Coverage for packages / ml / ensemble.py: 0%

1"""Ensemble model combining Elo ratings with ML predictions. 

2 

3The EnsembleModel takes Elo-based probabilities from the rating engine 

4and blends them with additional ML predictions (from feature-engineered 

5models) to produce a final probability estimate. 

6""" 

7 

8from __future__ import annotations 

9 

10from typing import Any 

11 

12from sqlalchemy.orm import Session 

13 

14 

15class EnsembleModel: 

16 """Combines Elo ratings with ML predictions for improved accuracy. 

17 

18 Supports multiple blending strategies: 

19 - Weighted average (fixed weights learned offline) 

20 - Logistic calibration (train a logistic regression on top of 

21 Elo + ML features) 

22 - Stacking (use ML predictions as features for a meta-model) 

23 """ 

24 

25 def __init__( 

26 self, 

27 session: Session, 

28 blend_mode: str = "weighted", 

29 ) -> None: 

30 """Initialize the ensemble model. 

31 

32 Args: 

33 session: Database session for loading data. 

34 blend_mode: Blending strategy — "weighted", "logistic", 

35 or "stacking". 

36 """ 

37 self._session = session 

38 self.blend_mode = blend_mode 

39 self._weights: dict[str, float] = {} 

40 

41 def train( 

42 self, 

43 date_from: str, 

44 date_to: str, 

45 ) -> dict[str, Any]: 

46 """Train the ensemble (e.g., learn blend weights). 

47 

48 Args: 

49 date_from: Start date string (YYYY-MM-DD). 

50 date_to: End date string (YYYY-MM-DD). 

51 

52 Returns: 

53 Training metrics dict. 

54 """ 

55 raise NotImplementedError 

56 

57 def predict( 

58 self, 

59 race_id: int, 

60 ) -> list[dict[str, Any]]: 

61 """Predict win/place probabilities for all starters in a race. 

62 

63 Combines Elo ratings with ML feature predictions. 

64 

65 Args: 

66 race_id: The race ID. 

67 

68 Returns: 

69 List of prediction dicts, one per starter, with keys: 

70 - starter_id: int 

71 - win_probability: float 

72 - place_probability: float 

73 - elo_win_prob: float 

74 - ml_win_prob: float (if applicable) 

75 """ 

76 raise NotImplementedError 

77 

78 def evaluate( 

79 self, 

80 date_from: str, 

81 date_to: str, 

82 ) -> dict[str, Any]: 

83 """Evaluate ensemble performance on a test window. 

84 

85 Args: 

86 date_from: Start date string (YYYY-MM-DD). 

87 date_to: End date string (YYYY-MM-DD). 

88 

89 Returns: 

90 Dict of evaluation metrics (accuracy, Brier score, log loss, 

91 top-3 rate, etc.). 

92 """ 

93 raise NotImplementedError 

94 

95 def save_weights(self, path: str) -> None: 

96 """Persist learned blend weights to disk. 

97 

98 Args: 

99 path: File path to save weights to (JSON format). 

100 """ 

101 raise NotImplementedError 

102 

103 def load_weights(self, path: str) -> None: 

104 """Load learned blend weights from disk. 

105 

106 Args: 

107 path: File path to load weights from (JSON format). 

108 """ 

109 raise NotImplementedError