Coverage for packages / core / ratings / form_cycle.py: 0%

1"""Form cycle detection — identifies peak/trough form periods for racing entities. 

2 

3This module provides the FormCycleDetector stub for detecting when an 

4entity (horse/driver/trainer) is in a peak or trough form period based 

5on recent performance trends. 

6""" 

7 

8from __future__ import annotations 

9 

10from typing import Any 

11 

12from sqlalchemy.orm import Session 

13 

14 

15class FormCycleDetector: 

16 """Detects peak and trough form periods for racing entities. 

17 

18 Uses rolling performance windows, consistency metrics, and trend 

19 analysis to classify form periods. 

20 """ 

21 

22 def __init__(self, session: Session) -> None: 

23 """Initialize the detector. 

24 

25 Args: 

26 session: Database session for loading historical data. 

27 """ 

28 self._session = session 

29 

30 def detect_horse_form( 

31 self, 

32 horse_id: int, 

33 window_size: int = 5, 

34 ) -> dict[str, Any]: 

35 """Detect form cycle for a horse based on recent starts. 

36 

37 Args: 

38 horse_id: The horse entity ID. 

39 window_size: Number of recent starts to analyze. 

40 

41 Returns: 

42 Dict with form classification and supporting metrics: 

43 { 

44 "entity_id": int, 

45 "entity_type": "horse", 

46 "form_phase": "peak" | "trough" | "neutral" | "improving" | "declining", 

47 "confidence": float, 

48 "recent_placings": list[int | None], 

49 "avg_placing": float | None, 

50 "trend_slope": float | None, 

51 "window_size": int, 

52 } 

53 """ 

54 raise NotImplementedError 

55 

56 def detect_driver_form( 

57 self, 

58 driver_id: int, 

59 window_size: int = 5, 

60 ) -> dict[str, Any]: 

61 """Detect form cycle for a driver. 

62 

63 Args: 

64 driver_id: The driver entity ID. 

65 window_size: Number of recent drives to analyze. 

66 

67 Returns: 

68 Form cycle dict (see detect_horse_form). 

69 """ 

70 raise NotImplementedError 

71 

72 def detect_trainer_form( 

73 self, 

74 trainer_id: int, 

75 window_size: int = 8, 

76 ) -> dict[str, Any]: 

77 """Detect form cycle for a trainer. 

78 

79 Args: 

80 trainer_id: The trainer entity ID. 

81 window_size: Number of recent runners to analyze. 

82 

83 Returns: 

84 Form cycle dict (see detect_horse_form). 

85 """ 

86 raise NotImplementedError 

87 

88 def detect_entity_form( 

89 self, 

90 entity_type: str, 

91 entity_id: int, 

92 window_size: int = 5, 

93 ) -> dict[str, Any]: 

94 """Detect form cycle for any entity type. 

95 

96 Dispatches to the appropriate type-specific detector. 

97 

98 Args: 

99 entity_type: "horse", "driver", or "trainer". 

100 entity_id: Entity ID. 

101 window_size: Analysis window size. 

102 

103 Returns: 

104 Form cycle dict. 

105 """ 

106 if entity_type == "horse": 

107 return self.detect_horse_form(entity_id, window_size) 

108 elif entity_type == "driver": 

109 return self.detect_driver_form(entity_id, window_size) 

110 elif entity_type == "trainer": 

111 return self.detect_trainer_form(entity_id, window_size) 

112 else: 

113 raise ValueError(f"Unknown entity type: {entity_type}") 

114 

115 def batch_detect( 

116 self, 

117 entity_type: str, 

118 entity_ids: list[int], 

119 window_size: int = 5, 

120 ) -> list[dict[str, Any]]: 

121 """Detect form cycles for multiple entities of the same type. 

122 

123 Args: 

124 entity_type: Entity type string. 

125 entity_ids: List of entity IDs. 

126 window_size: Analysis window size. 

127 

128 Returns: 

129 List of form cycle dicts. 

130 """ 

131 return [ 

132 self.detect_entity_form(entity_type, eid, window_size) for eid in entity_ids 

133 ]