feat: Add belief probe early exit integration

intentkit/belief_integration/alignment_calculator.py

@@ -0,0 +1,201 @@
+"""
+意图 - 信念对齐度计算
+Intent-Belief Alignment Calculator
+
+日期：2026-03-07
+作者：Claw (OpenClaw)
+"""
+
+from typing import Dict, Any, Optional
+from dataclasses import dataclass
+
+
+@dataclass
+class AlignmentResult:
+    """对齐度计算结果"""
+
+    # 综合对齐度 (0-1)
+    alignment_score: float
+
+    # 意图达成度 (0 或 1)
+    intent_achievement: float
+
+    # 信念置信度 (0-1)
+    belief_confidence: float
+
+    # 效率得分 (0-1)
+    efficiency: float
+
+    # 各组件权重
+    weights: Dict[str, float]
+
+    def to_dict(self) -> Dict[str, Any]:
+        """转换为字典"""
+        return {
+            "alignment_score": self.alignment_score,
+            "intent_achievement": self.intent_achievement,
+            "belief_confidence": self.belief_confidence,
+            "efficiency": self.efficiency,
+            "weights": self.weights
+        }
+
+    def __str__(self) -> str:
+        """字符串表示"""
+        return (
+            f"对齐度：{self.alignment_score:.4f}\n"
+            f"  - 意图达成：{self.intent_achievement:.4f} (权重 {self.weights['intent']:.1%})\n"
+            f"  - 信念置信：{self.belief_confidence:.4f} (权重 {self.weights['belief']:.1%})\n"
+            f"  - 效率得分：{self.efficiency:.4f} (权重 {self.weights['efficiency']:.1%})"
+        )
+
+
+class AlignmentCalculator:
+    """对齐度计算器"""
+
+    # 默认权重
+    DEFAULT_WEIGHTS = {
+        "intent": 0.5,      # 意图达成 50%
+        "belief": 0.3,      # 信念置信 30%
+        "efficiency": 0.2   # 效率奖励 20%
+    }
+
+    def __init__(self, weights: Optional[Dict[str, float]] = None):
+        """初始化计算器
+
+        Args:
+            weights: 自定义权重 (可选)
+        """
+        self.weights = weights or self.DEFAULT_WEIGHTS.copy()
+
+    def calculate(
+        self,
+        intent_achieved: bool,
+        belief_confidence: float,
+        layers_used: int,
+        total_layers: int = 24
+    ) -> AlignmentResult:
+        """计算对齐度
+
+        Args:
+            intent_achieved: 意图是否达成
+            belief_confidence: 信念置信度 (0-1)
+            layers_used: 实际使用层数
+            total_layers: 总层数 (默认 24)
+
+        Returns:
+            对齐度计算结果
+        """
+        # 意图达成度 (0 或 1)
+        intent_score = 1.0 if intent_achieved else 0.0
+
+        # 效率得分 (早退奖励)
+        efficiency = 1 - (layers_used / total_layers)
+
+        # 综合对齐度
+        alignment = (
+            self.weights["intent"] * intent_score +
+            self.weights["belief"] * belief_confidence +
+            self.weights["efficiency"] * efficiency
+        )
+
+        return AlignmentResult(
+            alignment_score=alignment,
+            intent_achievement=intent_score,
+            belief_confidence=belief_confidence,
+            efficiency=efficiency,
+            weights=self.weights
+        )
+
+    def calculate_batch(
+        self,
+        executions: list
+    ) -> Dict[str, float]:
+        """批量计算对齐度
+
+        Args:
+            executions: 执行列表，每项包含：
+                - intent_achieved: bool
+                - belief_confidence: float
+                - layers_used: int
+
+        Returns:
+            统计字典
+        """
+        if not executions:
+            return {"avg_alignment": 0.0, "count": 0}
+
+        results = []
+        for exec_data in executions:
+            result = self.calculate(
+                intent_achieved=exec_data["intent_achieved"],
+                belief_confidence=exec_data["belief_confidence"],
+                layers_used=exec_data["layers_used"]
+            )
+            results.append(result.alignment_score)
+
+        return {
+            "avg_alignment": sum(results) / len(results),
+            "min_alignment": min(results),
+            "max_alignment": max(results),
+            "std_alignment": (sum((x - sum(results)/len(results))**2 for x in results) / len(results)) ** 0.5,
+            "count": len(results)
+        }
+
+
+def demo():
+    """演示使用"""
+    print("=== 意图 - 信念对齐度计算演示 ===\n")
+
+    # 创建计算器
+    calculator = AlignmentCalculator()
+
+    # 示例 1: 早退成功
+    print("示例 1: 早退成功")
+    result1 = calculator.calculate(
+        intent_achieved=True,
+        belief_confidence=0.92,
+        layers_used=12
+    )
+    print(result1)
+    print()
+
+    # 示例 2: 全模型成功
+    print("示例 2: 全模型成功")
+    result2 = calculator.calculate(
+        intent_achieved=True,
+        belief_confidence=0.95,
+        layers_used=24
+    )
+    print(result2)
+    print()
+
+    # 示例 3: 早退失败
+    print("示例 3: 早退失败")
+    result3 = calculator.calculate(
+        intent_achieved=False,
+        belief_confidence=0.85,
+        layers_used=8
+    )
+    print(result3)
+    print()
+
+    # 批量计算
+    print("=== 批量统计 ===")
+    executions = [
+        {"intent_achieved": True, "belief_confidence": 0.92, "layers_used": 12},
+        {"intent_achieved": True, "belief_confidence": 0.95, "layers_used": 24},
+        {"intent_achieved": False, "belief_confidence": 0.85, "layers_used": 8},
+        {"intent_achieved": True, "belief_confidence": 0.88, "layers_used": 15},
+        {"intent_achieved": True, "belief_confidence": 0.91, "layers_used": 10},
+    ]
+
+    stats = calculator.calculate_batch(executions)
+    print(f"平均对齐度：{stats['avg_alignment']:.4f}")
+    print(f"最小对齐度：{stats['min_alignment']:.4f}")
+    print(f"最大对齐度：{stats['max_alignment']:.4f}")
+    print(f"标准差：{stats['std_alignment']:.4f}")
+    print(f"样本数：{stats['count']}")
+
+
+if __name__ == "__main__":
+    demo()