "w", suffix=".json", delete=False, encoding="utf-8"
+    ) as fh:
+        json.dump(
+            {"version": "test", "corrections": {"ms_home": [
+                {"lo": 0.60, "hi": 0.70, "delta": 0.042},
+            ]}},
+            fh,
+        )
+        path = fh.name
+    try:
+        os.environ["MARKET_ANCHOR_CORRECTIONS_PATH"] = path
+        ma.reload_corrections()
+        assert _approx(ma.home_favorite_delta(0.65), 0.042)
+        # band not in the artifact -> the STATIC PRIOR applies (silence must
+        # not erase proven knowledge); 0.45-0.55 static prior is 0.010
+        assert _approx(ma.home_favorite_delta(0.50), 0.010)
+
+        # 2) malformed artifact -> static fallback, never crashes
+        with open(path, "w", encoding="utf-8") as fh2:
+            fh2.write("{not json")
+        ma.reload_corrections()
+        assert ma.home_favorite_delta(0.65) > 0.0    # fallback band value
+        assert _approx(ma.home_favorite_delta(0.65), 0.028)
+    finally:
+        os.environ.pop("MARKET_ANCHOR_CORRECTIONS_PATH", None)
+        ma.reload_corrections()
+        os.unlink(path)
+
+
+def test_away_corrections_only_from_artifact():
+    import json
+    import tempfile
+    from models import market_anchor as ma
+
+    # without an artifact: away correction must be ZERO (earned, not assumed).
+    # (Point the env path at a nonexistent file: the repo now SHIPS a fitted
+    # artifact, so "no artifact" must be simulated explicitly.)
+    os.environ["MARKET_ANCHOR_CORRECTIONS_PATH"] = os.path.join(
+        os.path.dirname(__file__), "does_not_exist.json"
+    )
+    ma.reload_corrections()
+    assert ma.away_favorite_delta(0.65) == 0.0
+    base = ma.apply_corrections(0.20, 0.20, 0.60)
+    assert _approx(base[2], 0.60)  # away untouched without artifact
+
+    with tempfile.NamedTemporaryFile(
+        "w", suffix=".json", delete=False, encoding="utf-8"
+    ) as fh:
+        json.dump(
+            {"version": "t2", "corrections": {
+                "ms_home": [{"lo": 0.45, "hi": 0.55, "delta": 0.010}],
+                "ms_away": [{"lo": 0.55, "hi": 0.65, "delta": 0.020}],
+            }},
+            fh,
+        )
+        path = fh.name
+    try:
+        os.environ["MARKET_ANCHOR_CORRECTIONS_PATH"] = path
+        ma.reload_corrections()
+        assert _approx(ma.away_favorite_delta(0.60), 0.020)
+        p1, px, p2 = ma.apply_corrections(0.20, 0.20, 0.60)
+        assert p2 > 0.60                      # away favourite lifted
+        assert _approx(p1 + px + p2, 1.0)     # still a valid distribution
+        assert p1 < 0.20 and px < 0.20        # others renormalised down
+    finally:
+        os.environ.pop("MARKET_ANCHOR_CORRECTIONS_PATH", None)
+        ma.reload_corrections()
+        os.unlink(path)
+
+
 if __name__ == "__main__":
     fns = [v for k, v in sorted(globals().items()) if k.startswith("test_")]
     for fn in fns: