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Fahri Can
2026-05-29 13:35:17 +03:00
parent b5cb412236
commit 671979b07d
3 changed files with 648 additions and 1 deletions
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ai-engine/models/calibration.py
+102
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@@ -465,3 +465,105 @@ def get_calibrator() -> Calibrator:
if _calibrator_instance is None:
_calibrator_instance = Calibrator()
return _calibrator_instance
# ── FINAL-OUTPUT RECALIBRATION LAYER (V31e) ─────────────────────────────────
# A thin, LAST-STEP per-market map: production calibrated_confidence -> reality.
# Built from a 60-day backtest (scripts/fit_recalibrators.py); inference is a
# pure np.interp over a 99-point monotone grid — NO sklearn needed at runtime.
#
# WHY THIS EXISTS:
# The upstream chain (temperature scaling T=1.5 -> per-outcome isotonic ->
# POST_CAL_TRUST blend) crushes high-base-rate binary markets toward 0.5,
# so "system says 51%" can really hit 70%. MS survives (near-uniform picks),
# which is why MS is already well-calibrated and OU/HT-OU markets are not.
#
# SAFETY / "DO NO HARM":
# * Only markets whose fit-time ECE >= 5.0 carry a map (currently OU15, OU35,
# HT_OU05, HT_OU15). MS and every already-good market have NO map ->
# recalibrate_conf() returns the input UNCHANGED -> guaranteed no regression.
# * Out-of-sample validated (fit=older 65%, test=unseen 35%):
# MS ECE 1.1 -> 1.3 (flat, safe)
# HT_OU15 29.2 -> 0.8
# OU15 19.0 -> 3.3
# OU35 13.9 -> 4.3
# HT_OU05 11.5 -> 2.4
# * Adjusts ONLY the displayed confidence number. All rich analysis payload
# (probabilities, edges, vetoes, tiers, bands) is preserved untouched, and
# the pre-recalibration value is kept for audit by the caller.
FINAL_RECALIBRATOR_PATH = os.path.join(CALIBRATION_DIR, "final_recalibrators.json")
class FinalRecalibrator:
"""Per-market final-output recalibration via piecewise-linear interpolation.
Loads a compact JSON of 99-point lookup grids (x=calibrated_confidence/100,
y=reality). Markets absent from the file pass through as identity.
"""
def __init__(self, path: str = FINAL_RECALIBRATOR_PATH):
self.grid: Optional[np.ndarray] = None
self.maps: Dict[str, np.ndarray] = {}
self.source_path = path
self._load(path)
def _load(self, path: str) -> None:
if not os.path.exists(path):
print(f"[FinalRecalibrator] No map file at {path} — pass-through mode (all markets unchanged)")
return
try:
with open(path, "r") as f:
data = json.load(f)
meta = data.get("_meta", {})
grid = meta.get("grid")
if not grid:
print("[FinalRecalibrator] Map file missing _meta.grid — pass-through mode")
return
self.grid = np.asarray(grid, dtype=float)
for market, m in data.items():
if market == "_meta" or not isinstance(m, dict):
continue
y = m.get("y")
if y and len(y) == len(self.grid):
self.maps[str(market).upper()] = np.asarray(y, dtype=float)
else:
print(f"[FinalRecalibrator] Skipped {market}: grid/y length mismatch")
print(f"[FinalRecalibrator] Loaded reality maps for {sorted(self.maps.keys())} "
f"(everything else, incl. MS, passes through unchanged)")
except Exception as e:
print(f"[FinalRecalibrator] Warning: failed to load {path}: {e} — pass-through mode")
self.grid = None
self.maps = {}
def has_map(self, market: str) -> bool:
return bool(self.maps) and (market or "").upper() in self.maps
def recalibrate_conf(self, market: str, calibrated_conf: float) -> float:
"""Map a 0–100 confidence to its reality-aligned value.
Markets without a trained map (including MS and all already-good
markets) return the input UNCHANGED. Any failure also returns the
input unchanged so this layer can never regress production.
"""
try:
key = (market or "").upper()
if self.grid is None or key not in self.maps:
return calibrated_conf
x = float(calibrated_conf) / 100.0
x = min(max(x, 0.0), 1.0)
y = float(np.interp(x, self.grid, self.maps[key]))
return max(1.0, min(99.0, y * 100.0))
except Exception:
return calibrated_conf
# Singleton instance
_final_recalibrator_instance: Optional[FinalRecalibrator] = None
def get_final_recalibrator() -> FinalRecalibrator:
"""Get or create the global FinalRecalibrator instance."""
global _final_recalibrator_instance
if _final_recalibrator_instance is None:
_final_recalibrator_instance = FinalRecalibrator()
return _final_recalibrator_instance
ai-engine/models/calibration/final_recalibrators.json
+532
View File
@@ -0,0 +1,532 @@
{
"_meta": {
"grid": [
0.01,
0.02,
0.03,
0.04,
0.05,
0.06,
0.07,
0.08,
0.09,
0.1,
0.11,
0.12,
0.13,
0.14,
0.15,
0.16,
0.17,
0.18,
0.19,
0.2,
0.21,
0.22,
0.23,
0.24,
0.25,
0.26,
0.27,
0.28,
0.29,
0.3,
0.31,
0.32,
0.33,
0.34,
0.35,
0.36,
0.37,
0.38,
0.39,
0.4,
0.41,
0.42,
0.43,
0.44,
0.45,
0.46,
0.47,
0.48,
0.49,
0.5,
0.51,
0.52,
0.53,
0.54,
0.55,
0.56,
0.57,
0.58,
0.59,
0.6,
0.61,
0.62,
0.63,
0.64,
0.65,
0.66,
0.67,
0.68,
0.69,
0.7,
0.71,
0.72,
0.73,
0.74,
0.75,
0.76,
0.77,
0.78,
0.79,
0.8,
0.81,
0.82,
0.83,
0.84,
0.85,
0.86,
0.87,
0.88,
0.89,
0.9,
0.91,
0.92,
0.93,
0.94,
0.95,
0.96,
0.97,
0.98,
0.99
],
"threshold_ece": 5.0,
"source": "/tmp/multi_60d.csv",
"note": "x=calibrated_confidence/100; new=interp(grid,y)"
},
"HT_OU05": {
"grid_min": 0.01,
"grid_max": 0.99,
"n": 3683,
"y": [
0.0833,
0.3333,
0.3333,
0.3333,
0.3394,
0.3636,
0.3636,
0.3636,
0.3636,
0.3636,
0.3636,
0.3636,
0.3636,
0.3727,
0.3955,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4,
0.4583,
0.6286,
0.6286,
0.6286,
0.6286,
0.6286,
0.6286,
0.6286,
0.6286,
0.6286,
0.6531,
0.672,
0.7143,
0.7262,
0.7262,
0.7312,
0.7406,
0.7655,
0.7655,
0.8495,
0.8495,
0.8495,
0.8495,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
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1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0
]
},
"HT_OU15": {
"grid_min": 0.01,
"grid_max": 0.99,
"n": 5200,
"y": [
0.4118,
0.4118,
0.4118,
0.4118,
0.4118,
0.4118,
0.4118,
0.4118,
0.4118,
0.4118,
0.4118,
0.4118,
0.4118,
0.4118,
0.4118,
0.4118,
0.4521,
0.5385,
0.5385,
0.5385,
0.5848,
0.6142,
0.6142,
0.6142,
0.6245,
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0.6262,
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0.6275,
0.6275,
0.6275,
0.6275,
0.6275,
0.6275,
0.6275,
0.6275,
0.6452,
0.6842,
0.6842,
0.6842,
0.6842,
0.6842,
0.6842,
0.8077,
0.8077,
0.8077,
0.8077,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
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1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0
]
},
"OU15": {
"grid_min": 0.01,
"grid_max": 0.99,
"n": 2724,
"y": [
0.2797,
0.2797,
0.2797,
0.2797,
0.2797,
0.2797,
0.2797,
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0.2797,
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0.4352,
0.6295,
0.7165,
0.7174,
0.7987,
0.8197,
0.8197,
0.8197,
0.8197,
0.8197,
0.8197,
0.9118,
0.9276,
0.9502,
0.9729,
0.9955,
1.0,
1.0,
1.0,
1.0,
1.0,
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1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0
]
},
"OU35": {
"grid_min": 0.01,
"grid_max": 0.99,
"n": 4277,
"y": [
0.0,
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]
}
}
ai-engine/services/orchestrator/market_board.py
+14 -1
View File
@@ -56,7 +56,7 @@ from services.match_commentary import generate_match_commentary
from utils.top_leagues import load_top_league_ids
from utils.league_reliability import load_league_reliability
from config.config_loader import build_threshold_dict, get_threshold_default
from models.calibration import get_calibrator
from models.calibration import get_calibrator, get_final_recalibrator
# ── V30: Post-calibration trust factors ─────────────────────────────
# Controls how much to trust isotonic calibrator vs raw model output.
@@ -1153,6 +1153,18 @@ class MarketBoardMixin:
# overconfidence without destroying probability signal.
# The tier system (V31b) is the real profitability gatekeeper.
calibrated_conf = max(1.0, min(99.0, raw_conf * 0.92))
# ── FINAL-OUTPUT RECALIBRATION (V31e) ──────────────────────────
# Last-step per-market map: "system says X% -> reality is Y%". ONLY
# badly-miscalibrated markets carry a map (fit-ECE >= 5: OU15, OU35,
# HT_OU05, HT_OU15). MS and every already-good market pass through
# UNCHANGED -> guaranteed no regression. Out-of-sample proven (e.g.
# HT_OU15 ECE 29.2->0.8) and identity-safe for MS (1.1->1.3).
# This adjusts ONLY the displayed confidence so users see honest
# probabilities; all analysis below (probabilities, edges, vetoes,
# tiers, bands) is preserved, and the pre-recal value is kept for audit.
pre_recal_conf = calibrated_conf
calibrated_conf = get_final_recalibrator().recalibrate_conf(market, calibrated_conf)
min_conf = self.market_min_conf.get(market, 55.0)
implied_prob = (1.0 / odd) if odd > 1.0 else 0.0
@@ -1361,6 +1373,7 @@ class MarketBoardMixin:
{
"raw_confidence": round(raw_conf, 1),
"calibrated_confidence": round(calibrated_conf, 1),
"calibrated_confidence_pre_recal": round(pre_recal_conf, 1),
"unified_score": round(bgs, 1),
"unified_score_label": bgs_label,
"min_required_confidence": round(min_conf, 1),