main

ai-engine/core/calculators/base_calculator.py

@@ -40,7 +40,7 @@ class CalculationContext:
     is_surprise: bool = False
 
     # XGBoost Predictions (New)
-    xgboost_preds: dict[str, dict[str, Any]] = field(default_factory=dict)
+    xgboost_preds: dict[str, Any] = field(default_factory=dict)
 
 
 class BaseCalculator:

ai-engine/core/calculators/bet_recommender.py

@@ -28,7 +28,7 @@ class RecommendationResult:
 
 
 class BetRecommender(BaseCalculator):
-    def calculate(self,
+    def calculate(self,  # type: ignore[override]
                   ctx: CalculationContext,
                   ms_res: MatchResultPrediction,
                   ou_res: OverUnderPrediction,

ai-engine/core/calculators/expert_recommender.py

@@ -36,7 +36,7 @@ class ExpertResult:
 
 
 class ExpertRecommender(BaseCalculator):
-    def calculate(self,
+    def calculate(self,  # type: ignore[override]
                   ctx: CalculationContext,
                   ms_res: MatchResultPrediction,
                   ou_res: OverUnderPrediction,

ai-engine/core/calculators/half_time_calculator.py

@@ -31,7 +31,7 @@ class HalfTimeCalculator(BaseCalculator):
             return 1.0 if k == 0 else 0.0
         return (lam ** k) * math.exp(-lam) / math.factorial(k)
         
-    def calculate(self, ctx: CalculationContext) -> HalfTimePrediction:
+    def calculate(self, ctx: CalculationContext) -> HalfTimePrediction:  # type: ignore[override]
         team_pred = ctx.team_pred
         odds_pred = ctx.odds_pred
         

ai-engine/core/calculators/match_result_calculator.py

@@ -22,9 +22,9 @@ class MatchResultCalculator(BaseCalculator):
     def _get_engine_winner(self, home_prob: float, draw_prob: float, away_prob: float) -> str:
         """Determine which outcome an engine favors."""
         probs = {"1": home_prob, "X": draw_prob, "2": away_prob}
-        return max(probs, key=probs.get)
+        return max(probs, key=probs.__getitem__)
     
-    def calculate(self, ctx: CalculationContext) -> MatchResultPrediction:
+    def calculate(self, ctx: CalculationContext) -> MatchResultPrediction:  # type: ignore[override]
         # Weights
         w_team = ctx.weights["team"]
         w_player = ctx.weights["player"]

ai-engine/core/calculators/other_markets_calculator.py

@@ -28,7 +28,7 @@ class OtherMarketsPrediction:
 
 
 class OtherMarketsCalculator(BaseCalculator):
-    def calculate(
+    def calculate(  # type: ignore[override]
         self,
         ctx: CalculationContext,
         ms_result: MatchResultPrediction,

ai-engine/core/calculators/over_under_calculator.py

@@ -55,7 +55,7 @@ class OverUnderCalculator(BaseCalculator):
         
         return over_15, over_25, over_35, btts_yes
     
-    def calculate(self, ctx: CalculationContext) -> OverUnderPrediction:
+    def calculate(self, ctx: CalculationContext) -> OverUnderPrediction:  # type: ignore[override]
         odds_pred = ctx.odds_pred
         referee_mods = ctx.referee_mods
         

ai-engine/core/calculators/risk_assessor.py

@@ -67,12 +67,14 @@ class RiskAssessor(BaseCalculator):
 
         if sport_key == "basketball":
             if is_top_league:
-                return float(
-                    self.config.get("risk.surprise_threshold_basketball_top", self.config.get("risk.surprise_threshold_basketball", 0.30)),
-                )
-            return float(
-                self.config.get("risk.surprise_threshold_basketball_non_top", 0.34),
-            )
+                top_val = self.config.get("risk.surprise_threshold_basketball_top")
+                if top_val is not None:
+                    return float(top_val)
+                base_val = self.config.get("risk.surprise_threshold_basketball")
+                return float(base_val) if base_val is not None else 0.30
+            
+            non_top_val = self.config.get("risk.surprise_threshold_basketball_non_top")
+            return float(non_top_val) if non_top_val is not None else 0.34
 
         if top_label not in ("1/2", "2/1"):
             return base_threshold
@@ -81,27 +83,30 @@ class RiskAssessor(BaseCalculator):
         favorite_side, gap = self._favorite_profile_from_odds(ctx.odds_data)
 
         if is_top_league:
-            favorite_winner_threshold = float(
-                self.config.get(
-                    "risk.surprise_threshold_favorite_reversal_top",
-                    self.config.get("risk.surprise_threshold_favorite_reversal", 0.26),
-                ),
-            )
-            underdog_winner_threshold = float(
-                self.config.get(
-                    "risk.surprise_threshold_underdog_reversal_top",
-                    self.config.get("risk.surprise_threshold_underdog_reversal", 0.20),
-                ),
-            )
+            top_fav = self.config.get("risk.surprise_threshold_favorite_reversal_top")
+            if top_fav is not None:
+                favorite_winner_threshold = float(top_fav)
+            else:
+                base_fav = self.config.get("risk.surprise_threshold_favorite_reversal")
+                favorite_winner_threshold = float(base_fav) if base_fav is not None else 0.26
+                
+            top_ud = self.config.get("risk.surprise_threshold_underdog_reversal_top")
+            if top_ud is not None:
+                underdog_winner_threshold = float(top_ud)
+            else:
+                base_ud = self.config.get("risk.surprise_threshold_underdog_reversal")
+                underdog_winner_threshold = float(base_ud) if base_ud is not None else 0.20
         else:
-            favorite_winner_threshold = float(
-                self.config.get("risk.surprise_threshold_favorite_reversal_non_top", 0.30),
-            )
-            underdog_winner_threshold = float(
-                self.config.get("risk.surprise_threshold_underdog_reversal_non_top", 0.24),
-            )
-        gap_medium = float(self.config.get("risk.htft_reversal_gap_medium", 0.50))
-        gap_strong = float(self.config.get("risk.htft_reversal_gap_strong", 1.00))
+            nt_fav = self.config.get("risk.surprise_threshold_favorite_reversal_non_top")
+            favorite_winner_threshold = float(nt_fav) if nt_fav is not None else 0.30
+            nt_ud = self.config.get("risk.surprise_threshold_underdog_reversal_non_top")
+            underdog_winner_threshold = float(nt_ud) if nt_ud is not None else 0.24
+            
+        gm = self.config.get("risk.htft_reversal_gap_medium")
+        gap_medium = float(gm) if gm is not None else 0.50
+        
+        gs = self.config.get("risk.htft_reversal_gap_strong")
+        gap_strong = float(gs) if gs is not None else 1.00
 
         if favorite_side in ("H", "A"):
             threshold = (
@@ -117,7 +122,7 @@ class RiskAssessor(BaseCalculator):
 
         return base_threshold
     
-    def calculate(self, ctx: CalculationContext, ms_result=None) -> RiskAnalysis:
+    def calculate(self, ctx: CalculationContext, ms_result: Any = None) -> RiskAnalysis:  # type: ignore[override]
         """
         Wrapper for assess_risk to match BaseCalculator interface but with extra arg.
         """
@@ -173,9 +178,15 @@ class RiskAssessor(BaseCalculator):
 
                 threshold = self._dynamic_reversal_threshold(ctx, top_label)
                 if getattr(ctx, "is_top_league", False):
-                    min_gap = float(self.config.get("risk.surprise_min_top_gap_top", self.config.get("risk.surprise_min_top_gap", 0.02)))
+                    top_gap_val = self.config.get("risk.surprise_min_top_gap_top")
+                    if top_gap_val is not None:
+                        min_gap = float(top_gap_val)
+                    else:
+                        base_gap_val = self.config.get("risk.surprise_min_top_gap")
+                        min_gap = float(base_gap_val) if base_gap_val is not None else 0.02
                 else:
-                    min_gap = float(self.config.get("risk.surprise_min_top_gap_non_top", 0.03))
+                    non_top_gap_val = self.config.get("risk.surprise_min_top_gap_non_top")
+                    min_gap = float(non_top_gap_val) if non_top_gap_val is not None else 0.03
 
                 # Trigger surprise only when reversal class is:
                 # - top HT/FT outcome

ai-engine/core/calculators/score_calculator.py

@@ -3,7 +3,7 @@ import pickle
 import pandas as pd
 import xgboost as xgb
 from dataclasses import dataclass
-from typing import List, Dict, Tuple
+from typing import List, Dict, Tuple, Optional
 import math
 from .base_calculator import BaseCalculator, CalculationContext
 from .confidence import calc_confidence_3way, calc_confidence_dc
@@ -16,7 +16,7 @@ class ScorePrediction:
     ft_scores_top5: List[Dict]
     
     # Reconciled MS/DC predictions (can be updated here)
-    reconciled_ms: MatchResultPrediction = None
+    reconciled_ms: Optional[MatchResultPrediction] = None
 
 class ScoreCalculator(BaseCalculator):
 
@@ -57,7 +57,8 @@ class ScoreCalculator(BaseCalculator):
             return 1.0 if k == 0 else 0.0
         return (lam ** k) * math.exp(-lam) / math.factorial(k)
         
-    def calculate(self, ctx: CalculationContext, ms_result: MatchResultPrediction) -> ScorePrediction:
+    def calculate(self, ctx: CalculationContext, ms_result: MatchResultPrediction) -> ScorePrediction:  # type: ignore[override]
+        predicted_ht = None
         # Default Lambdas (fallback)
         lambda_home = max(0.5, ctx.home_xg)
         lambda_away = max(0.5, ctx.away_xg)
@@ -199,7 +200,7 @@ class ScoreCalculator(BaseCalculator):
             predicted_ft = top_overall_score
             
         # If we didn't calculate HT via ML (exception case), do it now
-        if 'predicted_ht' not in locals():
+        if predicted_ht is None:
             ft_to_ht = self.config.get("half_time.ft_to_ht_ratio", 0.42)
             ht_h = round(lambda_home * ft_to_ht)
             ht_a = round(lambda_away * ft_to_ht)