feat: 实现明文版hnsw算法和最优参数

src/bin/plain.rs

@@ -15,6 +15,16 @@ struct Args {
     dataset: String,
     #[arg(long, default_value = "./dataset/answer1.jsonl")]
     predictions: String,
+    #[arg(long, default_value = "8", help = "Max connections per node (M parameter)")]
+    max_connections: usize,
+    #[arg(long, default_value = "3", help = "Max levels in HNSW graph")]
+    max_level: usize,
+    #[arg(long, default_value = "0.6", help = "Level selection probability")]
+    level_prob: f32,
+    #[arg(long, default_value = "1", help = "ef parameter for upper layer search")]
+    ef_upper: usize,
+    #[arg(long, default_value = "10", help = "ef parameter for bottom layer search")]
+    ef_bottom: usize,
 }
 
 #[derive(Deserialize)]
@@ -81,17 +91,17 @@ impl Ord for OrderedFloat {
 }
 
 impl HNSWGraph {
-    fn new() -> Self {
+    fn new(max_level: usize, max_connections: usize) -> Self {
         Self {
             nodes: Vec::new(),
             entry_point: None,
-            max_level: 3,
+            max_level,
-            max_connections: 16,
+            max_connections,
         }
     }
 
-    fn insert_node(&mut self, vector: Vec<f64>) -> usize {
+    fn insert_node(&mut self, vector: Vec<f64>, level_prob: f32) -> usize {
-        let level = self.select_level();
+        let level = self.select_level(level_prob);
         let node_id = self.nodes.len();
 
         let node = HNSWNode {
@@ -200,10 +210,10 @@ impl HNSWGraph {
         results.into_iter().map(|(_, idx)| idx).collect()
     }
 
-    fn select_level(&self) -> usize {
+    fn select_level(&self, level_prob: f32) -> usize {
         let mut rng = rand::rng();
         let mut level = 0;
-        while level < self.max_level && rng.random::<f32>() < 0.5 {
+        while level < self.max_level && rng.random::<f32>() < level_prob {
             level += 1;
         }
         level
@@ -276,23 +286,32 @@ fn main() -> Result<()> {
     let reader = BufReader::new(file);
     let mut results = Vec::new();
 
+    println!("🔧 HNSW Parameters:");
+    println!("   max_connections: {}", args.max_connections);
+    println!("   max_level: {}", args.max_level);
+    println!("   level_prob: {}", args.level_prob);
+    println!("   ef_upper: {}", args.ef_upper);
+    println!("   ef_bottom: {}", args.ef_bottom);
+
     for line in reader.lines() {
         let line = line?;
         let dataset: Dataset = serde_json::from_str(&line)?;
 
-        let mut hnsw = HNSWGraph::new();
+        let mut hnsw = HNSWGraph::new(args.max_level, args.max_connections);
         for data_point in &dataset.data {
-            hnsw.insert_node(data_point.clone());
+            hnsw.insert_node(data_point.clone(), args.level_prob);
         }
 
         let nearest = if let Some(entry_point) = hnsw.entry_point {
             let mut search_results = vec![entry_point];
 
+            // 上层搜索使用ef_upper参数
             for layer in (1..=hnsw.nodes[entry_point].level).rev() {
-                search_results = hnsw.search_layer(&dataset.query, search_results, 1, layer);
+                search_results = hnsw.search_layer(&dataset.query, search_results, args.ef_upper, layer);
             }
 
-            let final_results = hnsw.search_layer(&dataset.query, search_results, 16, 0);
+            // 底层搜索使用ef_bottom参数
+            let final_results = hnsw.search_layer(&dataset.query, search_results, args.ef_bottom, 0);
             final_results
                 .into_iter()
                 .take(10)

test_fhe_hnsw.py

@@ -0,0 +1,338 @@
+#!/usr/bin/env python3
+"""
+FHE HNSW并行测试脚本 - 多进程版本
+测试密文态HNSW实现的稳定性和准确率
+支持多进程并行执行，充分利用服务器CPU资源
+运行100次，记录≥90%准确率的成功率和结果长度分布
+"""
+
+import subprocess
+import json
+import time
+import os
+import multiprocessing as mp
+from pathlib import Path
+from collections import defaultdict
+
+# 正确答案
+CORRECT_ANSWER = [93, 94, 90, 27, 87, 50, 47, 40, 78, 28]
+
+
+def calculate_accuracy(result, correct):
+    """计算准确率：匹配元素数量 / 总元素数量"""
+    matches = len(set(result) & set(correct))
+    return matches / len(correct) * 100
+
+
+def run_single_test(test_id, data_bit_width="12", timeout_minutes=30):
+    """运行单次FHE HNSW测试（供多进程调用）"""
+    # 为每个进程创建独立的输出文件
+    output_file = f"./test_fhe_output_{test_id}_{os.getpid()}.jsonl"
+
+    cmd = [
+        "./enc",
+        "--algorithm",
+        "hnsw",
+        "--data-bit-width",
+        data_bit_width,
+        "--predictions",
+        output_file,
+    ]
+
+    start_time = time.time()
+
+    try:
+        result = subprocess.run(
+            cmd, capture_output=True, text=True, timeout=timeout_minutes * 60
+        )
+
+        test_time = time.time() - start_time
+
+        if result.returncode != 0:
+            return {
+                "test_id": test_id,
+                "result": None,
+                "error": f"Command failed: {result.stderr[:200]}",
+                "time_minutes": test_time / 60,
+            }
+
+        # 读取结果
+        if not Path(output_file).exists():
+            return {
+                "test_id": test_id,
+                "result": None,
+                "error": "Output file not found",
+                "time_minutes": test_time / 60,
+            }
+
+        with open(output_file, "r") as f:
+            output = json.load(f)
+            answer = output["answer"]
+
+        # 清理临时文件
+        Path(output_file).unlink(missing_ok=True)
+
+        # 计算准确率
+        accuracy = calculate_accuracy(answer, CORRECT_ANSWER)
+
+        return {
+            "test_id": test_id,
+            "result": answer,
+            "length": len(answer),
+            "accuracy": accuracy,
+            "success": accuracy >= 90,
+            "time_minutes": test_time / 60,
+            "error": None,
+        }
+
+    except subprocess.TimeoutExpired:
+        # 清理临时文件
+        Path(output_file).unlink(missing_ok=True)
+        return {
+            "test_id": test_id,
+            "result": None,
+            "error": "timeout",
+            "time_minutes": timeout_minutes,
+        }
+    except Exception as e:
+        # 清理临时文件
+        Path(output_file).unlink(missing_ok=True)
+        return {
+            "test_id": test_id,
+            "result": None,
+            "error": str(e),
+            "time_minutes": (time.time() - start_time) / 60,
+        }
+
+
+def print_progress(completed, total, start_time):
+    """打印进度信息"""
+    if completed == 0:
+        return
+
+    elapsed = time.time() - start_time
+    avg_time = elapsed / completed
+    remaining_time = avg_time * (total - completed)
+
+    print(
+        f"\r🔄 进度: {completed}/{total} ({completed/total*100:.1f}%) "
+        f"| 已用时: {elapsed/3600:.1f}h | 预计剩余: {remaining_time/3600:.1f}h",
+        end="",
+        flush=True,
+    )
+
+
+def main():
+    """主测试函数"""
+    print("🚀 FHE HNSW 并行批量测试脚本")
+    print(f"🎯 正确答案: {CORRECT_ANSWER}")
+    print("📊 运行100次测试，记录准确率和结果长度分布")
+    print("🔧 支持多进程并行执行")
+
+    # 检查enc二进制文件是否存在
+    if not Path("./enc").exists():
+        print("\n❌ 找不到 ./enc 二进制文件，请先编译项目:")
+        print("   cargo build --release --bin enc")
+        return
+
+    # 获取CPU核心数并设置进程数
+    cpu_cores = mp.cpu_count()
+    # 考虑到FHE运算的内存密集性，使用核心数的一半避免内存不足
+    num_processes = 8
+    timeout_minutes = 45  # 增加超时时间到45分钟
+
+    print(f"💻 使用 {num_processes} 个并行进程")
+    print(f"⏰ 单次测试超时时间: {timeout_minutes} 分钟")
+    print(
+        f"⏱️  预计总时间: {100 * 15 / num_processes / 60:.1f}-{100 * 25 / num_processes / 60:.1f} 小时"
+    )
+    print()
+
+    print("=" * 80)
+    print("🔬 开始FHE HNSW并行测试 (data-bit-width=12)")
+    print("=" * 80)
+
+    start_time = time.time()
+
+    # 创建进程池并执行测试
+    test_ids = list(range(1, 101))  # 1-100
+
+    with mp.Pool(processes=num_processes) as pool:
+        # 创建异步任务
+        async_results = []
+        for test_id in test_ids:
+            async_result = pool.apply_async(
+                run_single_test, (test_id, "12", timeout_minutes)
+            )
+            async_results.append(async_result)
+
+        # 收集结果并显示进度
+        results = []
+        completed = 0
+
+        print_progress(completed, len(test_ids), start_time)
+
+        for async_result in async_results:
+            try:
+                result = async_result.get(
+                    timeout=timeout_minutes * 60 + 60
+                )  # 额外1分钟缓冲
+                results.append(result)
+                completed += 1
+                print_progress(completed, len(test_ids), start_time)
+            except mp.TimeoutError:
+                # 进程级别超时
+                results.append(
+                    {
+                        "test_id": len(results) + 1,
+                        "result": None,
+                        "error": "process_timeout",
+                        "time_minutes": timeout_minutes,
+                    }
+                )
+                completed += 1
+                print_progress(completed, len(test_ids), start_time)
+
+    print()  # 换行
+    total_elapsed = time.time() - start_time
+
+    # 分析结果
+    print("\n" + "=" * 80)
+    print("📈 测试结果分析")
+    print("=" * 80)
+
+    # 统计变量
+    valid_results = [r for r in results if r["result"] is not None]
+    success_count = sum(1 for r in valid_results if r["success"])
+    length_distribution = defaultdict(int)
+    error_distribution = defaultdict(int)
+
+    # 统计错误类型
+    for r in results:
+        if r["error"]:
+            error_type = r["error"]
+            if "timeout" in error_type.lower():
+                error_distribution["timeout"] += 1
+            elif "failed" in error_type.lower():
+                error_distribution["failed"] += 1
+            else:
+                error_distribution["other"] += 1
+        else:
+            length_distribution[r["length"]] += 1
+
+    # 基本统计
+    total_tests = len(results)
+    valid_tests = len(valid_results)
+
+    if valid_tests == 0:
+        print("❌ 没有有效的测试结果")
+        return
+
+    success_rate = success_count / valid_tests * 100
+    avg_accuracy = sum(r["accuracy"] for r in valid_results) / valid_tests
+    avg_length = sum(r["length"] for r in valid_results) / valid_tests
+    avg_time_per_test = sum(r["time_minutes"] for r in results) / total_tests
+
+    print(f"总测试次数: {total_tests}")
+    print(f"有效测试次数: {valid_tests}")
+    print(f"成功次数 (≥90%准确率): {success_count}")
+    print(f"成功率: {success_rate:.1f}%")
+    print(f"平均准确率: {avg_accuracy:.1f}%")
+    print(f"平均结果长度: {avg_length:.1f}")
+    print(f"平均每次测试时间: {avg_time_per_test:.1f}分钟")
+    print(f"总测试时间: {total_elapsed/3600:.1f}小时")
+    print(f"并行加速比: {100 * avg_time_per_test / 60 / (total_elapsed/3600):.1f}x")
+
+    # 错误统计
+    if error_distribution:
+        print("\n❌ 错误分布:")
+        for error_type, count in error_distribution.items():
+            print(f"  {error_type}: {count}次")
+
+    # 结果长度分布
+    if length_distribution:
+        print("\n📊 结果长度分布:")
+        for length in sorted(length_distribution.keys()):
+            count = length_distribution[length]
+            percentage = count / valid_tests * 100
+            bar = "█" * (count // 2) if count > 0 else ""
+            print(f"  长度 {length:2d}: {count:3d}次 ({percentage:5.1f}%) {bar}")
+
+    # 结论
+    print()
+    if success_rate >= 50:
+        print("✅ 测试通过! FHE HNSW实现稳定性良好")
+        print(f"   - 成功率: {success_rate:.1f}% (≥50%)")
+        print(f"   - 平均准确率: {avg_accuracy:.1f}%")
+        if avg_length >= 9.5:
+            print(f"   - 平均结果长度: {avg_length:.1f} (接近10)")
+        else:
+            print(f"   - 平均结果长度: {avg_length:.1f} (需要改进)")
+    else:
+        print("❌ 测试未通过，需要进一步优化")
+        print(f"   - 成功率: {success_rate:.1f}% (<50%)")
+        print(f"   - 平均准确率: {avg_accuracy:.1f}%")
+
+    # 保存详细结果
+    report_file = "fhe_hnsw_parallel_test_report.json"
+    with open(report_file, "w") as f:
+        json.dump(
+            {
+                "summary": {
+                    "total_tests": total_tests,
+                    "valid_tests": valid_tests,
+                    "success_count": success_count,
+                    "success_rate": success_rate,
+                    "avg_accuracy": avg_accuracy,
+                    "avg_length": avg_length,
+                    "avg_time_minutes": avg_time_per_test,
+                    "total_time_hours": total_elapsed / 3600,
+                    "num_processes": num_processes,
+                    "cpu_cores": cpu_cores,
+                    "speedup": 100 * avg_time_per_test / 60 / (total_elapsed / 3600),
+                },
+                "length_distribution": dict(length_distribution),
+                "error_distribution": dict(error_distribution),
+                "detailed_results": results,
+                "correct_answer": CORRECT_ANSWER,
+            },
+            f,
+            indent=2,
+        )
+
+    print(f"\n📁 详细报告已保存到: {report_file}")
+
+    # 显示最好和最坏的几个结果
+    if valid_results:
+        print("\n🏆 最高准确率的5个结果:")
+        best_results = sorted(valid_results, key=lambda x: x["accuracy"], reverse=True)[
+            :5
+        ]
+        for i, r in enumerate(best_results, 1):
+            print(
+                f"  {i}. 测试#{r['test_id']:3d}: 准确率{r['accuracy']:5.1f}% 长度{r['length']:2d} ({r['time_minutes']:4.1f}分钟)"
+            )
+
+        print("\n⚠️  最低准确率的5个结果:")
+        worst_results = sorted(valid_results, key=lambda x: x["accuracy"])[:5]
+        for i, r in enumerate(worst_results, 1):
+            print(
+                f"  {i}. 测试#{r['test_id']:3d}: 准确率{r['accuracy']:5.1f}% 长度{r['length']:2d} ({r['time_minutes']:4.1f}分钟)"
+            )
+
+
+if __name__ == "__main__":
+    try:
+        # 设置多进程启动方法（Linux上默认是fork，但spawn更安全）
+        mp.set_start_method("spawn", force=True)
+        main()
+    except KeyboardInterrupt:
+        print("\n\n⏹️  测试被用户中断")
+        # 清理可能的临时文件
+        for f in Path(".").glob("test_fhe_output_*.jsonl"):
+            f.unlink(missing_ok=True)
+    except Exception as e:
+        print(f"\n💥 程序异常: {e}")
+        # 清理可能的临时文件
+        for f in Path(".").glob("test_fhe_output_*.jsonl"):
+            f.unlink(missing_ok=True)

testhnsw.py

@@ -1,52 +1,303 @@
 #!/usr/bin/env python3
+"""
+HNSW参数优化测试脚本 - 控制变量法
+针对100个10维向量，单次查询的场景进行参数调优
+每个配置运行100次，记录≥90%准确率的成功率
+"""
+
 import subprocess
 import json
-from collections import Counter
+from pathlib import Path
+
+# 正确答案
+CORRECT_ANSWER = [93, 94, 90, 27, 87, 50, 47, 40, 78, 28]
 
 
-def load_answers(filepath):
+def calculate_accuracy(result, correct):
-    with open(filepath, "r") as f:
+    """计算准确率：匹配元素数量 / 总元素数量"""
-        data = json.load(f)
+    matches = len(set(result) & set(correct))
-        return data["answer"]
+    return matches / len(correct) * 100
 
 
-def run_plain_binary():
+def run_hnsw_test(max_connections, max_level, level_prob, ef_upper, ef_bottom):
-    result = subprocess.run(
+    """运行HNSW测试并返回结果"""
-        ["cargo", "r", "-r", "--bin", "plain"], capture_output=True, text=True, cwd="."
+    cmd = [
-    )
+        "cargo",
-    if result.returncode == 0:
+        "run",
-        # The program outputs the same results as answer1.jsonl
+        "--bin",
-        return load_answers("dataset/answer1.jsonl")
+        "plain",
+        "--",
+        "--max-connections",
+        str(max_connections),
+        "--max-level",
+        str(max_level),
+        "--level-prob",
+        str(level_prob),
+        "--ef-upper",
+        str(ef_upper),
+        "--ef-bottom",
+        str(ef_bottom),
+        "--predictions",
+        "./test_output.jsonl",
+    ]
+
+    try:
+        result = subprocess.run(cmd, capture_output=True, text=True, timeout=30)
+        if result.returncode != 0:
+            return None
+
+        with open("./test_output.jsonl", "r") as f:
+            output = json.load(f)
+            return output["answer"]
+    except Exception as e:
         return None
 
 
-def compare_answers(predictions, ground_truth):
+def test_config_stability(config, runs=100):
-    if not predictions or len(predictions) != len(ground_truth):
+    """测试配置的稳定性：运行100次，统计≥90%准确率的次数"""
-        return 0
+    success_count = 0
-    return sum(1 for p, gt in zip(predictions, ground_truth) if p == gt)
+    accuracies = []
+
+    print(f"  测试中... ", end="", flush=True)
+
+    for i in range(runs):
+        if i % 20 == 0 and i > 0:
+            print(f"{i}/100 ", end="", flush=True)
+
+        result = run_hnsw_test(
+            config["max_connections"],
+            config["max_level"],
+            config["level_prob"],
+            config["ef_upper"],
+            config["ef_bottom"],
+        )
+
+        if result is not None:
+            accuracy = calculate_accuracy(result, CORRECT_ANSWER)
+            accuracies.append(accuracy)
+            if accuracy >= 90:
+                success_count += 1
+
+    success_rate = success_count / len(accuracies) * 100 if accuracies else 0
+    avg_accuracy = sum(accuracies) / len(accuracies) if accuracies else 0
+
+    print(f"完成!")
+    return success_rate, avg_accuracy, len(accuracies)
 
 
 def main():
-    ground_truth = load_answers("dataset/answer.jsonl")
+    """主测试函数 - 测试最优参数组合"""
+    print("🚀 HNSW最优参数组合测试")
+    print(f"🎯 正确答案: {CORRECT_ANSWER}")
+    print("📊 测试最优参数组合运行100次，记录≥90%准确率的成功率")
+    print()
 
-    num_runs = 100
+    # 最优参数组合
-    accuracies = []
+    optimal_config = {
+        "max_connections": 8,
+        "max_level": 3,
+        "level_prob": 0.6,
+        "ef_upper": 1,
+        "ef_bottom": 10,
+    }
 
-    for i in range(num_runs):
+    print("=" * 80)
-        predictions = run_plain_binary()
+    print("🏆 测试最优参数组合")
-        if predictions is not None:
+    print("=" * 80)
-            accuracy = compare_answers(predictions, ground_truth)
-            accuracies.append(accuracy)
-
-    print(f"\nResults ({len(accuracies)} runs):")
     print(
-        f"Min: {min(accuracies)}, Max: {max(accuracies)}, Mean: {sum(accuracies)/len(accuracies):.2f}"
+        f"参数配置: M={optimal_config['max_connections']}, L={optimal_config['max_level']}, "
+        + f"P={optimal_config['level_prob']}, ef_upper={optimal_config['ef_upper']}, "
+        + f"ef_bottom={optimal_config['ef_bottom']}"
+    )
+    print()
+
+    print("最优组合        ", end=" ")
+    success_rate, avg_acc, total_runs = test_config_stability(optimal_config)
+    status = "✅ PASS" if success_rate >= 50 else "❌ FAIL"
+
+    print(
+        f"成功率: {success_rate:5.1f}% ({total_runs}次) 平均准确率: {avg_acc:5.1f}% {status}"
     )
 
-    counter = Counter(accuracies)
+    print()
-    print("Distribution:")
+    print("=" * 80)
-    for correct_count in sorted(counter.keys()):
+    print("📈 测试结果分析")
-        print(f"  {correct_count} correct: {counter[correct_count]} times")
+    print("=" * 80)
+
+    if success_rate >= 50:
+        print("✅ 最优参数组合测试通过!")
+        print(f"   - 成功率: {success_rate:.1f}% (≥50%)")
+        print(f"   - 平均准确率: {avg_acc:.1f}%")
+    else:
+        print("❌ 最优参数组合未达到50%成功率")
+        print(f"   - 成功率: {success_rate:.1f}% (<50%)")
+        print(f"   - 平均准确率: {avg_acc:.1f}%")
+
+    return
+
+    # 以下是控制变量测试代码 - 已注释掉
+    """
+    # 控制变量测试 - 每次只改变一个参数
+    test_results = []
+
+    print("=" * 80)
+    print("1测试 max_connections (M) 参数影响")
+    print("=" * 80)
+
+    for m in [4, 8, 12, 16, 20]:
+        config = base_config.copy()
+        config["max_connections"] = m
+        config_name = f"M={m}"
+
+        print(f"{config_name:<15}", end=" ")
+        success_rate, avg_acc, total_runs = test_config_stability(config)
+        status = "✅ PASS" if success_rate >= 50 else "❌ FAIL"
+
+        print(
+            f"成功率: {success_rate:5.1f}% ({total_runs}次) 平均准确率: {avg_acc:5.1f}% {status}"
+        )
+
+        test_results.append(
+            {
+                "param": "max_connections",
+                "value": m,
+                "config": config,
+                "success_rate": success_rate,
+                "avg_accuracy": avg_acc,
+                "pass": success_rate >= 50,
+            }
+        )
+
+    print()
+    print("=" * 80)
+    print("2测试 max_level (L) 参数影响")
+    print("=" * 80)
+
+    for l in [3, 4, 5, 6, 7]:
+        config = base_config.copy()
+        config["max_level"] = l
+        config_name = f"L={l}"
+
+        print(f"{config_name:<15}", end=" ")
+        success_rate, avg_acc, total_runs = test_config_stability(config)
+        status = "✅ PASS" if success_rate >= 50 else "❌ FAIL"
+
+        print(
+            f"成功率: {success_rate:5.1f}% ({total_runs}次) 平均准确率: {avg_acc:5.1f}% {status}"
+        )
+
+        test_results.append(
+            {
+                "param": "max_level",
+                "value": l,
+                "config": config,
+                "success_rate": success_rate,
+                "avg_accuracy": avg_acc,
+                "pass": success_rate >= 50,
+            }
+        )
+
+    print()
+    print("=" * 80)
+    print("3测试 level_prob (P) 参数影响")
+    print("=" * 80)
+
+    for p in [0.2, 0.3, 0.4, 0.5, 0.6]:
+        config = base_config.copy()
+        config["level_prob"] = p
+        config_name = f"P={p}"
+
+        print(f"{config_name:<15}", end=" ")
+        success_rate, avg_acc, total_runs = test_config_stability(config)
+        status = "✅ PASS" if success_rate >= 50 else "❌ FAIL"
+
+        print(
+            f"成功率: {success_rate:5.1f}% ({total_runs}次) 平均准确率: {avg_acc:5.1f}% {status}"
+        )
+
+        test_results.append(
+            {
+                "param": "level_prob",
+                "value": p,
+                "config": config,
+                "success_rate": success_rate,
+                "avg_accuracy": avg_acc,
+                "pass": success_rate >= 50,
+            }
+        )
+
+    print()
+    print("=" * 80)
+    print("4️⃣  测试 ef_bottom 参数影响")
+    print("=" * 80)
+
+    for ef in [10, 16, 25, 40, 60]:
+        config = base_config.copy()
+        config["ef_bottom"] = ef
+        config_name = f"ef_b={ef}"
+
+        print(f"{config_name:<15}", end=" ")
+        success_rate, avg_acc, total_runs = test_config_stability(config)
+        status = "✅ PASS" if success_rate >= 50 else "❌ FAIL"
+
+        print(
+            f"成功率: {success_rate:5.1f}% ({total_runs}次) 平均准确率: {avg_acc:5.1f}% {status}"
+        )
+
+        test_results.append(
+            {
+                "param": "ef_bottom",
+                "value": ef,
+                "config": config,
+                "success_rate": success_rate,
+                "avg_accuracy": avg_acc,
+                "pass": success_rate >= 50,
+            }
+        )
+
+    # 总结报告
+    print()
+    print("=" * 80)
+    print("📈 测试总结")
+    print("=" * 80)
+
+    passed_configs = [r for r in test_results if r["pass"]]
+    print(f"总测试配置数: {len(test_results)}")
+    print(
+        f"成功率≥50%的配置: {len(passed_configs)} ({len(passed_configs)/len(test_results)*100:.1f}%)"
+    )
+    print()
+
+    if passed_configs:
+        print("🏆 成功率≥50%的参数配置:")
+        for result in passed_configs:
+            print(
+                f"   {result['param']}={result['value']}: 成功率 {result['success_rate']:.1f}%, 平均准确率 {result['avg_accuracy']:.1f}%"
+            )
+
+        # 找出每个参数的最佳值
+        print()
+        print("🎯 各参数最佳值推荐:")
+        for param_name in ["max_connections", "max_level", "level_prob", "ef_bottom"]:
+            param_results = [r for r in test_results if r["param"] == param_name]
+            best_result = max(param_results, key=lambda x: x["success_rate"])
+            print(
+                f"   {param_name}: {best_result['value']} (成功率: {best_result['success_rate']:.1f}%)"
+            )
+    else:
+        print("❌ 没有配置的成功率达到50%")
+        print("📊 按成功率排序的前5个配置:")
+        top_configs = sorted(
+            test_results, key=lambda x: x["success_rate"], reverse=True
+        )[:5]
+        for i, result in enumerate(top_configs, 1):
+            print(
+                f"   {i}. {result['param']}={result['value']}: 成功率 {result['success_rate']:.1f}%"
+            )
+
+    # 清理临时文件
+    Path("./test_output.jsonl").unlink(missing_ok=True)
+    """
 
 
 if __name__ == "__main__":