feat: 彩票智能分析技能 v1.0 - 双色球/大乐透集成学习模型

lottery_picker/data/evolution_tool.py

@@ -0,0 +1,285 @@
+#!/usr/bin/env python3
+"""
+回滚迭代跟踪工具 - 管理 AI 进化版本和回滚机制
+"""
+
+import json
+import os
+from datetime import datetime
+from typing import Dict, List, Optional, Any
+from pathlib import Path
+
+
+class EvolutionTracker:
+    """回滚迭代跟踪器"""
+    
+    def __init__(self, data_dir: str = "./evolution_tracking"):
+        self.data_dir = Path(data_dir)
+        self.data_dir.mkdir(parents=True, exist_ok=True)
+        self.version_file = self.data_dir / "versions.json"
+        self.performance_log = self.data_dir / "performance.json"
+        self.rollback_log = self.data_dir / "rollbacks.json"
+        
+    def initialize(self):
+        """初始化跟踪器"""
+        # 初始化版本记录
+        if not self.version_file.exists():
+            initial_version = {
+                "version": 0,
+                "created_at": datetime.now().isoformat(),
+                "performance": {
+                    "win_rate": 0.0,
+                    "total_bets": 0,
+                    "total_winnings": 0.0
+                }
+            }
+            self.save_version(initial_version)
+        
+        # 初始化性能日志
+        if not self.performance_log.exists():
+            self.save_performance({
+                "history": [],
+                "current": {
+                    "total_bets": 0,
+                    "win_rate": 0.0,
+                    "earnings": 0.0,
+                    "total_winnings": 0.0
+                }
+            })
+        
+        # 初始化回滚日志
+        if not self.rollback_log.exists():
+            self.save_rollbacks({
+                "history": [],
+                "current_version": 0
+            })
+    
+    def save_version(self, version_data: Dict):
+        """保存版本信息"""
+        with open(self.version_file, 'w', encoding='utf-8') as f:
+            json.dump(version_data, f, ensure_ascii=False, indent=2)
+    
+    def load_version(self) -> Dict:
+        """加载当前版本"""
+        if self.version_file.exists():
+            with open(self.version_file, 'r', encoding='utf-8') as f:
+                return json.load(f)
+        return {}
+    
+    def record_performance(self, version: int, win_rate: float, earnings: float):
+        """记录性能数据"""
+        performance = self.load_performance()
+        
+        entry = {
+            "version": version,
+            "recorded_at": datetime.now().isoformat(),
+            "win_rate": win_rate,
+            "earnings": earnings,
+            "total_bets": performance["current"].get("total_bets", 0),
+            "total_winnings": performance["current"].get("total_winnings", 0.0)
+        }
+        
+        performance["history"].append(entry)
+        
+        # 更新当前性能
+        current = performance["current"]
+        current["total_bets"] += 1
+        current["win_rate"] = win_rate
+        current["earnings"] = earnings
+        current["total_winnings"] = current.get("total_winnings", 0.0) + earnings
+        
+        self.save_performance(performance)
+    
+    def load_performance(self) -> Dict:
+        """加载性能日志"""
+        if self.performance_log.exists():
+            with open(self.performance_log, 'r', encoding='utf-8') as f:
+                return json.load(f)
+        return {"history": [], "current": {}}
+    
+    def save_performance(self, performance: Dict):
+        """保存性能日志"""
+        with open(self.performance_log, 'w', encoding='utf-8') as f:
+            json.dump(performance, f, ensure_ascii=False, indent=2)
+    
+    def record_rollback(self, from_version: int, to_version: int, reason: str):
+        """记录回滚操作"""
+        rollbacks = self.load_rollbacks()
+        
+        entry = {
+            "from_version": from_version,
+            "to_version": to_version,
+            "reason": reason,
+            "timestamp": datetime.now().isoformat()
+        }
+        
+        rollbacks["history"].append(entry)
+        rollbacks["current_version"] = to_version
+        
+        self.save_rollbacks(rollbacks)
+    
+    def load_rollbacks(self) -> Dict:
+        """加载回滚日志"""
+        if self.rollback_log.exists():
+            with open(self.rollback_log, 'r', encoding='utf-8') as f:
+                return json.load(f)
+        return {"history": [], "current_version": 0}
+    
+    def save_rollbacks(self, rollbacks: Dict):
+        """保存回滚日志"""
+        with open(self.rollback_log, 'w', encoding='utf-8') as f:
+            json.dump(rollbacks, f, ensure_ascii=False, indent=2)
+    
+    def get_version_history(self) -> List[Dict]:
+        """获取版本历史"""
+        performance = self.load_performance()
+        return performance.get("history", [])
+    
+    def get_best_version(self) -> Optional[Dict]:
+        """获取性能最好的版本"""
+        history = self.get_version_history()
+        if not history:
+            return None
+        
+        best = max(history, key=lambda x: x.get("win_rate", 0))
+        return best
+    
+    def compare_versions(self, version1: int, version2: int) -> Dict:
+        """比较两个版本的性能"""
+        history = self.get_version_history()
+        
+        v1_data = next((h for h in history if h["version"] == version1), None)
+        v2_data = next((h for h in history if h["version"] == version2), None)
+        
+        if not v1_data or not v2_data:
+            return {"error": "版本不存在"}
+        
+        comparison = {
+            "version1": version1,
+            "version2": version2,
+            "v1_win_rate": v1_data.get("win_rate", 0),
+            "v2_win_rate": v2_data.get("win_rate", 0),
+            "v1_earnings": v1_data.get("earnings", 0.0),
+            "v2_earnings": v2_data.get("earnings", 0.0),
+            "better_version": version1 if v1_data.get("win_rate", 0) > v2_data.get("win_rate", 0) else version2
+        }
+        
+        return comparison
+    
+    def generate_report(self) -> str:
+        """生成进化报告"""
+        performance = self.load_performance()
+        rollbacks = self.load_rollbacks()
+        
+        report = [
+            "="*60,
+            "AI 进化跟踪报告",
+            "="*60,
+            f"当前版本：{rollbacks.get('current_version', 0)}",
+            f"回滚历史数：{len(rollbacks.get('history', []))}",
+            "\n当前性能数据：",
+            f"  胜率：{performance['current'].get('win_rate', 0):.2%}",
+            f"  累计投注：{performance['current'].get('total_bets', 0)}",
+            f"  累计收益：¥{performance['current'].get('total_winnings', 0.0):.2f}",
+            "\n版本历史：",
+        ]
+        
+        history = performance.get("history", [])
+        if history:
+            for i, entry in enumerate(history[-10:], 1):  # 显示最后 10 个
+                report.extend([
+                    f"  [v{entry['version']}] 胜率：{entry['win_rate']:.2%}, "
+                    f"收益：¥{entry['earnings']:.2f}, "
+                    f"记录时间：{entry['recorded_at']}",
+                    ""
+                ])
+        else:
+            report.append("  暂无历史数据")
+        
+        report.extend(["\n回滚历史：", ""])
+        
+        if rollbacks.get("history"):
+            for i, entry in enumerate(rollbacks["history"]):
+                report.extend([
+                    f"  回滚 #{i+1}: v{entry['from_version']} → v{entry['to_version']}, "
+                    f"原因：{entry['reason']}, "
+                    f"时间：{entry['timestamp']}",
+                    ""
+                ])
+        else:
+            report.append("  暂无回滚记录")
+        
+        report.extend(["="*60, ""])
+        return "\n".join(report)
+    
+    def optimize_weights(self, learning_rate: float = 0.1, epochs: int = 100) -> Dict:
+        """优化模型权重 - 模拟进化过程"""
+        import random
+        
+        # 初始化权重
+        weights = {
+            "red_hot": 0.3,
+            "red_cold": 0.2,
+            "consecutive": 0.15,
+            "sum_range": 0.15,
+            "parity": 0.1,
+            "entropy": 0.1
+        }
+        
+        # 模拟进化过程
+        for epoch in range(epochs):
+            # 模拟数据输入
+            sample_data = [random.randint(1, 33) for _ in range(10)]
+            
+            # 计算损失
+            loss = sum((w - random.random())**2 for w in weights.values())
+            
+            # 梯度下降
+            for key in weights:
+                gradient = sum(random.random() for _ in range(10)) / len(sample_data)
+                weights[key] -= learning_rate * gradient
+        
+        # 归一化权重
+        total = sum(weights.values())
+        weights = {k: round(v/total, 4) for k, v in weights.items()}
+        
+        return {
+            "optimized_weights": weights,
+            "epochs_completed": epochs,
+            "final_loss": round(loss, 4)
+        }
+
+
+def main():
+    """主函数 - 测试回滚跟踪工具"""
+    tracker = EvolutionTracker()
+    tracker.initialize()
+    
+    # 模拟一些版本记录
+    versions = [
+        (1, 0.05, 100.0),
+        (2, 0.12, 150.0),
+        (3, 0.08, 120.0),
+        (4, 0.15, 200.0),
+        (5, 0.18, 250.0)
+    ]
+    
+    for version, win_rate, earnings in versions:
+        tracker.record_performance(version, win_rate, earnings)
+    
+    # 模拟回滚
+    tracker.record_rollback(5, 4, "性能下降")
+    
+    # 生成报告
+    print(tracker.generate_report())
+    
+    # 优化权重
+    optimized = tracker.optimize_weights()
+    print("\n优化结果：")
+    print(f"  完成轮数：{optimized['epochs_completed']}")
+    print(f"  最终损失：{optimized['final_loss']}")
+    print(f"  优化权重：{optimized['optimized_weights']}")
+
+
+if __name__ == "__main__":
+    main()