gg

ai-engine/services/single_match_orchestrator.py

@@ -15,6 +15,7 @@ import json
 import re
 import time
 import math
+import os
 import pandas as pd
 import numpy as np
 from collections import defaultdict
@@ -33,6 +34,7 @@ from models.basketball_v25 import (
 )
 from core.engines.player_predictor import PlayerPrediction, get_player_predictor
 from services.feature_enrichment import FeatureEnrichmentService
+from services.v26_shadow_engine import V26ShadowEngine, get_v26_shadow_engine
 from utils.top_leagues import load_top_league_ids
 from utils.league_reliability import load_league_reliability
 
@@ -137,8 +139,10 @@ class SingleMatchOrchestrator:
 
     def __init__(self) -> None:
         self.v25_predictor: Optional[V25Predictor] = None
+        self.v26_shadow_engine: Optional[V26ShadowEngine] = None
         self.basketball_predictor: Optional[Any] = None
         self.dsn = get_clean_dsn()
+        self.engine_mode = str(os.getenv("AI_ENGINE_MODE", "v25")).strip().lower()
         self.top_league_ids = load_top_league_ids()
         self.league_reliability = load_league_reliability()
         self.enrichment = FeatureEnrichmentService()
@@ -212,6 +216,11 @@ class SingleMatchOrchestrator:
             self.v25_predictor = get_v25_predictor()
         return self.v25_predictor
 
+    def _get_v26_shadow_engine(self) -> V26ShadowEngine:
+        if getattr(self, "v26_shadow_engine", None) is None:
+            self.v26_shadow_engine = get_v26_shadow_engine()
+        return self.v26_shadow_engine
+
     def _build_v25_features(self, data: MatchData) -> Dict[str, float]:
         """
         Build the single authoritative V25 pre-match feature vector.
@@ -676,7 +685,35 @@ class SingleMatchOrchestrator:
         features = self._build_v25_features(data)
         v25_signal = self._get_v25_signal(data, features)
         prediction = self._build_v25_prediction(data, features, v25_signal)
-        return self._build_prediction_package(data, prediction, v25_signal)
+        base_package = self._build_prediction_package(data, prediction, v25_signal)
+
+        mode = str(getattr(self, "engine_mode", "v25") or "v25").lower()
+        if mode not in {"v25", "v26", "dual"}:
+            mode = "v25"
+
+        quality = base_package.get("data_quality", self._compute_data_quality(data))
+        shadow_package = self._get_v26_shadow_engine().build_package(
+            data=data,
+            prediction=prediction,
+            v25_signal=v25_signal,
+            quality=quality,
+        )
+
+        if mode == "v26":
+            return shadow_package
+        if mode == "dual":
+            merged = dict(base_package)
+            merged.update(
+                {
+                    "shadow_engine": shadow_package,
+                    "shadow_engine_version": shadow_package.get("model_version"),
+                    "calibration_version": shadow_package.get("calibration_version"),
+                    "decision_trace_id": shadow_package.get("decision_trace_id"),
+                    "market_reliability": shadow_package.get("market_reliability", {}),
+                }
+            )
+            return merged
+        return base_package
 
     def analyze_match_htms(self, match_id: str) -> Optional[Dict[str, Any]]:
         """