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FeatureSelect-Methods-Leaderboard/.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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FeatureSelect-Methods-Leaderboard/AutoFS/Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . /code
+
+CMD ["python", "Webapp/app.py"]

FeatureSelect-Methods-Leaderboard/AutoFS/README.md

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+---
+title: AutoFS Leaderboard
+emoji: 📊
+colorFrom: blue
+colorTo: green
+sdk: docker
+pinned: false
+---
+
+# AutoFS Leaderboard
+
+This is a visualization dashboard for AutoFS experiment results.
+
+## Deployment on Hugging Face Spaces
+
+1. Create a new Space on Hugging Face.
+2. Select "Docker" as the SDK.
+3. Upload all files from this repository.
+4. The application will automatically build and run.
+
+## Local Development
+
+1. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+2. Run the application:
+   ```bash
+   python Webapp/app.py
+   ```
+
+3. Open http://localhost:5000 in your browser.

FeatureSelect-Methods-Leaderboard/AutoFS/Webapp/app.py

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+import os
+import sys
+import pickle
+import json
+from flask import Flask, jsonify, request, render_template
+
+# Add project root to sys.path to import leaderboard
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+from leaderboard import rank_results
+# ===============================
+# 基本路径配置
+# ===============================
+PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+RESULT_DIR = os.path.join(PROJECT_ROOT, "results")
+DATASET_DIR = os.path.join(PROJECT_ROOT, "datasets")
+
+os.makedirs(RESULT_DIR, exist_ok=True)
+os.makedirs(DATASET_DIR, exist_ok=True)
+
+# ===============================
+# Flask App
+# ===============================
+app = Flask(__name__)
+
+# ===============================
+# 内存缓存
+# ===============================
+RESULT_CACHE = {}  # {dataset_name: results}
+
+
+# ===============================
+# PKL IO 工具
+# ===============================
+def save_result_json(dataset, results):
+    path = os.path.join(RESULT_DIR, f"{dataset}.json")
+    with open(path, "w", encoding='utf-8') as f:
+        json.dump(results, f, indent=4)
+
+
+def load_result_json(dataset):
+    path = os.path.join(RESULT_DIR, f"{dataset}.json")
+    if not os.path.exists(path):
+        return None
+    with open(path, 'r', encoding='utf-8') as f:
+        return json.load(f)
+
+
+def list_available_datasets():
+    datasets = set()
+
+    for f in os.listdir(RESULT_DIR):
+        if f.endswith(".json"):
+            datasets.add(f.replace(".json", ""))
+
+    # 默认展示数据集
+    datasets.add("Authorship")
+
+    return sorted(datasets)
+
+
+# ===============================
+# ⚠️ 你自己的 Agent 入口
+# ===============================
+def run_agent_for_dataset(dataset):
+    """
+    ⚠️ 用你自己的 router / agent 替换这里
+    必须返回 List[Dict]
+    """
+    # ---------------------------
+    # 示例（占位）
+    # ---------------------------
+    return [
+        
+    ]
+# {
+#             "algorithm": "shibaile",
+#             "num_features": 15,
+#             "mean_f1": 0.9233716475,
+#             "mean_auc": 0.9816098520,
+#             "time": 5.75,
+#             "score": 0.9408431088,
+#         },
+#         {
+#             "algorithm": "JMchongxinzailai",
+#             "num_features": 15,
+#             "mean_f1": 0.918,
+#             "mean_auc": 0.979,
+#             "time": 7.32,
+#             "score": 0.932,
+#         },
+
+# ===============================
+# 页面
+# ===============================
+@app.route("/")
+def index():
+    return render_template(
+        "index.html",
+        datasets=list_available_datasets(),
+        default_dataset="Authorship",
+    )
+
+
+# ===============================
+# API：获取结果
+# ===============================
+@app.route("/api/results")
+def get_results():
+    dataset = request.args.get("dataset", "Authorship")
+
+    # ① 内存缓存 (Disabled for debugging)
+    # if dataset in RESULT_CACHE:
+    #     print("------------------------------------------------------------------zoude cache")
+    #     return jsonify(RESULT_CACHE[dataset])
+    
+    # ② 磁盘 pkl/json
+    results = load_result_json(dataset)
+    print(111,results)
+    if results is not None:
+        print("------------------------------------------------------------------zoude json\n",results)
+        RESULT_CACHE[dataset] = results
+        leaderboard = rank_results(results)
+        return jsonify(leaderboard)
+
+    # ③ Agent 实时运行
+    results = run_agent_for_dataset(dataset)
+    print(222,results)
+
+    # ④ 存储
+    if results and len(results) > 0:
+        save_result_json(dataset, results)
+        RESULT_CACHE[dataset] = results
+    
+    print("------------------------------------------------------------------zoude agent")
+    leaderboard = rank_results(results)
+    # 
+    return jsonify(leaderboard)
+    # print(333,leaderboard)
+# @app.route("/api/results")
+# def get_results():
+    dataset = request.args.get("dataset", "Authorship")
+
+    print(f"[DEBUG] request dataset = {dataset}")
+
+    if dataset in RESULT_CACHE:
+        print("[DEBUG] hit memory cache")
+        return jsonify(RESULT_CACHE[dataset])
+
+    results = load_result_pkl(dataset)
+    if results is not None:
+        print("[DEBUG] hit pkl cache")
+        RESULT_CACHE[dataset] = results
+        return jsonify(results)
+
+    print("[DEBUG] run agent")
+    results = run_agent_for_dataset(dataset)
+    print("[DEBUG] agent results =", results)
+
+    save_result_pkl(dataset, results)
+    RESULT_CACHE[dataset] = results
+    return jsonify(results)
+
+
+# ===============================
+# API：数据集列表
+# ===============================
+@app.route("/api/datasets")
+def api_datasets():
+    return jsonify(list_available_datasets())
+
+
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 5000))
+    app.run(host="0.0.0.0", port=port, debug=True)

FeatureSelect-Methods-Leaderboard/AutoFS/Webapp/app1.py

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+import os
+import pickle
+from flask import Flask, jsonify, request, render_template
+
+# ===============================
+# 基本路径配置
+# ===============================
+PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+RESULT_DIR = os.path.join(PROJECT_ROOT, "results")
+DATASET_DIR = os.path.join(PROJECT_ROOT, "datasets")
+
+os.makedirs(RESULT_DIR, exist_ok=True)
+os.makedirs(DATASET_DIR, exist_ok=True)
+
+# ===============================
+# Flask App
+# ===============================
+app = Flask(__name__)
+
+# ===============================
+# 内存缓存
+# ===============================
+RESULT_CACHE = {}  # {dataset_name: results}
+
+
+# ===============================
+# PKL IO 工具
+# ===============================
+def save_result_pkl(dataset, results):
+    path = os.path.join(RESULT_DIR, f"{dataset}.pkl")
+    with open(path, "wb") as f:
+        pickle.dump(results, f)
+
+
+def load_result_pkl(dataset):
+    path = os.path.join(RESULT_DIR, f"{dataset}.pkl")
+    if not os.path.exists(path):
+        return None
+    with open(path, "rb") as f:
+        return pickle.load(f)
+
+
+def list_available_datasets():
+    datasets = set()
+
+    for f in os.listdir(RESULT_DIR):
+        if f.endswith(".pkl"):
+            datasets.add(f.replace(".pkl", ""))
+
+    # 默认展示数据集
+    datasets.add("Authorship")
+
+    return sorted(datasets)
+
+
+# ===============================
+# ⚠️ 你自己的 Agent 入口
+# ===============================
+def run_agent_for_dataset(dataset):
+    """
+    ⚠️ 用你自己的 router / agent 替换这里
+    必须返回 List[Dict]
+    """
+    # ---------------------------
+    # 示例（占位）
+    # ---------------------------
+    return [
+        {
+            "algorithm": "UCRFS",
+            "num_features": 15,
+            "mean_f1": 0.9233716475,
+            "mean_auc": 0.9816098520,
+            "time": 5.75,
+            "score": 0.9408431088,
+        },
+        {
+            "algorithm": "JMIM",
+            "num_features": 15,
+            "mean_f1": 0.918,
+            "mean_auc": 0.979,
+            "time": 7.32,
+            "score": 0.932,
+        },
+    ]
+
+
+# ===============================
+# 页面
+# ===============================
+@app.route("/")
+def index():
+    return render_template(
+        "index.html",
+        datasets=list_available_datasets(),
+        default_dataset="Authorship",
+    )
+
+
+# ===============================
+# API：获取结果
+# ===============================
+@app.route("/api/results")
+def get_results():
+    dataset = request.args.get("dataset", "Authorship")
+
+    # ① 内存缓存
+    if dataset in RESULT_CACHE:
+        rank_results = jsonify(RESULT_CACHE[dataset])
+
+    # ② 磁盘 pkl
+    results = load_result_pkl(dataset)
+    if results is not None:
+        RESULT_CACHE[dataset] = results
+        rank_results = jsonify(results)
+
+    # ③ Agent 实时运行
+    results = run_agent_for_dataset(dataset)
+
+    # ④ 存储
+    save_result_pkl(dataset, results)
+    RESULT_CACHE[dataset] = results
+
+    rank_results = jsonify(results)
+    leaderboard = rank_results(rank_results)
+    return leaderboard
+# @app.route("/api/results")
+# def get_results():
+    dataset = request.args.get("dataset", "Authorship")
+
+    print(f"[DEBUG] request dataset = {dataset}")
+
+    if dataset in RESULT_CACHE:
+        print("[DEBUG] hit memory cache")
+        return jsonify(RESULT_CACHE[dataset])
+
+    results = load_result_pkl(dataset)
+    if results is not None:
+        print("[DEBUG] hit pkl cache")
+        RESULT_CACHE[dataset] = results
+        return jsonify(results)
+
+    print("[DEBUG] run agent")
+    results = run_agent_for_dataset(dataset)
+    print("[DEBUG] agent results =", results)
+
+    save_result_pkl(dataset, results)
+    RESULT_CACHE[dataset] = results
+    return jsonify(results)
+
+
+# ===============================
+# API：数据集列表
+# ===============================
+@app.route("/api/datasets")
+def api_datasets():
+    return jsonify(list_available_datasets())
+
+
+if __name__ == "__main__":
+    app.run(debug=True)

FeatureSelect-Methods-Leaderboard/AutoFS/Webapp/app11.py

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+import pickle
+from flask import Flask, render_template
+from leaderboard import rank_results
+
+app = Flask(__name__)
+
+# 🚀 这里直接接你 Agent 跑完的 results
+def get_results(dataname):
+    # with open("/home/fangsensen/AutoFS/results/"+ dataname +".pkl", "rb") as f:
+    #   results = pickle.load(f)
+    # print(1111111111,results)
+    results = [{'selected_features': [59, 50, 56, 4, 38, 9, 29, 23, 0, 20, 34, 36, 24, 26, 28], 'num_features': 15, 'metrics': {'nb': {'f1': 0.9181133571145461, 'auc': 0.9807805770573524}, 'svm': {'f1': 0.9282600079270711, 'auc': 0.980695564275392}, 'rf': {'f1': 0.9219976218787156, 'auc': 0.9768409098650539}}, 'time': 9.468129634857178, 'algorithm': 'JMIM'}, {'selected_features': [59, 50, 56, 4, 38, 0, 9, 29, 23, 20, 36, 34, 24, 28, 26], 'num_features': 15, 'metrics': {'nb': {'f1': 0.9163694015061433, 'auc': 0.9805189493459717}, 'svm': {'f1': 0.9265953230281413, 'auc': 0.98064247666047}, 'rf': {'f1': 0.9189853349187476, 'auc': 0.9769432925613145}}, 'time': 1.5439717769622803, 'algorithm': 'CFR'}, {'selected_features': [59, 64, 63, 22, 26, 11, 49, 7, 18, 24, 28, 12, 0, 8, 45], 'num_features': 15, 'metrics': {'nb': {'f1': 0.8498612762584224, 'auc': 0.9612941645198875}, 'svm': {'f1': 0.8672215616329766, 'auc': 0.9669919810144432}, 'rf': {'f1': 0.8516052318668254, 'auc': 0.9579325242146627}}, 'time': 3.4254932403564453, 'algorithm': 'DCSF'}, {'selected_features': [69, 59, 9, 4, 38, 24, 0, 49, 26, 18, 28, 11, 66, 12, 7], 'num_features': 15, 'metrics': {'nb': {'f1': 0.8747522790328972, 'auc': 0.968331958034509}, 'svm': {'f1': 0.8916369401506141, 'auc': 0.9765525653706246}, 'rf': {'f1': 0.9151010701545778, 'auc': 0.9804838761712887}}, 'time': 2.531461477279663, 'algorithm': 'IWFS'}, {'selected_features': [59, 50, 4, 38, 24, 0, 56, 26, 29, 49, 28, 23, 34, 36, 20], 'num_features': 15, 'metrics': {'nb': {'f1': 0.8806183115338884, 'auc': 0.973024320439098}, 'svm': {'f1': 0.9082837891399126, 'auc': 0.9784503098286724}, 'rf': {'f1': 0.897661514070551, 'auc': 0.9735557096666029}}, 'time': 2.793144941329956, 'algorithm': 'MRI'}, {'selected_features': [59, 69, 9, 5, 10, 31, 36, 20, 33, 47, 22, 29, 44, 56, 8], 'num_features': 15, 'metrics': {'nb': {'f1': 0.911375346809354, 'auc': 0.979648928949016}, 'svm': {'f1': 0.9064605628220372, 'auc': 0.9782951525850493}, 'rf': {'f1': 0.9252477209671027, 'auc': 0.9822236522028844}}, 'time': 2.9142298698425293, 'algorithm': 'MRMD'}, {'selected_features': [59, 69, 9, 56, 29, 50, 36, 4, 38, 0, 20, 24, 23, 28, 34], 'num_features': 15, 'metrics': {'nb': {'f1': 0.9177962742766549, 'auc': 0.9819010381640604}, 'svm': {'f1': 0.9178755449861277, 'auc': 0.980385760789456}, 'rf': {'f1': 0.9344431232659534, 'auc': 0.9825427569391104}}, 'time': 5.751329660415649, 'algorithm': 'UCRFS'}, {'selected_features': [[23, 15, 69, 43, 9, 52, 33, 8, 5, 3, 59, 47, 34, 55, 36], [50, 16, 31, 44, 47, 9, 69, 42, 33, 36, 63, 65, 23, 20, 22], [29, 13, 38, 3, 28, 59, 56, 69, 26, 20, 34, 50, 14, 49, 36], [59, 19, 20, 36, 24, 29, 9, 10, 23, 28, 22, 8, 56, 0, 60]], 'num_features': [15, 15, 15, 15], 'union_num_features': 4, 'metrics': {'nb': {'f1': 0.879587792310741, 'auc': 0.9680606961937624}, 'svm': {'f1': 0.8917162108600871, 'auc': 0.9710497573464302}, 'rf': {'f1': 0.8789536266349584, 'auc': 0.9655313327795009}}, 'time': 14.973412275314331, 'algorithm': 'CSMDCCMR'}]
+    leaderboard = rank_results(results)
+    # print(222222222222222,leaderboard)
+    return leaderboard
+
+
+@app.route("/")
+def index():
+    dataname = 'Authorship'
+    results = get_results(dataname)
+    leaderboard = rank_results(results)
+    return render_template("index.html", leaderboard=leaderboard)
+
+
+if __name__ == "__main__":
+    app.run(debug=True)

FeatureSelect-Methods-Leaderboard/AutoFS/Webapp/templates/index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>AutoFS Leaderboard</title>
+    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
+    <style>
+        :root {
+            --primary-color: #3498db;
+            --secondary-color: #2c3e50;
+            --background-color: #f8f9fa;
+            --text-color: #333;
+            --border-color: #dee2e6;
+            --hover-color: #f1f1f1;
+        }
+
+        body {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            margin: 0;
+            padding: 20px;
+            background-color: var(--background-color);
+            color: var(--text-color);
+        }
+
+        .container {
+            max-width: 1200px;
+            margin: 0 auto;
+            background: white;
+            padding: 20px;
+            border-radius: 8px;
+            box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+        }
+
+        header {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            margin-bottom: 20px;
+            border-bottom: 2px solid var(--primary-color);
+            padding-bottom: 10px;
+        }
+
+        h1 {
+            margin: 0;
+            color: var(--secondary-color);
+        }
+
+        .controls {
+            display: flex;
+            gap: 10px;
+            align-items: center;
+        }
+
+        select {
+            padding: 8px 12px;
+            border: 1px solid var(--border-color);
+            border-radius: 4px;
+            font-size: 14px;
+        }
+
+        table {
+            width: 100%;
+            border-collapse: collapse;
+            margin-top: 10px;
+        }
+
+        th, td {
+            padding: 12px 15px;
+            text-align: left;
+            border-bottom: 1px solid var(--border-color);
+        }
+
+        th {
+            background-color: var(--secondary-color);
+            color: white;
+            cursor: pointer;
+            user-select: none;
+            position: sticky;
+            top: 0;
+        }
+
+        th:hover {
+            background-color: #34495e;
+        }
+
+        th .arrow {
+            font-size: 10px;
+            margin-left: 5px;
+            opacity: 0.7;
+        }
+
+        tr:hover {
+            background-color: var(--hover-color);
+        }
+
+        .score-bar {
+            height: 6px;
+            background-color: #e9ecef;
+            border-radius: 3px;
+            overflow: hidden;
+            margin-top: 5px;
+        }
+
+        .score-fill {
+            height: 100%;
+            background-color: var(--primary-color);
+        }
+
+        .features-cell {
+            max-width: 200px;
+            white-space: nowrap;
+            overflow: hidden;
+            text-overflow: ellipsis;
+            color: #666;
+            font-size: 0.9em;
+            cursor: pointer;
+        }
+        
+        .features-cell:hover {
+            text-decoration: underline;
+            color: var(--primary-color);
+        }
+
+        /* Modal styles */
+        .modal {
+            display: none;
+            position: fixed;
+            z-index: 1000;
+            left: 0;
+            top: 0;
+            width: 100%;
+            height: 100%;
+            background-color: rgba(0,0,0,0.5);
+        }
+
+        .modal-content {
+            background-color: white;
+            margin: 10% auto;
+            padding: 20px;
+            border-radius: 8px;
+            width: 50%;
+            max-width: 600px;
+            box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+        }
+
+        .close {
+            color: #aaa;
+            float: right;
+            font-size: 28px;
+            font-weight: bold;
+            cursor: pointer;
+        }
+
+        .close:hover {
+            color: black;
+        }
+
+        .feature-tag {
+            display: inline-block;
+            background-color: #e1ecf4;
+            color: #2c3e50;
+            padding: 4px 8px;
+            border-radius: 4px;
+            margin: 2px;
+            font-size: 0.9em;
+        }
+        
+        .loading {
+            text-align: center;
+            padding: 20px;
+            color: #666;
+        }
+    </style>
+</head>
+<body>
+
+<div class="container">
+    <header>
+        <h1>🏆 AutoFS Leaderboard</h1>
+        <div class="controls">
+            <label for="dataset-select">Dataset:</label>
+            <select id="dataset-select">
+                <option value="" disabled selected>Loading...</option>
+            </select>
+        </div>
+    </header>
+
+    <div id="loading-indicator" class="loading" style="display: none;">Loading data...</div>
+
+    <div class="chart-controls" style="text-align:center; margin-top: 20px; margin-bottom: 15px;">
+        <label style="margin-right:15px; font-weight:bold;">View Mode:</label>
+        <input type="radio" id="view-overall" name="chart-view" value="overall" checked onchange="updateView()">
+        <label for="view-overall" style="margin-right:10px;">Overall (Mean)</label>
+        
+        <input type="radio" id="view-classifiers-f1" name="chart-view" value="classifiers-f1" onchange="updateView()">
+        <label for="view-classifiers-f1" style="margin-right:10px;">F1 by Classifier</label>
+        
+        <input type="radio" id="view-classifiers-auc" name="chart-view" value="classifiers-auc" onchange="updateView()">
+        <label for="view-classifiers-auc">AUC by Classifier</label>
+    </div>
+
+    <div class="charts-container" style="display: flex; gap: 20px; margin-bottom: 20px;">
+        <div style="flex: 1; background: white; padding: 15px; border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+            <canvas id="scoreChart"></canvas>
+        </div>
+        <div style="flex: 1; background: white; padding: 15px; border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+            <canvas id="timeChart"></canvas>
+        </div>
+    </div>
+
+    <table id="result-table">
+        <thead>
+            <!-- Headers generated dynamically -->
+        </thead>
+        <tbody>
+            <!-- Data rows will be populated here -->
+        </tbody>
+    </table>
+</div>
+
+<!-- Modal for details -->
+<div id="details-modal" class="modal">
+    <div class="modal-content">
+        <span class="close">&times;</span>
+        <h2 id="modal-title">Algorithm Details</h2>
+        <div id="modal-body"></div>
+    </div>
+</div>
+
+<script>
+    let currentResults = [];
+    let sortDirection = 1; // 1 for asc, -1 for desc
+    let lastSortKey = '';
+
+    const VIEW_CONFIG = {
+        'overall': [
+            { key: 'mean_f1', label: 'Mean F1' },
+            { key: 'mean_auc', label: 'Mean AUC' }
+        ],
+        'classifiers-f1': [
+            { key: 'metrics.nb.f1', label: 'NB F1' },
+            { key: 'metrics.svm.f1', label: 'SVM F1' },
+            { key: 'metrics.rf.f1', label: 'RF F1' }
+        ],
+        'classifiers-auc': [
+            { key: 'metrics.nb.auc', label: 'NB AUC' },
+            { key: 'metrics.svm.auc', label: 'SVM AUC' },
+            { key: 'metrics.rf.auc', label: 'RF AUC' }
+        ]
+    };
+
+    const tableHead = document.querySelector("#result-table thead");
+    const tableBody = document.querySelector("#result-table tbody");
+    const datasetSelect = document.getElementById("dataset-select");
+    const loadingIndicator = document.getElementById("loading-indicator");
+    const modal = document.getElementById("details-modal");
+    const closeModal = document.querySelector(".close");
+
+    // Close modal
+    closeModal.onclick = () => modal.style.display = "none";
+    window.onclick = (event) => {
+        if (event.target == modal) modal.style.display = "none";
+    }
+
+    // Global chart instances
+    let scoreChartInstance = null;
+    let timeChartInstance = null;
+
+    function updateCharts(results) {
+        if (!Array.isArray(results) || results.length === 0) return;
+
+        // Limit to top 15 for readability
+        const topResults = results.slice(0, 15);
+        const labels = topResults.map(r => r.algorithm || 'Unknown');
+        const times = topResults.map(r => r.time || 0);
+
+        const viewMode = document.querySelector('input[name="chart-view"]:checked').value;
+        let datasets = [];
+
+        if (viewMode === 'overall') {
+            const f1Scores = topResults.map(r => r.mean_f1 || 0);
+            const aucScores = topResults.map(r => r.mean_auc || 0);
+            datasets = [
+                {
+                    label: 'Mean F1',
+                    data: f1Scores,
+                    backgroundColor: 'rgba(52, 152, 219, 0.7)',
+                    borderColor: 'rgba(52, 152, 219, 1)',
+                    borderWidth: 1
+                },
+                {
+                    label: 'Mean AUC',
+                    data: aucScores,
+                    backgroundColor: 'rgba(46, 204, 113, 0.7)',
+                    borderColor: 'rgba(46, 204, 113, 1)',
+                    borderWidth: 1
+                }
+            ];
+        } else if (viewMode === 'classifiers-f1') {
+            const classifiers = ['nb', 'svm', 'rf'];
+            const colors = ['rgba(255, 206, 86, 0.5)', 'rgba(75, 192, 192, 0.5)', 'rgba(153, 102, 255, 0.5)'];
+            const borderColors = ['rgba(255, 206, 86, 1)', 'rgba(75, 192, 192, 1)', 'rgba(153, 102, 255, 1)'];
+            
+            datasets = classifiers.map((cls, idx) => ({
+                label: cls.toUpperCase() + ' F1',
+                data: topResults.map(r => (r.metrics && r.metrics[cls]) ? r.metrics[cls].f1 : 0),
+                backgroundColor: colors[idx],
+                borderColor: borderColors[idx],
+                borderWidth: 1
+            }));
+        } else if (viewMode === 'classifiers-auc') {
+            const classifiers = ['nb', 'svm', 'rf'];
+            const colors = ['rgba(255, 206, 86, 0.5)', 'rgba(75, 192, 192, 0.5)', 'rgba(153, 102, 255, 0.5)'];
+            const borderColors = ['rgba(255, 206, 86, 1)', 'rgba(75, 192, 192, 1)', 'rgba(153, 102, 255, 1)'];
+            
+            datasets = classifiers.map((cls, idx) => ({
+                label: cls.toUpperCase() + ' AUC',
+                data: topResults.map(r => (r.metrics && r.metrics[cls]) ? r.metrics[cls].auc : 0),
+                backgroundColor: colors[idx],
+                borderColor: borderColors[idx],
+                borderWidth: 1
+            }));
+        }
+
+        // Score Chart
+        const scoreCtx = document.getElementById('scoreChart').getContext('2d');
+        if (scoreChartInstance) scoreChartInstance.destroy();
+        
+        scoreChartInstance = new Chart(scoreCtx, {
+            type: 'bar',
+            data: {
+                labels: labels,
+                datasets: datasets
+            },
+            options: {
+                responsive: true,
+                maintainAspectRatio: false,
+                plugins: {
+                    title: {
+                        display: true,
+                        text: viewMode === 'overall' ? 'Top Algorithms Performance (Mean)' : 
+                              (viewMode === 'classifiers-f1' ? 'F1-Score by Classifier' : 'AUC by Classifier')
+                    }
+                },
+                scales: {
+                    y: {
+                        beginAtZero: false,
+                        // min: 0.8
+                    }
+                }
+            }
+        });
+
+        // Time Chart
+        const timeCtx = document.getElementById('timeChart').getContext('2d');
+        if (timeChartInstance) timeChartInstance.destroy();
+
+        timeChartInstance = new Chart(timeCtx, {
+            type: 'line',
+            data: {
+                labels: labels,
+                datasets: [{
+                    label: 'Time (s)',
+                    data: times,
+                    backgroundColor: 'rgba(231, 76, 60, 0.2)',
+                    borderColor: 'rgba(231, 76, 60, 1)',
+                    borderWidth: 2,
+                    tension: 0.3,
+                    fill: true
+                }]
+            },
+            options: {
+                responsive: true,
+                maintainAspectRatio: false,
+                plugins: {
+                    title: { display: true, text: 'Execution Time' }
+                },
+                scales: {
+                    y: { beginAtZero: true }
+                }
+            }
+        });
+    }
+
+    function showDetails(result) {
+        const title = document.getElementById("modal-title");
+        const body = document.getElementById("modal-body");
+        
+        title.textContent = `${result.algorithm} Details`;
+        
+        let featuresHtml = result.selected_features.map(f => 
+            `<span class="feature-tag">${f}</span>`
+        ).join('');
+
+        let metricsHtml = '<div style="margin-top: 15px;"><h3>Metrics Breakdown</h3>';
+        for (const [clf, m] of Object.entries(result.metrics || {})) {
+            metricsHtml += `
+                <div style="margin-bottom: 10px;">
+                    <strong>${clf.toUpperCase()}:</strong> 
+                    F1: ${m.f1.toFixed(4)}, AUC: ${m.auc.toFixed(4)}
+                </div>`;
+        }
+        metricsHtml += '</div>';
+
+        body.innerHTML = `
+            <p><strong>Time:</strong> ${result.time.toFixed(4)}s</p>
+            <p><strong>Num Features:</strong> ${result.num_features}</p>
+            <p><strong>Selected Features (${result.selected_features.length}):</strong></p>
+            <div>${featuresHtml}</div>
+            ${metricsHtml}
+        `;
+        
+        modal.style.display = "block";
+    }
+
+    function getValue(obj, path) {
+        if (!path) return undefined;
+        return path.split('.').reduce((acc, part) => (acc && acc[part] !== undefined) ? acc[part] : undefined, obj);
+    }
+
+    function safeFixed(value, digits=4) {
+        if (value === undefined || value === null) return 'N/A';
+        return Number(value).toFixed(digits);
+    }
+
+    function renderTableHeader() {
+        const viewMode = document.querySelector('input[name="chart-view"]:checked').value;
+        const dynamicCols = VIEW_CONFIG[viewMode] || VIEW_CONFIG['overall'];
+
+        let html = '<tr>';
+        html += '<th data-key="rank" style="width: 60px;">#</th>';
+        html += '<th data-key="algorithm">Algorithm <span class="arrow">↕</span></th>';
+        
+        dynamicCols.forEach(col => {
+            html += `<th data-key="${col.key}">${col.label} <span class="arrow">↕</span></th>`;
+        });
+
+        html += '<th data-key="time">Time (s) <span class="arrow">↕</span></th>';
+        html += '<th data-key="selected_features">Selected Features</th>';
+        html += '</tr>';
+
+        tableHead.innerHTML = html;
+
+        // Re-attach sort listeners
+        tableHead.querySelectorAll('th[data-key]').forEach(th => {
+            th.addEventListener('click', () => sortTable(th.dataset.key));
+        });
+    }
+
+    function updateTable(results) {
+        tableBody.innerHTML = "";
+        
+        // Safety check
+        if (!Array.isArray(results)) {
+            tableBody.innerHTML = '<tr><td colspan="10" style="text-align:center; color:red;">Error: Invalid data format</td></tr>';
+            return;
+        }
+
+        if (results.length === 0) {
+            tableBody.innerHTML = '<tr><td colspan="10" style="text-align:center;">No results found</td></tr>';
+            return;
+        }
+
+        const viewMode = document.querySelector('input[name="chart-view"]:checked').value;
+        const dynamicCols = VIEW_CONFIG[viewMode] || VIEW_CONFIG['overall'];
+
+        results.forEach((r, idx) => {
+            const row = document.createElement("tr");
+            
+            // Format features for preview
+            const featurePreview = (r.selected_features && Array.isArray(r.selected_features)) 
+                ? r.selected_features.slice(0, 5).join(', ') + (r.selected_features.length > 5 ? '...' : '')
+                : 'N/A';
+            
+            let html = `<td>${idx + 1}</td>`;
+            html += `<td><strong>${r.algorithm || 'Unknown'}</strong></td>`;
+
+            dynamicCols.forEach(col => {
+                const val = getValue(r, col.key);
+                const score = val !== undefined ? val : 0;
+                html += `
+                    <td>
+                        ${safeFixed(val)}
+                        <div class="score-bar"><div class="score-fill" style="width: ${Math.min(score * 100, 100)}%"></div></div>
+                    </td>`;
+            });
+
+            const time = r.time || 0;
+            html += `<td>${safeFixed(time, 2)}</td>`;
+            html += `
+                <td class="features-cell" onclick="showDetails(currentResults[${idx}])" title="Click for details">
+                    ${featurePreview} <span style="font-size:0.8em; color:#999;">(Click for details)</span>
+                </td>`;
+            
+            row.innerHTML = html;
+            tableBody.appendChild(row);
+        });
+    }
+
+    function sortTable(key) {
+        if (lastSortKey === key) {
+            sortDirection *= -1;
+        } else {
+            sortDirection = key === 'time' || key === 'rank' ? 1 : -1;
+            lastSortKey = key;
+        }
+
+        // We don't call renderTableHeader here because it resets the sort indicators if we rebuild entirely.
+        // Instead, we just update the arrows.
+        document.querySelectorAll('th .arrow').forEach(span => span.textContent = '↕');
+        const activeHeader = document.querySelector(`th[data-key="${key}"] .arrow`);
+        if (activeHeader) activeHeader.textContent = sortDirection === 1 ? '↑' : '↓';
+
+        const sorted = [...currentResults].sort((a, b) => {
+            let valA = getValue(a, key);
+            let valB = getValue(b, key);
+            
+            if (key === 'rank') return 0;
+
+            if (valA === undefined) valA = -Infinity;
+            if (valB === undefined) valB = -Infinity;
+
+            if (valA < valB) return -1 * sortDirection;
+            if (valA > valB) return 1 * sortDirection;
+            return 0;
+        });
+        
+        // Don't update currentResults global if it breaks things, but here it's fine.
+        // Actually, let's keep currentResults as the master list? 
+        // No, currentResults should be the sorted list for consistent subsequent sorts.
+        currentResults = sorted; 
+        updateTable(sorted);
+    }
+
+    function updateView() {
+        renderTableHeader();
+        updateTable(currentResults);
+        updateCharts(currentResults);
+    }
+
+    function fetchResults(dataset) {
+        loadingIndicator.style.display = 'block';
+        tableBody.innerHTML = '';
+        
+        console.log("Fetching results for:", dataset);
+        fetch(`/api/results?dataset=${dataset}`)
+            .then(res => {
+                if (!res.ok) throw new Error("Network response was not ok");
+                return res.json();
+            })
+            .then(data => {
+                console.log("Data received:", data);
+                currentResults = data;
+                updateView();
+                loadingIndicator.style.display = 'none';
+            })
+            .catch(err => {
+                console.error("Error fetching results:", err);
+                loadingIndicator.textContent = "Error loading data. Make sure the server is running.";
+            });
+    }
+
+    // Initialize
+    document.addEventListener("DOMContentLoaded", () => {
+        // Setup sort listeners
+        document.querySelectorAll('th[data-key]').forEach(th => {
+            th.addEventListener('click', () => sortTable(th.dataset.key));
+        });
+
+        // Load datasets
+        fetch("/api/datasets")
+            .then(res => res.json())
+            .then(datasets => {
+                datasetSelect.innerHTML = "";
+                datasets.forEach(ds => {
+                    const option = document.createElement("option");
+                    option.value = ds;
+                    option.textContent = ds;
+                    datasetSelect.appendChild(option);
+                });
+
+                if (datasets.includes("Authorship")) {
+                    datasetSelect.value = "Authorship";
+                    fetchResults("Authorship");
+                } else if (datasets.length > 0) {
+                    datasetSelect.value = datasets[0];
+                    fetchResults(datasets[0]);
+                }
+            })
+            .catch(err => {
+                console.error("Error fetching datasets:", err);
+                datasetSelect.innerHTML = "<option>Error loading datasets</option>";
+            });
+
+        datasetSelect.addEventListener('change', (e) => {
+            fetchResults(e.target.value);
+        });
+    });
+</script>
+
+</body>
+</html>

FeatureSelect-Methods-Leaderboard/AutoFS/Webapp/templates/index1.html

@@ -0,0 +1,212 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>AutoFS Leaderboard</title>
+
+    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
+
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            margin: 40px;
+        }
+
+        table {
+            border-collapse: collapse;
+            width: 100%;
+        }
+
+        th, td {
+            border: 1px solid #ddd;
+            padding: 10px;
+            text-align: center;
+        }
+
+        th {
+            cursor: pointer;
+            background-color: #f5f5f5;
+            user-select: none;
+        }
+
+        th span {
+            margin-left: 6px;
+            font-size: 12px;
+            opacity: 0.7;
+        }
+
+        tr:nth-child(even) {
+            background-color: #fafafa;
+        }
+
+        .chart-row {
+            display: flex;
+            gap: 40px;
+            margin-top: 40px;
+        }
+
+        .chart-container {
+            width: 50%;
+        }
+
+        .chart-row-single {
+            display: flex;
+            justify-content: center;
+            margin-top: 40px;
+        }
+
+        .chart-container-single {
+            width: 60%;
+        }
+    </style>
+</head>
+<body>
+
+<h1>Feature Selection Leaderboard</h1>
+
+<table>
+    <thead>
+    <tr>
+        <th>Rank</th>
+        <th onclick="sortTable('algorithm')">Algorithm <span id="arrow-algorithm">↕</span></th>
+        <th onclick="sortTable('num_features')">#Features <span id="arrow-num_features">↕</span></th>
+        <th onclick="sortTable('mean_f1')">Mean F1 <span id="arrow-mean_f1">↕</span></th>
+        <th onclick="sortTable('mean_auc')">Mean AUC <span id="arrow-mean_auc">↕</span></th>
+        <th onclick="sortTable('time')">Time (s) <span id="arrow-time">↕</span></th>
+    </tr>
+    </thead>
+    <tbody id="tbody"></tbody>
+</table>
+
+<!-- F1 & AUC -->
+<div class="chart-row">
+    <div class="chart-container">
+        <canvas id="f1Chart"></canvas>
+    </div>
+    <div class="chart-container">
+        <canvas id="aucChart"></canvas>
+    </div>
+</div>
+
+<!-- Time -->
+<div class="chart-row-single">
+    <div class="chart-container-single">
+        <canvas id="timeChart"></canvas>
+    </div>
+</div>
+
+<script>
+    let leaderboardData = {{ leaderboard | tojson }};
+    let sortKey = null;
+    let sortAsc = true;
+    let f1Chart, aucChart, timeChart;
+
+    const metricOrder = {
+        algorithm: "asc",     // 字符串
+        num_features: "asc",  // 少特征更好（你也可以改）
+        mean_f1: "desc",      // 越大越好
+        mean_auc: "desc",     // 越大越好
+        time: "asc"           // 越小越好 ✅
+    };
+
+    function renderTable() {
+        const tbody = document.getElementById("tbody");
+        tbody.innerHTML = "";
+
+        const n = leaderboardData.length;
+
+        const isBestFirst =
+            (metricOrder[sortKey] === "desc" && !sortAsc) ||
+            (metricOrder[sortKey] === "asc" && sortAsc);
+
+        leaderboardData.forEach((r, i) => {
+            const rank = isBestFirst ? i + 1 : n - i;
+
+            tbody.insertAdjacentHTML("beforeend", `
+                <tr>
+                    <td>${rank}</td>
+                    <td>${r.algorithm}</td>
+                    <td>${r.num_features}</td>
+                    <td>${r.mean_f1.toFixed(4)}</td>
+                    <td>${r.mean_auc.toFixed(4)}</td>
+                    <td>${r.time.toFixed(2)}</td>
+                </tr>
+            `);
+        });
+    }
+
+    function sortTable(key) {
+        if (sortKey === key) {
+            sortAsc = !sortAsc;
+        } else {
+            sortKey = key;
+            sortAsc = metricOrder[key] === "asc";
+        }
+
+        leaderboardData.sort((a, b) => {
+            if (typeof a[key] === "string") {
+                return sortAsc
+                    ? a[key].localeCompare(b[key])
+                    : b[key].localeCompare(a[key]);
+            }
+            return sortAsc ? a[key] - b[key] : b[key] - a[key];
+        });
+
+        updateArrows();
+        renderTable();
+        updateCharts();
+    }
+
+    function updateArrows() {
+        document.querySelectorAll("th span").forEach(s => s.textContent = "↕");
+        document.getElementById("arrow-" + sortKey).textContent = sortAsc ? "↑" : "↓";
+    }
+
+    function updateCharts() {
+        const labels = leaderboardData.map(r => r.algorithm);
+
+        if (f1Chart) f1Chart.destroy();
+        if (aucChart) aucChart.destroy();
+        if (timeChart) timeChart.destroy();
+
+        const baseOptions = title => ({
+            responsive: true,
+            maintainAspectRatio: true,
+            aspectRatio: 2,
+            plugins: { title: { display: true, text: title } }
+        });
+
+        f1Chart = new Chart(document.getElementById("f1Chart"), {
+            type: "line",
+            data: {
+                labels,
+                datasets: [{ label: "Mean F1", data: leaderboardData.map(r => r.mean_f1) }]
+            },
+            options: baseOptions("Mean F1 Curve")
+        });
+
+        aucChart = new Chart(document.getElementById("aucChart"), {
+            type: "line",
+            data: {
+                labels,
+                datasets: [{ label: "Mean AUC", data: leaderboardData.map(r => r.mean_auc) }]
+            },
+            options: baseOptions("Mean AUC Curve")
+        });
+
+        timeChart = new Chart(document.getElementById("timeChart"), {
+            type: "line",
+            data: {
+                labels,
+                datasets: [{ label: "Time (s)", data: leaderboardData.map(r => r.time) }]
+            },
+            options: baseOptions("Runtime Curve")
+        });
+    }
+
+    renderTable();
+    updateCharts();
+</script>
+
+</body>
+</html>

FeatureSelect-Methods-Leaderboard/AutoFS/Webapp/templates/index11.html

@@ -0,0 +1,136 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Feature Selection Leaderboard</title>
+    <style>
+        body { font-family: Arial, sans-serif; margin: 20px; }
+        table { border-collapse: collapse; width: 100%; margin-top: 20px; }
+        th, td { border: 1px solid #ddd; padding: 8px; text-align: center; }
+        th { cursor: pointer; background-color: #f2f2f2; position: relative; }
+        th .arrow { font-size: 12px; margin-left: 4px; }
+        select { padding: 5px; margin-bottom: 10px; }
+    </style>
+</head>
+<body>
+
+<h1>Feature Selection Leaderboard</h1>
+
+<label for="dataset-select">Select Dataset:</label>
+<select id="dataset-select">
+    <!-- 这里的 options 会在后端渲染或者 JS 初始化 -->
+</select>
+
+<table id="result-table">
+    <thead>
+        <tr>
+            <th data-key="rank">Rank <span class="arrow">↑↓</span></th>
+            <th data-key="algorithm">Algorithm <span class="arrow">↑↓</span></th>
+            <th data-key="num_features">Num Features <span class="arrow">↑↓</span></th>
+            <th data-key="mean_f1">Mean F1 <span class="arrow">↑↓</span></th>
+            <th data-key="mean_auc">Mean AUC <span class="arrow">↑↓</span></th>
+            <th data-key="time">Time <span class="arrow">↑↓</span></th>
+        </tr>
+    </thead>
+    <tbody>
+        <!-- 数据行由 JS 填充 -->
+    </tbody>
+</table>
+
+<script>
+    // 全局变量存储当前表格数据
+    let currentResults = [];
+
+    // 渲染表格
+    function updateTable(results) {
+        const tbody = document.querySelector("#result-table tbody");
+        tbody.innerHTML = "";
+        currentResults = results; // 保存全局，用于排序
+
+        results.forEach((r, idx) => {
+            const row = document.createElement("tr");
+            row.innerHTML = `
+                <td>${idx + 1}</td>
+                <td>${r.algorithm}</td>
+                <td>${r.num_features}</td>
+                <td>${r.mean_f1.toFixed(4)}</td>
+                <td>${r.mean_auc.toFixed(4)}</td>
+                <td>${r.time.toFixed(2)}</td>
+            `;
+            tbody.appendChild(row);
+        });
+    }
+
+    // 获取数据
+    function fetchResults(dataset) {
+        console.log("[DEBUG] Fetching dataset:", dataset);
+        fetch(`/api/results?dataset=${dataset}`)
+          .then(res => res.json())
+          .then(data => {
+              console.log("[DEBUG] Fetched results:", data);
+              updateTable(data);
+          })
+          .catch(err => console.error(err));
+    }
+
+    // 初始化下拉框和默认数据集
+    document.addEventListener("DOMContentLoaded", () => {
+        const select = document.getElementById("dataset-select");
+
+        // 从后端获取可用数据集列表
+        fetch("/api/datasets")
+          .then(res => res.json())
+          .then(datasets => {
+              datasets.forEach(ds => {
+                  const option = document.createElement("option");
+                  option.value = ds;
+                  option.textContent = ds;
+                  select.appendChild(option);
+              });
+
+              // 默认加载 Authorship，如果存在
+              if (datasets.includes("Authorship")) {
+                  select.value = "Authorship";
+                  fetchResults("Authorship");
+              } else if (datasets.length > 0) {
+                  select.value = datasets[0];
+                  fetchResults(datasets[0]);
+              }
+          });
+
+        // 绑定选择事件
+        select.addEventListener("change", () => {
+            fetchResults(select.value);
+        });
+
+        // 绑定表头点击排序
+        document.querySelectorAll("#result-table th").forEach(th => {
+            th.addEventListener("click", () => {
+                const key = th.dataset.key;
+                if (!key) return;
+                sortTable(key);
+            });
+        });
+    });
+
+    // 排序函数
+    let sortAsc = true;
+    function sortTable(key) {
+        currentResults.sort((a, b) => {
+            let valA = a[key], valB = b[key];
+
+            // time 值越小越好，其余指标越大越好
+            if (key === "time") {
+                return sortAsc ? valA - valB : valB - valA;
+            } else {
+                return sortAsc ? valB - valA : valA - valB;
+            }
+        });
+        // 切换排序方向
+        sortAsc = !sortAsc;
+        updateTable(currentResults);
+    }
+</script>
+
+</body>
+</html>

FeatureSelect-Methods-Leaderboard/AutoFS/Webapp/templates/indexa.html

@@ -0,0 +1,434 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <title>FeatureSelect Leaderboard</title>
+
+    <!-- Google tag (gtag.js) -->
+    <!-- <script async src="https://www.googletagmanager.com/gtag/js?id=G-VWV023WWP4"></script> -->
+    <!-- <script>
+        window.dataLayer = window.dataLayer || [];
+
+        function gtag() {
+            dataLayer.push(arguments);
+        }
+
+        gtag('js', new Date());
+
+        gtag('config', 'G-VWV023WWP4');
+    </script> -->
+
+    <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css">
+    <link rel="icon" href="https://raw.githubusercontent.com/tatsu-lab/alpaca_eval/main/docs/AlpacaFarm_small.png">
+    <link href="https://cdn.jsdelivr.net/css-toggle-switch/latest/toggle-switch.css" rel="stylesheet"/>
+
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            margin: 0;
+            padding: 50px 20px;
+            background-color: #FFFFFF;
+            color: #000000;
+        }
+
+        .container {
+            max-width: 700px;
+            margin: auto;
+        }
+
+        #branding {
+            text-align: center;
+            margin-bottom: 20px;
+        }
+
+        #branding h1 {
+            margin: 0;
+            font-size: 2em;
+        }
+
+        h2 {
+            margin: 0;
+            font-size: 1.2em;
+            color: #777;
+        }
+
+        table {
+            max-width: 700px;
+            width: 100%;
+            table-layout: fixed;
+            margin: auto;
+            font-size: 1em;
+        }
+
+        table th,
+        table td {
+            padding: 6px;
+            word-wrap: break-word;
+            vertical-align: middle;
+        }
+
+        table th {
+            border-bottom: 2px solid #000;
+        }
+
+        th.rank, td.rank {
+            width: 9%; /* Adjust as needed */
+            padding-left: 10px; /* Small margin */
+            text-align: left;
+        }
+
+        th.name, td.name {
+            width: 55%;
+            padding-left: 30px;
+            text-align: left;
+        }
+
+        th:not(.rank):not(.name),
+        td:not(.rank):not(.name) {
+            text-align: right;
+            padding-right: 10px;
+        }
+
+        th.winRate, td.winRate {
+            width: 17%;
+            padding-right: 30px;
+        }
+
+        th {
+            text-align: right;
+            padding-bottom: 15px;
+        }
+
+        td {
+            padding-bottom: 10px;
+        }
+
+        #leaderboard tr th.winRate,
+        #leaderboard tr td.winRate {
+            color: #999999;
+        }
+
+        #leaderboard tr th.rank,
+        #leaderboard tr td.rank {
+            color: #999999;
+        }
+
+        table tr:nth-child(even) {
+            background-color: #E8E8E8;
+        }
+
+        table tr:nth-child(odd) {
+            background-color: #F8F8F8;
+        }
+
+        .switch-toggle {
+            display: inline-block;
+            vertical-align: middle;
+        }
+
+        .switch-toggle input + label {
+            padding: 2px;
+            padding-left: 7px;
+            padding-right: 7px;
+            cursor: pointer;
+            background-color: lightgrey;
+            border: 1px solid transparent;
+            font-size: 16px;
+        }
+
+        .switch-toggle input:checked + label {
+            border-color: green;
+            color: green;
+        }
+
+        .switch-toggle input:not(:checked) + label {
+            color: black;
+            box-shadow: none !important;
+            user-select: none;
+        }
+
+
+        .toggle-line {
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            margin-bottom: 20px;
+            font-size: 17px;
+        }
+
+        .toggle-line .switch-toggle {
+            margin: 0 10px;
+        }
+    </style>
+    <!-- <script src="https://cdnjs.cloudflare.com/ajax/libs/PapaParse/5.3.0/papaparse.min.js"></script> -->
+</head>
+
+<body>
+<div class="container">
+    <div id="branding">
+
+        <h1>FeatureSelect
+            <!-- <a href="https://github.com/tatsu-lab/alpaca_eval/tree/main">
+                <img src="https://raw.githubusercontent.com/tatsu-lab/alpaca_eval/main/docs/AlpacaFarm_small.png"
+                     alt="Logo" style="height: 2em; vertical-align: middle;"></a> -->
+            Leaderboard
+        </h1>
+        <br>
+        <h2>An Automatic Evaluator for FeatureSelect Methods</h2>
+<!--        <small id="alpaca_eval_info" style="color: #777;">-->
+<!--            Baseline: GPT-4 Preview &nbsp; | &nbsp; Auto-annotator: GPT-4 Preview-->
+<!--        </small>-->
+<!--        <br>-->
+        <small id="caution" style="color: #8C1515;">
+            <b> Length-controlled</b> (LC) win rates alleviate length biases of GPT-4, but it may favor models finetuned on its outputs.
+        </small>
+        <br>
+        <a href="https://github.com/Fss2652530458/AutoFS">
+            <img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub logo" style="height: 1.5em;/* margin-bottom: 0; */">
+        </a>
+    </div>
+
+
+<!--           选择器                    -->
+    <div class="toggle-line">
+
+        Version:
+        <div class="switch-toggle switch-evaluator" style="margin-right: 4em">
+            <input id="alpaca_eval" name="version" type="radio"/>
+            <label for="alpaca_eval" onclick="">AlpacaEval</label>
+            <input id="alpaca_eval_2" name="version" type="radio" checked="checked"/>
+            <label for="alpaca_eval_2" onclick="">AlpacaEval 2.0</label>
+        </div>
+
+        Filter:
+        <div class="switch-toggle switch-filter">
+            <input id="community" name="filter" type="radio"/>
+            <label for="community" onclick="">Community</label>
+            <input id="verified" name="filter" type="radio" checked="checked"/>
+            <label for="verified" onclick="">Verified</label>
+<!--            <input id="minimal" name="compactness" type="radio"/>-->
+<!--            <label for="minimal" onclick="">Minimal</label>-->
+        </div>
+
+
+
+    </div>
+<!--            Baseline小灰字-->
+    <div class="container" style="text-align: center; margin-bottom: 10px; margin-top: -10px;">
+        <small id="alpaca_eval_info" style="color: #777;">
+            Baseline: GPT-4 Preview (11/06) &nbsp; | &nbsp; Auto-annotator: GPT-4 Preview (11/06)
+        </small>
+    </div>
+
+
+<!--            排行榜本体-->
+    <table id="leaderboard">
+        <tr>
+            <th class="rank">Rank</th>
+            <th class="name" onclick="sortTable('algorithm')">Algorithm <span id="arrow-algorithm">↕</span></th>
+            <th class="lenWinRate" onclick="sortTable('num_features')">#Features <span id="arrow-num_features">↕</span></th>
+            <th class="winRate" onclick="sortTable('mean_f1')">Mean F1 <span id="arrow-mean_f1">↕</span></th>
+            <th class="winRate" onclick="sortTable('mean_auc')">Mean AUC <span id="arrow-mean_auc">↕</span></th>
+            <th class="winRate" onclick="sortTable('time')">Time (s) <span id="arrow-time">↕</span></th>
+        </tr>
+    </table>
+
+    <!--            文档简介-->
+    <div id="documentation">
+        <div style="text-align: center;">
+            <a href="https://github.com/tatsu-lab/alpaca_eval" style="display: inline-block;">
+                <i class="fab fa-fw fa-github" aria-hidden="true"></i> Github
+            </a>
+        </div>
+        <br>
+        <h2>About AlpacaEval</h2>
+        <p>
+            <a href="https://github.com/tatsu-lab/alpaca_eval" target="_blank">AlpacaEval</a>
+            an LLM-based automatic evaluation that is fast, cheap, and reliable.
+            It is based on the
+            <a href="https://crfm.stanford.edu/2023/05/22/alpaca-farm.html">AlpacaFarm</a>
+            evaluation set,
+            which tests the ability of models to follow general user instructions.
+            These responses are then compared to reference responses (Davinci003 for AlpacaEval, GPT-4 Preview for AlpacaEval 2.0) by
+            the provided GPT-4 based auto-annotators,
+            which results in the win rates presented above.
+            AlpacaEval displays a high agreement rate with ground truth human annotations,
+            and leaderboard rankings on AlpacaEval are very correlated with leaderboard rankings
+            based on human annotators.
+            Please see our
+            <a href="https://github.com/tatsu-lab/alpaca_eval#analysis" target="_blank">documentation</a>
+            for more details on our analysis.
+        </p>
+        <h2>Adding new models</h2>
+        <p>
+            We welcome new model contributions to the leaderboard from the community!
+            To do so, please follow the steps in the
+            <a href="https://github.com/tatsu-lab/alpaca_eval#contributing" target="_blank">contributions
+                section</a>.
+            Specifically, you'll need to run the model on the evaluation set,
+            auto-annotate the outputs, and submit a PR with the model config and leaderboard results.
+            We've also set up a
+            <a href="https://discord.gg/GJMxJSVZZM" target="_blank">Discord</a>
+            for community support and discussion.
+        </p>
+        <h2>Adding new evaluators or eval sets </h2>
+        <p>
+            We also welcome contributions for new evaluators or new eval sets!
+            For making new evaluators, we release our ground-truth
+            <a href="https://github.com/tatsu-lab/alpaca_eval#data-release" target="_blank">human annotations</a>
+            and <a href="https://github.com/tatsu-lab/alpaca_eval#analyzing-an-evaluator" target="_blank">comparison
+            metrics</a>.
+            We also release a
+            <a href="https://github.com/tatsu-lab/alpaca_eval#analyzing-an-eval-set" target="_blank">rough guide</a>
+            to follow for making new eval sets.
+            We specifically encourage contributions for harder instructions distributions and for safety testing of
+            LLMs.
+        </p>
+        <h2>AlpacaEval limitations</h2>
+        <p>
+           这里是简介
+        </p>
+    </div>
+
+</div>
+
+<script>
+    const alpacaEvalRadio = document.getElementById('alpaca_eval');
+    const alpacaEval2Radio = document.getElementById('alpaca_eval_2');
+
+    const communityRadio = document.getElementById('community');
+    const verifiedRadio = document.getElementById('verified');
+    // const minimalRadio = document.getElementById('minimal');
+
+    const table = document.getElementById('leaderboard');
+
+    const urls = {
+        'alpaca_eval': 'https://raw.githubusercontent.com/tatsu-lab/alpaca_eval/main/docs/data_AlpacaEval/alpaca_eval_gpt4_leaderboard.csv',
+        'alpaca_eval_2': 'https://raw.githubusercontent.com/tatsu-lab/alpaca_eval/main/docs/data_AlpacaEval_2/weighted_alpaca_eval_gpt4_turbo_leaderboard.csv',
+        // 'claude': 'https://raw.githubusercontent.com/tatsu-lab/alpaca_eval/main/docs/claude_leaderboard.csv',
+    }
+
+    let currentUrl = urls['alpaca_eval_2'];
+
+    function updateTable(url) {
+        while (table.rows.length > 1) {
+            table.deleteRow(1);
+        }
+
+        Papa.parse(url, {
+            download: true,
+            header: true,
+            complete: function (results) {
+                console.log(results.data);
+                let rank = 0; // Initialize rank counter
+                results.data.forEach(row => {
+                    if (row['name'] || row['win_rate'] || row['length_controlled_winrate']) { //|| row['avg_length']
+                        let filter = row['filter'];
+
+                        if ((communityRadio.checked && (filter === 'verified' || filter === 'minimal' || filter === 'community')) ||
+                            (verifiedRadio.checked && (filter === 'verified' || filter === 'minimal'))) {
+
+                            const tr = document.createElement('tr');
+                            const rankTd = document.createElement('td');
+                            const nameTd = document.createElement('td');
+                            const winRateTd = document.createElement('td');
+                            //const lengthTd = document.createElement('td');
+                            const lenWinRateTd = document.createElement('td');
+
+                            rankTd.classList.add('rank');
+                            nameTd.classList.add('name');
+                            winRateTd.classList.add('winRate');
+                            lenWinRateTd.classList.add('lenWinRate');
+
+                            // Set the rank value
+                            rank++;
+                            rankTd.textContent = rank;
+
+                            if (row['link'] && row['link'].trim() !== '') {
+                                const a = document.createElement('a');
+                                a.textContent = row['name'];
+                                a.href = row['link'];
+                                a.target = "_blank";
+                                nameTd.appendChild(a);
+                            } else {
+                                nameTd.textContent = row['name'];
+                            }
+
+
+                            if (row['samples'] && row['samples'].trim() !== '') {
+                                const samplesLink = document.createElement('a');
+                                samplesLink.textContent = " 📄"; // adding a space before emoji to separate from name
+                                samplesLink.href = row['samples'];
+                                samplesLink.target = "_blank";
+                                samplesLink.style.textDecoration = "none";
+                                nameTd.appendChild(samplesLink);
+                            }
+
+                            winRateTd.textContent = Number(row['win_rate']).toFixed(1) + '%';
+
+                            if (row['length_controlled_winrate'] === '') {
+                                lenWinRateTd.textContent = 'N/A';
+                            } else {
+                                lenWinRateTd.textContent = Number(row['length_controlled_winrate']).toFixed(1) + '%';
+                            }
+                            //lenWinRateTd.textContent = Number(row['length_controlled_winrate']).toFixed(1) + '%';
+                            //lengthTd.textContent = Math.round(Number(row['avg_length'])).toString() ;
+
+
+                            tr.appendChild(rankTd);
+                            tr.appendChild(nameTd);
+                            tr.appendChild(lenWinRateTd);
+                            tr.appendChild(winRateTd);
+                            //tr.appendChild(lengthTd);
+
+                            table.appendChild(tr);
+                        }
+                    }
+                });
+            }
+        });
+    }
+
+    function updateInfoMessage(version) {
+        let infoText;
+        if (version === 'alpaca_eval_2') {
+            infoText = 'Baseline: GPT-4 Preview (11/06) &nbsp; | &nbsp; Auto-annotator: GPT-4 Preview (11/06)';
+        } else if (version === 'alpaca_eval') {
+            infoText = 'Baseline: Davinci003 &nbsp; | &nbsp; Auto-annotator: GPT-4';
+        }
+        document.getElementById('alpaca_eval_info').innerHTML = infoText;
+    }
+
+    updateTable(urls['alpaca_eval_2']);
+
+    alpacaEval2Radio.addEventListener('click', function () {
+        currentUrl = urls['alpaca_eval_2'];
+        updateTable(currentUrl);
+        updateInfoMessage('alpaca_eval_2');
+    });
+
+    alpacaEvalRadio.addEventListener('click', function () {
+        currentUrl = urls['alpaca_eval'];
+        updateTable(currentUrl);
+        updateInfoMessage('alpaca_eval');
+    });
+
+    communityRadio.addEventListener('click', function () {
+        updateTable(currentUrl);
+    });
+
+    verifiedRadio.addEventListener('click', function () {
+        updateTable(currentUrl);
+    });
+
+    // minimalRadio.addEventListener('click', function () {
+    //     updateTable(currentUrl);
+    // });
+
+    updateCautionMessage('alpaca_eval_2');
+</script>
+
+
+</body>
+
+</html>

FeatureSelect-Methods-Leaderboard/AutoFS/debug_data.py

@@ -0,0 +1,36 @@
+import os
+import json
+import sys
+import traceback
+
+# Mock paths
+PROJECT_ROOT = os.path.abspath(os.getcwd())
+RESULT_DIR = os.path.join(PROJECT_ROOT, "results")
+print(f"Result Dir: {RESULT_DIR}")
+
+dataset = "Authorship"
+path = os.path.join(RESULT_DIR, f"{dataset}.json")
+print(f"Path: {path}")
+print(f"Exists: {os.path.exists(path)}")
+
+if os.path.exists(path):
+    try:
+        with open(path, 'r', encoding='utf-8') as f:
+            data = json.load(f)
+            print(f"Data loaded, length: {len(data)}")
+            
+            # Try ranking
+            sys.path.append(PROJECT_ROOT)
+            try:
+                from leaderboard import rank_results
+                ranked = rank_results(data)
+                print(f"Ranked data length: {len(ranked)}")
+                if len(ranked) > 0:
+                    print("First item:", ranked[0])
+            except Exception as e:
+                print(f"Ranking failed: {e}")
+                traceback.print_exc()
+    except Exception as e:
+        print(f"Failed to read/parse json: {e}")
+else:
+    print("File not found!")

FeatureSelect-Methods-Leaderboard/AutoFS/leaderboard.py

@@ -0,0 +1,124 @@
+import numpy as np
+import pandas as pd
+# def rank_results(
+#     results,
+#     metric="f1",
+#     clf_average="mean",
+#     weights=None
+# ):
+#     """
+#     对 FSExecutor 输出结果进行排行榜排序
+
+#     Parameters
+#     ----------
+#     results : list of dict
+#         每个 dict 是一个算法在一个数据集上的结果
+#     metric : str
+#         使用的指标: 'f1' or 'auc'
+#     clf_average : str
+#         'mean' 或 'max'，表示跨分类器如何聚合
+#     weights : dict or None
+#         多指标加权，例如 {'f1':0.5, 'auc':0.5}
+
+#     Returns
+#     -------
+#     ranked_df : pd.DataFrame
+#     """
+
+#     rows = []
+
+#     for res in results:
+#         algo = res["algorithm"]
+#         metrics = res["metrics"]
+
+#         # --------- 单指标 ----------
+#         if weights is None:
+#             vals = []
+#             for clf, m in metrics.items():
+#                 if metric in m:
+#                     vals.append(m[metric])
+
+#             if not vals:
+#                 raise ValueError(f"No metric {metric} for {algo}")
+
+#             score = np.mean(vals) if clf_average == "mean" else np.max(vals)
+
+#         # --------- 多指标加权 ----------
+#         else:
+#             score = 0.0
+#             for m_name, w in weights.items():
+#                 vals = [
+#                     metrics[clf][m_name]
+#                     for clf in metrics
+#                     if m_name in metrics[clf]
+#                 ]
+#                 score += w * np.mean(vals)
+
+#         rows.append({
+#             "algorithm": algo,
+#             "score": score,
+#             "num_features": res["num_features"],
+#             "time": res.get("time", None)
+#         })
+
+#     df = pd.DataFrame(rows)
+
+#     # --------- 按 score 排序 ----------
+#     df = df.sort_values(
+#         by="score",
+#         ascending=False
+#     ).reset_index(drop=True)
+
+#     df["rank"] = df.index + 1
+
+#     return df
+
+def aggregate_metrics(metrics, w_f1=0.7, w_auc=0.3):
+    """
+    metrics:
+        {
+            "nb":  {"f1": x, "auc": y},
+            "svm": {"f1": x, "auc": y},
+            "rf":  {"f1": x, "auc": y},
+        }
+    """
+    f1s = [m["f1"] for m in metrics.values()]
+    aucs = [m["auc"] for m in metrics.values()]
+
+    mean_f1 = sum(f1s) / len(f1s)
+    mean_auc = sum(aucs) / len(aucs)
+
+    return w_f1 * mean_f1 + w_auc * mean_auc,mean_f1,mean_auc
+
+def rank_results(
+    results,
+):
+    """
+    results: list[dict]
+    return: 排序后的 list[dict]（每个 dict 会新增 score 字段）
+    """
+
+    ranked = []
+
+    for r in results:
+        # 1. 性能融合
+        perf_score,mean_f1,mean_auc = aggregate_metrics(r["metrics"])
+
+        # 2. 惩罚项
+        # feature_penalty = alpha * r["num_features"]
+        # time_penalty = beta * r["time"]
+
+        # final_score = perf_score - feature_penalty - time_penalty
+        final_score = perf_score
+        ranked.append({
+            **r,
+            "mean_f1":mean_f1,
+            "mean_auc":mean_auc,
+            "score": final_score,
+            "perf_score": perf_score
+        })
+
+    # 3. 排序（score 越大越好）
+    ranked.sort(key=lambda x: x["score"], reverse=True)
+
+    return ranked

FeatureSelect-Methods-Leaderboard/AutoFS/requirements.txt

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+    },
+    "time": 14.290248394012451,
+    "algorithm": "MRMD"
+  },
+  {
+    "selected_features": [
+      89,
+      92,
+      84,
+      104,
+      99,
+      82,
+      93,
+      95,
+      94,
+      97,
+      83,
+      81,
+      90,
+      87,
+      88
+    ],
+    "num_features": 15,
+    "metrics": {
+      "nb": {
+        "f1": 0.8353211969031178,
+        "auc": 0.9344102233712435
+      },
+      "svm": {
+        "f1": 0.8754760410127641,
+        "auc": 0.9449093560542415
+      },
+      "rf": {
+        "f1": 0.8743460975099393,
+        "auc": 0.9470115615925281
+      }
+    },
+    "time": 28.11224675178528,
+    "algorithm": "UCRFS"
+  },
+  {
+    "selected_features": [
+      [
+        92,
+        104,
+        93,
+        95,
+        94,
+        99,
+        90,
+        91,
+        97,
+        89,
+        103,
+        102,
+        96,
+        88,
+        98
+      ],
+      [
+        84,
+        81,
+        89,
+        83,
+        104,
+        82,
+        85,
+        86,
+        87,
+        88,
+        74,
+        72,
+        66,
+        71,
+        57
+      ],
+      [
+        89,
+        84,
+        92,
+        104,
+        82,
+        99,
+        87,
+        93,
+        88,
+        83,
+        85,
+        91,
+        86,
+        74,
+        94
+      ]
+    ],
+    "num_features": [
+      15,
+      15,
+      15
+    ],
+    "union_num_features": 3,
+    "metrics": {
+      "nb": {
+        "f1": 0.7881146683406571,
+        "auc": 0.9145317738835733
+      },
+      "svm": {
+        "f1": 0.8584641138313454,
+        "auc": 0.9310307930574658
+      },
+      "rf": {
+        "f1": 0.857187696170747,
+        "auc": 0.9399791620380172
+      }
+    },
+    "time": 96.02905464172363,
+    "algorithm": "CSMDCCMR"
+  }
+]

FeatureSelect-Methods-Leaderboard/AutoFS/verify_backend.py

@@ -0,0 +1,41 @@
+import json
+import os
+import sys
+
+# Mock paths
+PROJECT_ROOT = os.path.abspath(os.getcwd())
+sys.path.append(PROJECT_ROOT)
+
+from leaderboard import rank_results
+
+def test_dataset(name):
+    path = os.path.join(PROJECT_ROOT, "results", f"{name}.json")
+    if not os.path.exists(path):
+        print(f"[ERROR] {name} not found")
+        return
+
+    with open(path, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+    
+    print(f"--- Testing {name} ---")
+    try:
+        ranked = rank_results(data)
+        if len(ranked) > 0:
+            first = ranked[0]
+            print("Keys in first item:", first.keys())
+            # Check for critical keys
+            for key in ['mean_f1', 'mean_auc', 'time']:
+                if key not in first:
+                    print(f"[FAIL] Missing key: {key}")
+                elif first[key] is None:
+                    print(f"[FAIL] Key is None: {key}")
+                else:
+                    print(f"[OK] {key}: {first[key]} (type: {type(first[key])})")
+        else:
+            print("[WARN] Ranked list is empty")
+    except Exception as e:
+        print(f"[ERROR] Ranking failed: {e}")
+
+test_dataset("Authorship")
+test_dataset("Factors")
+test_dataset("dna")

FeatureSelect-Methods-Leaderboard/Dockerfile

@@ -0,0 +1,16 @@
+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . /code
+
+# Fix permissions for Hugging Face Spaces (user 1000)
+RUN chmod -R 777 /code
+
+EXPOSE 7860
+
+CMD ["python", "Webapp/app.py"]

FeatureSelect-Methods-Leaderboard/README.md

@@ -0,0 +1,33 @@
+---
+title: AutoFS Leaderboard
+emoji: 📊
+colorFrom: blue
+colorTo: green
+sdk: docker
+pinned: false
+---
+
+# AutoFS Leaderboard
+
+This is a visualization dashboard for AutoFS experiment results.
+
+## Deployment on Hugging Face Spaces
+
+1. Create a new Space on Hugging Face.
+2. Select "Docker" as the SDK.
+3. Upload all files from this repository.
+4. The application will automatically build and run.
+
+## Local Development
+
+1. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+2. Run the application:
+   ```bash
+   python Webapp/app.py
+   ```
+
+3. Open http://localhost:5000 in your browser.

FeatureSelect-Methods-Leaderboard/Webapp/app.py

@@ -0,0 +1,174 @@
+import os
+import sys
+import pickle
+import json
+from flask import Flask, jsonify, request, render_template
+
+# Add project root to sys.path to import leaderboard
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+from leaderboard import rank_results
+# ===============================
+# 基本路径配置
+# ===============================
+PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+RESULT_DIR = os.path.join(PROJECT_ROOT, "results")
+DATASET_DIR = os.path.join(PROJECT_ROOT, "datasets")
+
+os.makedirs(RESULT_DIR, exist_ok=True)
+os.makedirs(DATASET_DIR, exist_ok=True)
+
+# ===============================
+# Flask App
+# ===============================
+app = Flask(__name__)
+
+# ===============================
+# 内存缓存
+# ===============================
+RESULT_CACHE = {}  # {dataset_name: results}
+
+
+# ===============================
+# PKL IO 工具
+# ===============================
+def save_result_json(dataset, results):
+    path = os.path.join(RESULT_DIR, f"{dataset}.json")
+    with open(path, "w", encoding='utf-8') as f:
+        json.dump(results, f, indent=4)
+
+
+def load_result_json(dataset):
+    path = os.path.join(RESULT_DIR, f"{dataset}.json")
+    if not os.path.exists(path):
+        return None
+    with open(path, 'r', encoding='utf-8') as f:
+        return json.load(f)
+
+
+def list_available_datasets():
+    datasets = set()
+
+    for f in os.listdir(RESULT_DIR):
+        if f.endswith(".json"):
+            datasets.add(f.replace(".json", ""))
+
+    # 默认展示数据集
+    datasets.add("Authorship")
+
+    return sorted(datasets)
+
+
+# ===============================
+# ⚠️ 你自己的 Agent 入口
+# ===============================
+def run_agent_for_dataset(dataset):
+    """
+    ⚠️ 用你自己的 router / agent 替换这里
+    必须返回 List[Dict]
+    """
+    # ---------------------------
+    # 示例（占位）
+    # ---------------------------
+    return [
+        
+    ]
+# {
+#             "algorithm": "shibaile",
+#             "num_features": 15,
+#             "mean_f1": 0.9233716475,
+#             "mean_auc": 0.9816098520,
+#             "time": 5.75,
+#             "score": 0.9408431088,
+#         },
+#         {
+#             "algorithm": "JMchongxinzailai",
+#             "num_features": 15,
+#             "mean_f1": 0.918,
+#             "mean_auc": 0.979,
+#             "time": 7.32,
+#             "score": 0.932,
+#         },
+
+# ===============================
+# 页面
+# ===============================
+@app.route("/")
+def index():
+    return render_template(
+        "index.html",
+        datasets=list_available_datasets(),
+        default_dataset="Authorship",
+    )
+
+
+# ===============================
+# API：获取结果
+# ===============================
+@app.route("/api/results")
+def get_results():
+    dataset = request.args.get("dataset", "Authorship")
+
+    # ① 内存缓存 (Disabled for debugging)
+    # if dataset in RESULT_CACHE:
+    #     print("------------------------------------------------------------------zoude cache")
+    #     return jsonify(RESULT_CACHE[dataset])
+    
+    # ② 磁盘 pkl/json
+    results = load_result_json(dataset)
+    print(111,results)
+    if results is not None:
+        print("------------------------------------------------------------------zoude json\n",results)
+        RESULT_CACHE[dataset] = results
+        leaderboard = rank_results(results)
+        return jsonify(leaderboard)
+
+    # ③ Agent 实时运行
+    results = run_agent_for_dataset(dataset)
+    print(222,results)
+
+    # ④ 存储
+    if results and len(results) > 0:
+        save_result_json(dataset, results)
+        RESULT_CACHE[dataset] = results
+    
+    print("------------------------------------------------------------------zoude agent")
+    leaderboard = rank_results(results)
+    # 
+    return jsonify(leaderboard)
+    # print(333,leaderboard)
+# @app.route("/api/results")
+# def get_results():
+    dataset = request.args.get("dataset", "Authorship")
+
+    print(f"[DEBUG] request dataset = {dataset}")
+
+    if dataset in RESULT_CACHE:
+        print("[DEBUG] hit memory cache")
+        return jsonify(RESULT_CACHE[dataset])
+
+    results = load_result_pkl(dataset)
+    if results is not None:
+        print("[DEBUG] hit pkl cache")
+        RESULT_CACHE[dataset] = results
+        return jsonify(results)
+
+    print("[DEBUG] run agent")
+    results = run_agent_for_dataset(dataset)
+    print("[DEBUG] agent results =", results)
+
+    save_result_pkl(dataset, results)
+    RESULT_CACHE[dataset] = results
+    return jsonify(results)
+
+
+# ===============================
+# API：数据集列表
+# ===============================
+@app.route("/api/datasets")
+def api_datasets():
+    return jsonify(list_available_datasets())
+
+
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 7860))
+    app.run(host="0.0.0.0", port=port, debug=False)

FeatureSelect-Methods-Leaderboard/Webapp/app1.py

@@ -0,0 +1,160 @@
+import os
+import pickle
+from flask import Flask, jsonify, request, render_template
+
+# ===============================
+# 基本路径配置
+# ===============================
+PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+RESULT_DIR = os.path.join(PROJECT_ROOT, "results")
+DATASET_DIR = os.path.join(PROJECT_ROOT, "datasets")
+
+os.makedirs(RESULT_DIR, exist_ok=True)
+os.makedirs(DATASET_DIR, exist_ok=True)
+
+# ===============================
+# Flask App
+# ===============================
+app = Flask(__name__)
+
+# ===============================
+# 内存缓存
+# ===============================
+RESULT_CACHE = {}  # {dataset_name: results}
+
+
+# ===============================
+# PKL IO 工具
+# ===============================
+def save_result_pkl(dataset, results):
+    path = os.path.join(RESULT_DIR, f"{dataset}.pkl")
+    with open(path, "wb") as f:
+        pickle.dump(results, f)
+
+
+def load_result_pkl(dataset):
+    path = os.path.join(RESULT_DIR, f"{dataset}.pkl")
+    if not os.path.exists(path):
+        return None
+    with open(path, "rb") as f:
+        return pickle.load(f)
+
+
+def list_available_datasets():
+    datasets = set()
+
+    for f in os.listdir(RESULT_DIR):
+        if f.endswith(".pkl"):
+            datasets.add(f.replace(".pkl", ""))
+
+    # 默认展示数据集
+    datasets.add("Authorship")
+
+    return sorted(datasets)
+
+
+# ===============================
+# ⚠️ 你自己的 Agent 入口
+# ===============================
+def run_agent_for_dataset(dataset):
+    """
+    ⚠️ 用你自己的 router / agent 替换这里
+    必须返回 List[Dict]
+    """
+    # ---------------------------
+    # 示例（占位）
+    # ---------------------------
+    return [
+        {
+            "algorithm": "UCRFS",
+            "num_features": 15,
+            "mean_f1": 0.9233716475,
+            "mean_auc": 0.9816098520,
+            "time": 5.75,
+            "score": 0.9408431088,
+        },
+        {
+            "algorithm": "JMIM",
+            "num_features": 15,
+            "mean_f1": 0.918,
+            "mean_auc": 0.979,
+            "time": 7.32,
+            "score": 0.932,
+        },
+    ]
+
+
+# ===============================
+# 页面
+# ===============================
+@app.route("/")
+def index():
+    return render_template(
+        "index.html",
+        datasets=list_available_datasets(),
+        default_dataset="Authorship",
+    )
+
+
+# ===============================
+# API：获取结果
+# ===============================
+@app.route("/api/results")
+def get_results():
+    dataset = request.args.get("dataset", "Authorship")
+
+    # ① 内存缓存
+    if dataset in RESULT_CACHE:
+        rank_results = jsonify(RESULT_CACHE[dataset])
+
+    # ② 磁盘 pkl
+    results = load_result_pkl(dataset)
+    if results is not None:
+        RESULT_CACHE[dataset] = results
+        rank_results = jsonify(results)
+
+    # ③ Agent 实时运行
+    results = run_agent_for_dataset(dataset)
+
+    # ④ 存储
+    save_result_pkl(dataset, results)
+    RESULT_CACHE[dataset] = results
+
+    rank_results = jsonify(results)
+    leaderboard = rank_results(rank_results)
+    return leaderboard
+# @app.route("/api/results")
+# def get_results():
+    dataset = request.args.get("dataset", "Authorship")
+
+    print(f"[DEBUG] request dataset = {dataset}")
+
+    if dataset in RESULT_CACHE:
+        print("[DEBUG] hit memory cache")
+        return jsonify(RESULT_CACHE[dataset])
+
+    results = load_result_pkl(dataset)
+    if results is not None:
+        print("[DEBUG] hit pkl cache")
+        RESULT_CACHE[dataset] = results
+        return jsonify(results)
+
+    print("[DEBUG] run agent")
+    results = run_agent_for_dataset(dataset)
+    print("[DEBUG] agent results =", results)
+
+    save_result_pkl(dataset, results)
+    RESULT_CACHE[dataset] = results
+    return jsonify(results)
+
+
+# ===============================
+# API：数据集列表
+# ===============================
+@app.route("/api/datasets")
+def api_datasets():
+    return jsonify(list_available_datasets())
+
+
+if __name__ == "__main__":
+    app.run(debug=True)

FeatureSelect-Methods-Leaderboard/Webapp/app11.py

@@ -0,0 +1,27 @@
+import pickle
+from flask import Flask, render_template
+from leaderboard import rank_results
+
+app = Flask(__name__)
+
+# 🚀 这里直接接你 Agent 跑完的 results
+def get_results(dataname):
+    # with open("/home/fangsensen/AutoFS/results/"+ dataname +".pkl", "rb") as f:
+    #   results = pickle.load(f)
+    # print(1111111111,results)
+    results = [{'selected_features': [59, 50, 56, 4, 38, 9, 29, 23, 0, 20, 34, 36, 24, 26, 28], 'num_features': 15, 'metrics': {'nb': {'f1': 0.9181133571145461, 'auc': 0.9807805770573524}, 'svm': {'f1': 0.9282600079270711, 'auc': 0.980695564275392}, 'rf': {'f1': 0.9219976218787156, 'auc': 0.9768409098650539}}, 'time': 9.468129634857178, 'algorithm': 'JMIM'}, {'selected_features': [59, 50, 56, 4, 38, 0, 9, 29, 23, 20, 36, 34, 24, 28, 26], 'num_features': 15, 'metrics': {'nb': {'f1': 0.9163694015061433, 'auc': 0.9805189493459717}, 'svm': {'f1': 0.9265953230281413, 'auc': 0.98064247666047}, 'rf': {'f1': 0.9189853349187476, 'auc': 0.9769432925613145}}, 'time': 1.5439717769622803, 'algorithm': 'CFR'}, {'selected_features': [59, 64, 63, 22, 26, 11, 49, 7, 18, 24, 28, 12, 0, 8, 45], 'num_features': 15, 'metrics': {'nb': {'f1': 0.8498612762584224, 'auc': 0.9612941645198875}, 'svm': {'f1': 0.8672215616329766, 'auc': 0.9669919810144432}, 'rf': {'f1': 0.8516052318668254, 'auc': 0.9579325242146627}}, 'time': 3.4254932403564453, 'algorithm': 'DCSF'}, {'selected_features': [69, 59, 9, 4, 38, 24, 0, 49, 26, 18, 28, 11, 66, 12, 7], 'num_features': 15, 'metrics': {'nb': {'f1': 0.8747522790328972, 'auc': 0.968331958034509}, 'svm': {'f1': 0.8916369401506141, 'auc': 0.9765525653706246}, 'rf': {'f1': 0.9151010701545778, 'auc': 0.9804838761712887}}, 'time': 2.531461477279663, 'algorithm': 'IWFS'}, {'selected_features': [59, 50, 4, 38, 24, 0, 56, 26, 29, 49, 28, 23, 34, 36, 20], 'num_features': 15, 'metrics': {'nb': {'f1': 0.8806183115338884, 'auc': 0.973024320439098}, 'svm': {'f1': 0.9082837891399126, 'auc': 0.9784503098286724}, 'rf': {'f1': 0.897661514070551, 'auc': 0.9735557096666029}}, 'time': 2.793144941329956, 'algorithm': 'MRI'}, {'selected_features': [59, 69, 9, 5, 10, 31, 36, 20, 33, 47, 22, 29, 44, 56, 8], 'num_features': 15, 'metrics': {'nb': {'f1': 0.911375346809354, 'auc': 0.979648928949016}, 'svm': {'f1': 0.9064605628220372, 'auc': 0.9782951525850493}, 'rf': {'f1': 0.9252477209671027, 'auc': 0.9822236522028844}}, 'time': 2.9142298698425293, 'algorithm': 'MRMD'}, {'selected_features': [59, 69, 9, 56, 29, 50, 36, 4, 38, 0, 20, 24, 23, 28, 34], 'num_features': 15, 'metrics': {'nb': {'f1': 0.9177962742766549, 'auc': 0.9819010381640604}, 'svm': {'f1': 0.9178755449861277, 'auc': 0.980385760789456}, 'rf': {'f1': 0.9344431232659534, 'auc': 0.9825427569391104}}, 'time': 5.751329660415649, 'algorithm': 'UCRFS'}, {'selected_features': [[23, 15, 69, 43, 9, 52, 33, 8, 5, 3, 59, 47, 34, 55, 36], [50, 16, 31, 44, 47, 9, 69, 42, 33, 36, 63, 65, 23, 20, 22], [29, 13, 38, 3, 28, 59, 56, 69, 26, 20, 34, 50, 14, 49, 36], [59, 19, 20, 36, 24, 29, 9, 10, 23, 28, 22, 8, 56, 0, 60]], 'num_features': [15, 15, 15, 15], 'union_num_features': 4, 'metrics': {'nb': {'f1': 0.879587792310741, 'auc': 0.9680606961937624}, 'svm': {'f1': 0.8917162108600871, 'auc': 0.9710497573464302}, 'rf': {'f1': 0.8789536266349584, 'auc': 0.9655313327795009}}, 'time': 14.973412275314331, 'algorithm': 'CSMDCCMR'}]
+    leaderboard = rank_results(results)
+    # print(222222222222222,leaderboard)
+    return leaderboard
+
+
+@app.route("/")
+def index():
+    dataname = 'Authorship'
+    results = get_results(dataname)
+    leaderboard = rank_results(results)
+    return render_template("index.html", leaderboard=leaderboard)
+
+
+if __name__ == "__main__":
+    app.run(debug=True)

FeatureSelect-Methods-Leaderboard/Webapp/templates/index.html

@@ -0,0 +1,603 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>AutoFS Leaderboard</title>
+    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
+    <style>
+        :root {
+            --primary-color: #3498db;
+            --secondary-color: #2c3e50;
+            --background-color: #f8f9fa;
+            --text-color: #333;
+            --border-color: #dee2e6;
+            --hover-color: #f1f1f1;
+        }
+
+        body {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            margin: 0;
+            padding: 20px;
+            background-color: var(--background-color);
+            color: var(--text-color);
+        }
+
+        .container {
+            max-width: 1200px;
+            margin: 0 auto;
+            background: white;
+            padding: 20px;
+            border-radius: 8px;
+            box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+        }
+
+        header {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            margin-bottom: 20px;
+            border-bottom: 2px solid var(--primary-color);
+            padding-bottom: 10px;
+        }
+
+        h1 {
+            margin: 0;
+            color: var(--secondary-color);
+        }
+
+        .controls {
+            display: flex;
+            gap: 10px;
+            align-items: center;
+        }
+
+        select {
+            padding: 8px 12px;
+            border: 1px solid var(--border-color);
+            border-radius: 4px;
+            font-size: 14px;
+        }
+
+        table {
+            width: 100%;
+            border-collapse: collapse;
+            margin-top: 10px;
+        }
+
+        th, td {
+            padding: 12px 15px;
+            text-align: left;
+            border-bottom: 1px solid var(--border-color);
+        }
+
+        th {
+            background-color: var(--secondary-color);
+            color: white;
+            cursor: pointer;
+            user-select: none;
+            position: sticky;
+            top: 0;
+        }
+
+        th:hover {
+            background-color: #34495e;
+        }
+
+        th .arrow {
+            font-size: 10px;
+            margin-left: 5px;
+            opacity: 0.7;
+        }
+
+        tr:hover {
+            background-color: var(--hover-color);
+        }
+
+        .score-bar {
+            height: 6px;
+            background-color: #e9ecef;
+            border-radius: 3px;
+            overflow: hidden;
+            margin-top: 5px;
+        }
+
+        .score-fill {
+            height: 100%;
+            background-color: var(--primary-color);
+        }
+
+        .features-cell {
+            max-width: 200px;
+            white-space: nowrap;
+            overflow: hidden;
+            text-overflow: ellipsis;
+            color: #666;
+            font-size: 0.9em;
+            cursor: pointer;
+        }
+        
+        .features-cell:hover {
+            text-decoration: underline;
+            color: var(--primary-color);
+        }
+
+        /* Modal styles */
+        .modal {
+            display: none;
+            position: fixed;
+            z-index: 1000;
+            left: 0;
+            top: 0;
+            width: 100%;
+            height: 100%;
+            background-color: rgba(0,0,0,0.5);
+        }
+
+        .modal-content {
+            background-color: white;
+            margin: 10% auto;
+            padding: 20px;
+            border-radius: 8px;
+            width: 50%;
+            max-width: 600px;
+            box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+        }
+
+        .close {
+            color: #aaa;
+            float: right;
+            font-size: 28px;
+            font-weight: bold;
+            cursor: pointer;
+        }
+
+        .close:hover {
+            color: black;
+        }
+
+        .feature-tag {
+            display: inline-block;
+            background-color: #e1ecf4;
+            color: #2c3e50;
+            padding: 4px 8px;
+            border-radius: 4px;
+            margin: 2px;
+            font-size: 0.9em;
+        }
+        
+        .loading {
+            text-align: center;
+            padding: 20px;
+            color: #666;
+        }
+    </style>
+</head>
+<body>
+
+<div class="container">
+    <header>
+        <h1>🏆 AutoFS Leaderboard</h1>
+        <div class="controls">
+            <label for="dataset-select">Dataset:</label>
+            <select id="dataset-select">
+                <option value="" disabled selected>Loading...</option>
+            </select>
+        </div>
+    </header>
+
+    <div id="loading-indicator" class="loading" style="display: none;">Loading data...</div>
+
+    <div class="chart-controls" style="text-align:center; margin-top: 20px; margin-bottom: 15px;">
+        <label style="margin-right:15px; font-weight:bold;">View Mode:</label>
+        <input type="radio" id="view-overall" name="chart-view" value="overall" checked onchange="updateView()">
+        <label for="view-overall" style="margin-right:10px;">Overall (Mean)</label>
+        
+        <input type="radio" id="view-classifiers-f1" name="chart-view" value="classifiers-f1" onchange="updateView()">
+        <label for="view-classifiers-f1" style="margin-right:10px;">F1 by Classifier</label>
+        
+        <input type="radio" id="view-classifiers-auc" name="chart-view" value="classifiers-auc" onchange="updateView()">
+        <label for="view-classifiers-auc">AUC by Classifier</label>
+    </div>
+
+    <div class="charts-container" style="display: flex; gap: 20px; margin-bottom: 20px;">
+        <div style="flex: 1; background: white; padding: 15px; border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+            <canvas id="scoreChart"></canvas>
+        </div>
+        <div style="flex: 1; background: white; padding: 15px; border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+            <canvas id="timeChart"></canvas>
+        </div>
+    </div>
+
+    <table id="result-table">
+        <thead>
+            <!-- Headers generated dynamically -->
+        </thead>
+        <tbody>
+            <!-- Data rows will be populated here -->
+        </tbody>
+    </table>
+</div>
+
+<!-- Modal for details -->
+<div id="details-modal" class="modal">
+    <div class="modal-content">
+        <span class="close">&times;</span>
+        <h2 id="modal-title">Algorithm Details</h2>
+        <div id="modal-body"></div>
+    </div>
+</div>
+
+<script>
+    let currentResults = [];
+    let sortDirection = 1; // 1 for asc, -1 for desc
+    let lastSortKey = '';
+
+    const VIEW_CONFIG = {
+        'overall': [
+            { key: 'mean_f1', label: 'Mean F1' },
+            { key: 'mean_auc', label: 'Mean AUC' }
+        ],
+        'classifiers-f1': [
+            { key: 'metrics.nb.f1', label: 'NB F1' },
+            { key: 'metrics.svm.f1', label: 'SVM F1' },
+            { key: 'metrics.rf.f1', label: 'RF F1' }
+        ],
+        'classifiers-auc': [
+            { key: 'metrics.nb.auc', label: 'NB AUC' },
+            { key: 'metrics.svm.auc', label: 'SVM AUC' },
+            { key: 'metrics.rf.auc', label: 'RF AUC' }
+        ]
+    };
+
+    const tableHead = document.querySelector("#result-table thead");
+    const tableBody = document.querySelector("#result-table tbody");
+    const datasetSelect = document.getElementById("dataset-select");
+    const loadingIndicator = document.getElementById("loading-indicator");
+    const modal = document.getElementById("details-modal");
+    const closeModal = document.querySelector(".close");
+
+    // Close modal
+    closeModal.onclick = () => modal.style.display = "none";
+    window.onclick = (event) => {
+        if (event.target == modal) modal.style.display = "none";
+    }
+
+    // Global chart instances
+    let scoreChartInstance = null;
+    let timeChartInstance = null;
+
+    function updateCharts(results) {
+        if (!Array.isArray(results) || results.length === 0) return;
+
+        // Limit to top 15 for readability
+        const topResults = results.slice(0, 15);
+        const labels = topResults.map(r => r.algorithm || 'Unknown');
+        const times = topResults.map(r => r.time || 0);
+
+        const viewMode = document.querySelector('input[name="chart-view"]:checked').value;
+        let datasets = [];
+
+        if (viewMode === 'overall') {
+            const f1Scores = topResults.map(r => r.mean_f1 || 0);
+            const aucScores = topResults.map(r => r.mean_auc || 0);
+            datasets = [
+                {
+                    label: 'Mean F1',
+                    data: f1Scores,
+                    backgroundColor: 'rgba(52, 152, 219, 0.7)',
+                    borderColor: 'rgba(52, 152, 219, 1)',
+                    borderWidth: 1
+                },
+                {
+                    label: 'Mean AUC',
+                    data: aucScores,
+                    backgroundColor: 'rgba(46, 204, 113, 0.7)',
+                    borderColor: 'rgba(46, 204, 113, 1)',
+                    borderWidth: 1
+                }
+            ];
+        } else if (viewMode === 'classifiers-f1') {
+            const classifiers = ['nb', 'svm', 'rf'];
+            const colors = ['rgba(255, 206, 86, 0.5)', 'rgba(75, 192, 192, 0.5)', 'rgba(153, 102, 255, 0.5)'];
+            const borderColors = ['rgba(255, 206, 86, 1)', 'rgba(75, 192, 192, 1)', 'rgba(153, 102, 255, 1)'];
+            
+            datasets = classifiers.map((cls, idx) => ({
+                label: cls.toUpperCase() + ' F1',
+                data: topResults.map(r => (r.metrics && r.metrics[cls]) ? r.metrics[cls].f1 : 0),
+                backgroundColor: colors[idx],
+                borderColor: borderColors[idx],
+                borderWidth: 1
+            }));
+        } else if (viewMode === 'classifiers-auc') {
+            const classifiers = ['nb', 'svm', 'rf'];
+            const colors = ['rgba(255, 206, 86, 0.5)', 'rgba(75, 192, 192, 0.5)', 'rgba(153, 102, 255, 0.5)'];
+            const borderColors = ['rgba(255, 206, 86, 1)', 'rgba(75, 192, 192, 1)', 'rgba(153, 102, 255, 1)'];
+            
+            datasets = classifiers.map((cls, idx) => ({
+                label: cls.toUpperCase() + ' AUC',
+                data: topResults.map(r => (r.metrics && r.metrics[cls]) ? r.metrics[cls].auc : 0),
+                backgroundColor: colors[idx],
+                borderColor: borderColors[idx],
+                borderWidth: 1
+            }));
+        }
+
+        // Score Chart
+        const scoreCtx = document.getElementById('scoreChart').getContext('2d');
+        if (scoreChartInstance) scoreChartInstance.destroy();
+        
+        scoreChartInstance = new Chart(scoreCtx, {
+            type: 'bar',
+            data: {
+                labels: labels,
+                datasets: datasets
+            },
+            options: {
+                responsive: true,
+                maintainAspectRatio: false,
+                plugins: {
+                    title: {
+                        display: true,
+                        text: viewMode === 'overall' ? 'Top Algorithms Performance (Mean)' : 
+                              (viewMode === 'classifiers-f1' ? 'F1-Score by Classifier' : 'AUC by Classifier')
+                    }
+                },
+                scales: {
+                    y: {
+                        beginAtZero: false,
+                        // min: 0.8
+                    }
+                }
+            }
+        });
+
+        // Time Chart
+        const timeCtx = document.getElementById('timeChart').getContext('2d');
+        if (timeChartInstance) timeChartInstance.destroy();
+
+        timeChartInstance = new Chart(timeCtx, {
+            type: 'line',
+            data: {
+                labels: labels,
+                datasets: [{
+                    label: 'Time (s)',
+                    data: times,
+                    backgroundColor: 'rgba(231, 76, 60, 0.2)',
+                    borderColor: 'rgba(231, 76, 60, 1)',
+                    borderWidth: 2,
+                    tension: 0.3,
+                    fill: true
+                }]
+            },
+            options: {
+                responsive: true,
+                maintainAspectRatio: false,
+                plugins: {
+                    title: { display: true, text: 'Execution Time' }
+                },
+                scales: {
+                    y: { beginAtZero: true }
+                }
+            }
+        });
+    }
+
+    function showDetails(result) {
+        const title = document.getElementById("modal-title");
+        const body = document.getElementById("modal-body");
+        
+        title.textContent = `${result.algorithm} Details`;
+        
+        let featuresHtml = result.selected_features.map(f => 
+            `<span class="feature-tag">${f}</span>`
+        ).join('');
+
+        let metricsHtml = '<div style="margin-top: 15px;"><h3>Metrics Breakdown</h3>';
+        for (const [clf, m] of Object.entries(result.metrics || {})) {
+            metricsHtml += `
+                <div style="margin-bottom: 10px;">
+                    <strong>${clf.toUpperCase()}:</strong> 
+                    F1: ${m.f1.toFixed(4)}, AUC: ${m.auc.toFixed(4)}
+                </div>`;
+        }
+        metricsHtml += '</div>';
+
+        body.innerHTML = `
+            <p><strong>Time:</strong> ${result.time.toFixed(4)}s</p>
+            <p><strong>Num Features:</strong> ${result.num_features}</p>
+            <p><strong>Selected Features (${result.selected_features.length}):</strong></p>
+            <div>${featuresHtml}</div>
+            ${metricsHtml}
+        `;
+        
+        modal.style.display = "block";
+    }
+
+    function getValue(obj, path) {
+        if (!path) return undefined;
+        return path.split('.').reduce((acc, part) => (acc && acc[part] !== undefined) ? acc[part] : undefined, obj);
+    }
+
+    function safeFixed(value, digits=4) {
+        if (value === undefined || value === null) return 'N/A';
+        return Number(value).toFixed(digits);
+    }
+
+    function renderTableHeader() {
+        const viewMode = document.querySelector('input[name="chart-view"]:checked').value;
+        const dynamicCols = VIEW_CONFIG[viewMode] || VIEW_CONFIG['overall'];
+
+        let html = '<tr>';
+        html += '<th data-key="rank" style="width: 60px;">#</th>';
+        html += '<th data-key="algorithm">Algorithm <span class="arrow">↕</span></th>';
+        
+        dynamicCols.forEach(col => {
+            html += `<th data-key="${col.key}">${col.label} <span class="arrow">↕</span></th>`;
+        });
+
+        html += '<th data-key="time">Time (s) <span class="arrow">↕</span></th>';
+        html += '<th data-key="selected_features">Selected Features</th>';
+        html += '</tr>';
+
+        tableHead.innerHTML = html;
+
+        // Re-attach sort listeners
+        tableHead.querySelectorAll('th[data-key]').forEach(th => {
+            th.addEventListener('click', () => sortTable(th.dataset.key));
+        });
+    }
+
+    function updateTable(results) {
+        tableBody.innerHTML = "";
+        
+        // Safety check
+        if (!Array.isArray(results)) {
+            tableBody.innerHTML = '<tr><td colspan="10" style="text-align:center; color:red;">Error: Invalid data format</td></tr>';
+            return;
+        }
+
+        if (results.length === 0) {
+            tableBody.innerHTML = '<tr><td colspan="10" style="text-align:center;">No results found</td></tr>';
+            return;
+        }
+
+        const viewMode = document.querySelector('input[name="chart-view"]:checked').value;
+        const dynamicCols = VIEW_CONFIG[viewMode] || VIEW_CONFIG['overall'];
+
+        results.forEach((r, idx) => {
+            const row = document.createElement("tr");
+            
+            // Format features for preview
+            const featurePreview = (r.selected_features && Array.isArray(r.selected_features)) 
+                ? r.selected_features.slice(0, 5).join(', ') + (r.selected_features.length > 5 ? '...' : '')
+                : 'N/A';
+            
+            let html = `<td>${idx + 1}</td>`;
+            html += `<td><strong>${r.algorithm || 'Unknown'}</strong></td>`;
+
+            dynamicCols.forEach(col => {
+                const val = getValue(r, col.key);
+                const score = val !== undefined ? val : 0;
+                html += `
+                    <td>
+                        ${safeFixed(val)}
+                        <div class="score-bar"><div class="score-fill" style="width: ${Math.min(score * 100, 100)}%"></div></div>
+                    </td>`;
+            });
+
+            const time = r.time || 0;
+            html += `<td>${safeFixed(time, 2)}</td>`;
+            html += `
+                <td class="features-cell" onclick="showDetails(currentResults[${idx}])" title="Click for details">
+                    ${featurePreview} <span style="font-size:0.8em; color:#999;">(Click for details)</span>
+                </td>`;
+            
+            row.innerHTML = html;
+            tableBody.appendChild(row);
+        });
+    }
+
+    function sortTable(key) {
+        if (lastSortKey === key) {
+            sortDirection *= -1;
+        } else {
+            sortDirection = key === 'time' || key === 'rank' ? 1 : -1;
+            lastSortKey = key;
+        }
+
+        // We don't call renderTableHeader here because it resets the sort indicators if we rebuild entirely.
+        // Instead, we just update the arrows.
+        document.querySelectorAll('th .arrow').forEach(span => span.textContent = '↕');
+        const activeHeader = document.querySelector(`th[data-key="${key}"] .arrow`);
+        if (activeHeader) activeHeader.textContent = sortDirection === 1 ? '↑' : '↓';
+
+        const sorted = [...currentResults].sort((a, b) => {
+            let valA = getValue(a, key);
+            let valB = getValue(b, key);
+            
+            if (key === 'rank') return 0;
+
+            if (valA === undefined) valA = -Infinity;
+            if (valB === undefined) valB = -Infinity;
+
+            if (valA < valB) return -1 * sortDirection;
+            if (valA > valB) return 1 * sortDirection;
+            return 0;
+        });
+        
+        // Don't update currentResults global if it breaks things, but here it's fine.
+        // Actually, let's keep currentResults as the master list? 
+        // No, currentResults should be the sorted list for consistent subsequent sorts.
+        currentResults = sorted; 
+        updateTable(sorted);
+    }
+
+    function updateView() {
+        renderTableHeader();
+        updateTable(currentResults);
+        updateCharts(currentResults);
+    }
+
+    function fetchResults(dataset) {
+        loadingIndicator.style.display = 'block';
+        tableBody.innerHTML = '';
+        
+        console.log("Fetching results for:", dataset);
+        fetch(`/api/results?dataset=${dataset}`)
+            .then(res => {
+                if (!res.ok) throw new Error("Network response was not ok");
+                return res.json();
+            })
+            .then(data => {
+                console.log("Data received:", data);
+                currentResults = data;
+                updateView();
+                loadingIndicator.style.display = 'none';
+            })
+            .catch(err => {
+                console.error("Error fetching results:", err);
+                loadingIndicator.textContent = "Error loading data. Make sure the server is running.";
+            });
+    }
+
+    // Initialize
+    document.addEventListener("DOMContentLoaded", () => {
+        // Setup sort listeners
+        document.querySelectorAll('th[data-key]').forEach(th => {
+            th.addEventListener('click', () => sortTable(th.dataset.key));
+        });
+
+        // Load datasets
+        fetch("/api/datasets")
+            .then(res => res.json())
+            .then(datasets => {
+                datasetSelect.innerHTML = "";
+                datasets.forEach(ds => {
+                    const option = document.createElement("option");
+                    option.value = ds;
+                    option.textContent = ds;
+                    datasetSelect.appendChild(option);
+                });
+
+                if (datasets.includes("Authorship")) {
+                    datasetSelect.value = "Authorship";
+                    fetchResults("Authorship");
+                } else if (datasets.length > 0) {
+                    datasetSelect.value = datasets[0];
+                    fetchResults(datasets[0]);
+                }
+            })
+            .catch(err => {
+                console.error("Error fetching datasets:", err);
+                datasetSelect.innerHTML = "<option>Error loading datasets</option>";
+            });
+
+        datasetSelect.addEventListener('change', (e) => {
+            fetchResults(e.target.value);
+        });
+    });
+</script>
+
+</body>
+</html>

FeatureSelect-Methods-Leaderboard/Webapp/templates/index1.html

@@ -0,0 +1,212 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>AutoFS Leaderboard</title>
+
+    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
+
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            margin: 40px;
+        }
+
+        table {
+            border-collapse: collapse;
+            width: 100%;
+        }
+
+        th, td {
+            border: 1px solid #ddd;
+            padding: 10px;
+            text-align: center;
+        }
+
+        th {
+            cursor: pointer;
+            background-color: #f5f5f5;
+            user-select: none;
+        }
+
+        th span {
+            margin-left: 6px;
+            font-size: 12px;
+            opacity: 0.7;
+        }
+
+        tr:nth-child(even) {
+            background-color: #fafafa;
+        }
+
+        .chart-row {
+            display: flex;
+            gap: 40px;
+            margin-top: 40px;
+        }
+
+        .chart-container {
+            width: 50%;
+        }
+
+        .chart-row-single {
+            display: flex;
+            justify-content: center;
+            margin-top: 40px;
+        }
+
+        .chart-container-single {
+            width: 60%;
+        }
+    </style>
+</head>
+<body>
+
+<h1>Feature Selection Leaderboard</h1>
+
+<table>
+    <thead>
+    <tr>
+        <th>Rank</th>
+        <th onclick="sortTable('algorithm')">Algorithm <span id="arrow-algorithm">↕</span></th>
+        <th onclick="sortTable('num_features')">#Features <span id="arrow-num_features">↕</span></th>
+        <th onclick="sortTable('mean_f1')">Mean F1 <span id="arrow-mean_f1">↕</span></th>
+        <th onclick="sortTable('mean_auc')">Mean AUC <span id="arrow-mean_auc">↕</span></th>
+        <th onclick="sortTable('time')">Time (s) <span id="arrow-time">↕</span></th>
+    </tr>
+    </thead>
+    <tbody id="tbody"></tbody>
+</table>
+
+<!-- F1 & AUC -->
+<div class="chart-row">
+    <div class="chart-container">
+        <canvas id="f1Chart"></canvas>
+    </div>
+    <div class="chart-container">
+        <canvas id="aucChart"></canvas>
+    </div>
+</div>
+
+<!-- Time -->
+<div class="chart-row-single">
+    <div class="chart-container-single">
+        <canvas id="timeChart"></canvas>
+    </div>
+</div>
+
+<script>
+    let leaderboardData = {{ leaderboard | tojson }};
+    let sortKey = null;
+    let sortAsc = true;
+    let f1Chart, aucChart, timeChart;
+
+    const metricOrder = {
+        algorithm: "asc",     // 字符串
+        num_features: "asc",  // 少特征更好（你也可以改）
+        mean_f1: "desc",      // 越大越好
+        mean_auc: "desc",     // 越大越好
+        time: "asc"           // 越小越好 ✅
+    };
+
+    function renderTable() {
+        const tbody = document.getElementById("tbody");
+        tbody.innerHTML = "";
+
+        const n = leaderboardData.length;
+
+        const isBestFirst =
+            (metricOrder[sortKey] === "desc" && !sortAsc) ||
+            (metricOrder[sortKey] === "asc" && sortAsc);
+
+        leaderboardData.forEach((r, i) => {
+            const rank = isBestFirst ? i + 1 : n - i;
+
+            tbody.insertAdjacentHTML("beforeend", `
+                <tr>
+                    <td>${rank}</td>
+                    <td>${r.algorithm}</td>
+                    <td>${r.num_features}</td>
+                    <td>${r.mean_f1.toFixed(4)}</td>
+                    <td>${r.mean_auc.toFixed(4)}</td>
+                    <td>${r.time.toFixed(2)}</td>
+                </tr>
+            `);
+        });
+    }
+
+    function sortTable(key) {
+        if (sortKey === key) {
+            sortAsc = !sortAsc;
+        } else {
+            sortKey = key;
+            sortAsc = metricOrder[key] === "asc";
+        }
+
+        leaderboardData.sort((a, b) => {
+            if (typeof a[key] === "string") {
+                return sortAsc
+                    ? a[key].localeCompare(b[key])
+                    : b[key].localeCompare(a[key]);
+            }
+            return sortAsc ? a[key] - b[key] : b[key] - a[key];
+        });
+
+        updateArrows();
+        renderTable();
+        updateCharts();
+    }
+
+    function updateArrows() {
+        document.querySelectorAll("th span").forEach(s => s.textContent = "↕");
+        document.getElementById("arrow-" + sortKey).textContent = sortAsc ? "↑" : "↓";
+    }
+
+    function updateCharts() {
+        const labels = leaderboardData.map(r => r.algorithm);
+
+        if (f1Chart) f1Chart.destroy();
+        if (aucChart) aucChart.destroy();
+        if (timeChart) timeChart.destroy();
+
+        const baseOptions = title => ({
+            responsive: true,
+            maintainAspectRatio: true,
+            aspectRatio: 2,
+            plugins: { title: { display: true, text: title } }
+        });
+
+        f1Chart = new Chart(document.getElementById("f1Chart"), {
+            type: "line",
+            data: {
+                labels,
+                datasets: [{ label: "Mean F1", data: leaderboardData.map(r => r.mean_f1) }]
+            },
+            options: baseOptions("Mean F1 Curve")
+        });
+
+        aucChart = new Chart(document.getElementById("aucChart"), {
+            type: "line",
+            data: {
+                labels,
+                datasets: [{ label: "Mean AUC", data: leaderboardData.map(r => r.mean_auc) }]
+            },
+            options: baseOptions("Mean AUC Curve")
+        });
+
+        timeChart = new Chart(document.getElementById("timeChart"), {
+            type: "line",
+            data: {
+                labels,
+                datasets: [{ label: "Time (s)", data: leaderboardData.map(r => r.time) }]
+            },
+            options: baseOptions("Runtime Curve")
+        });
+    }
+
+    renderTable();
+    updateCharts();
+</script>
+
+</body>
+</html>

FeatureSelect-Methods-Leaderboard/Webapp/templates/index11.html

@@ -0,0 +1,136 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Feature Selection Leaderboard</title>
+    <style>
+        body { font-family: Arial, sans-serif; margin: 20px; }
+        table { border-collapse: collapse; width: 100%; margin-top: 20px; }
+        th, td { border: 1px solid #ddd; padding: 8px; text-align: center; }
+        th { cursor: pointer; background-color: #f2f2f2; position: relative; }
+        th .arrow { font-size: 12px; margin-left: 4px; }
+        select { padding: 5px; margin-bottom: 10px; }
+    </style>
+</head>
+<body>
+
+<h1>Feature Selection Leaderboard</h1>
+
+<label for="dataset-select">Select Dataset:</label>
+<select id="dataset-select">
+    <!-- 这里的 options 会在后端渲染或者 JS 初始化 -->
+</select>
+
+<table id="result-table">
+    <thead>
+        <tr>
+            <th data-key="rank">Rank <span class="arrow">↑↓</span></th>
+            <th data-key="algorithm">Algorithm <span class="arrow">↑↓</span></th>
+            <th data-key="num_features">Num Features <span class="arrow">↑↓</span></th>
+            <th data-key="mean_f1">Mean F1 <span class="arrow">↑↓</span></th>
+            <th data-key="mean_auc">Mean AUC <span class="arrow">↑↓</span></th>
+            <th data-key="time">Time <span class="arrow">↑↓</span></th>
+        </tr>
+    </thead>
+    <tbody>
+        <!-- 数据行由 JS 填充 -->
+    </tbody>
+</table>
+
+<script>
+    // 全局变量存储当前表格数据
+    let currentResults = [];
+
+    // 渲染表格
+    function updateTable(results) {
+        const tbody = document.querySelector("#result-table tbody");
+        tbody.innerHTML = "";
+        currentResults = results; // 保存全局，用于排序
+
+        results.forEach((r, idx) => {
+            const row = document.createElement("tr");
+            row.innerHTML = `
+                <td>${idx + 1}</td>
+                <td>${r.algorithm}</td>
+                <td>${r.num_features}</td>
+                <td>${r.mean_f1.toFixed(4)}</td>
+                <td>${r.mean_auc.toFixed(4)}</td>
+                <td>${r.time.toFixed(2)}</td>
+            `;
+            tbody.appendChild(row);
+        });
+    }
+
+    // 获取数据
+    function fetchResults(dataset) {
+        console.log("[DEBUG] Fetching dataset:", dataset);
+        fetch(`/api/results?dataset=${dataset}`)
+          .then(res => res.json())
+          .then(data => {
+              console.log("[DEBUG] Fetched results:", data);
+              updateTable(data);
+          })
+          .catch(err => console.error(err));
+    }
+
+    // 初始化下拉框和默认数据集
+    document.addEventListener("DOMContentLoaded", () => {
+        const select = document.getElementById("dataset-select");
+
+        // 从后端获取可用数据集列表
+        fetch("/api/datasets")
+          .then(res => res.json())
+          .then(datasets => {
+              datasets.forEach(ds => {
+                  const option = document.createElement("option");
+                  option.value = ds;
+                  option.textContent = ds;
+                  select.appendChild(option);
+              });
+
+              // 默认加载 Authorship，如果存在
+              if (datasets.includes("Authorship")) {
+                  select.value = "Authorship";
+                  fetchResults("Authorship");
+              } else if (datasets.length > 0) {
+                  select.value = datasets[0];
+                  fetchResults(datasets[0]);
+              }
+          });
+
+        // 绑定选择事件
+        select.addEventListener("change", () => {
+            fetchResults(select.value);
+        });
+
+        // 绑定表头点击排序
+        document.querySelectorAll("#result-table th").forEach(th => {
+            th.addEventListener("click", () => {
+                const key = th.dataset.key;
+                if (!key) return;
+                sortTable(key);
+            });
+        });
+    });
+
+    // 排序函数
+    let sortAsc = true;
+    function sortTable(key) {
+        currentResults.sort((a, b) => {
+            let valA = a[key], valB = b[key];
+
+            // time 值越小越好，其余指标越大越好
+            if (key === "time") {
+                return sortAsc ? valA - valB : valB - valA;
+            } else {
+                return sortAsc ? valB - valA : valA - valB;
+            }
+        });
+        // 切换排序方向
+        sortAsc = !sortAsc;
+        updateTable(currentResults);
+    }
+</script>
+
+</body>
+</html>

FeatureSelect-Methods-Leaderboard/Webapp/templates/indexa.html

@@ -0,0 +1,434 @@
+<!DOCTYPE html>
+<html>
+
+<head>
+    <title>FeatureSelect Leaderboard</title>
+
+    <!-- Google tag (gtag.js) -->
+    <!-- <script async src="https://www.googletagmanager.com/gtag/js?id=G-VWV023WWP4"></script> -->
+    <!-- <script>
+        window.dataLayer = window.dataLayer || [];
+
+        function gtag() {
+            dataLayer.push(arguments);
+        }
+
+        gtag('js', new Date());
+
+        gtag('config', 'G-VWV023WWP4');
+    </script> -->
+
+    <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css">
+    <link rel="icon" href="https://raw.githubusercontent.com/tatsu-lab/alpaca_eval/main/docs/AlpacaFarm_small.png">
+    <link href="https://cdn.jsdelivr.net/css-toggle-switch/latest/toggle-switch.css" rel="stylesheet"/>
+
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            margin: 0;
+            padding: 50px 20px;
+            background-color: #FFFFFF;
+            color: #000000;
+        }
+
+        .container {
+            max-width: 700px;
+            margin: auto;
+        }
+
+        #branding {
+            text-align: center;
+            margin-bottom: 20px;
+        }
+
+        #branding h1 {
+            margin: 0;
+            font-size: 2em;
+        }
+
+        h2 {
+            margin: 0;
+            font-size: 1.2em;
+            color: #777;
+        }
+
+        table {
+            max-width: 700px;
+            width: 100%;
+            table-layout: fixed;
+            margin: auto;
+            font-size: 1em;
+        }
+
+        table th,
+        table td {
+            padding: 6px;
+            word-wrap: break-word;
+            vertical-align: middle;
+        }
+
+        table th {
+            border-bottom: 2px solid #000;
+        }
+
+        th.rank, td.rank {
+            width: 9%; /* Adjust as needed */
+            padding-left: 10px; /* Small margin */
+            text-align: left;
+        }
+
+        th.name, td.name {
+            width: 55%;
+            padding-left: 30px;
+            text-align: left;
+        }
+
+        th:not(.rank):not(.name),
+        td:not(.rank):not(.name) {
+            text-align: right;
+            padding-right: 10px;
+        }
+
+        th.winRate, td.winRate {
+            width: 17%;
+            padding-right: 30px;
+        }
+
+        th {
+            text-align: right;
+            padding-bottom: 15px;
+        }
+
+        td {
+            padding-bottom: 10px;
+        }
+
+        #leaderboard tr th.winRate,
+        #leaderboard tr td.winRate {
+            color: #999999;
+        }
+
+        #leaderboard tr th.rank,
+        #leaderboard tr td.rank {
+            color: #999999;
+        }
+
+        table tr:nth-child(even) {
+            background-color: #E8E8E8;
+        }
+
+        table tr:nth-child(odd) {
+            background-color: #F8F8F8;
+        }
+
+        .switch-toggle {
+            display: inline-block;
+            vertical-align: middle;
+        }
+
+        .switch-toggle input + label {
+            padding: 2px;
+            padding-left: 7px;
+            padding-right: 7px;
+            cursor: pointer;
+            background-color: lightgrey;
+            border: 1px solid transparent;
+            font-size: 16px;
+        }
+
+        .switch-toggle input:checked + label {
+            border-color: green;
+            color: green;
+        }
+
+        .switch-toggle input:not(:checked) + label {
+            color: black;
+            box-shadow: none !important;
+            user-select: none;
+        }
+
+
+        .toggle-line {
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            margin-bottom: 20px;
+            font-size: 17px;
+        }
+
+        .toggle-line .switch-toggle {
+            margin: 0 10px;
+        }
+    </style>
+    <!-- <script src="https://cdnjs.cloudflare.com/ajax/libs/PapaParse/5.3.0/papaparse.min.js"></script> -->
+</head>
+
+<body>
+<div class="container">
+    <div id="branding">
+
+        <h1>FeatureSelect
+            <!-- <a href="https://github.com/tatsu-lab/alpaca_eval/tree/main">
+                <img src="https://raw.githubusercontent.com/tatsu-lab/alpaca_eval/main/docs/AlpacaFarm_small.png"
+                     alt="Logo" style="height: 2em; vertical-align: middle;"></a> -->
+            Leaderboard
+        </h1>
+        <br>
+        <h2>An Automatic Evaluator for FeatureSelect Methods</h2>
+<!--        <small id="alpaca_eval_info" style="color: #777;">-->
+<!--            Baseline: GPT-4 Preview &nbsp; | &nbsp; Auto-annotator: GPT-4 Preview-->
+<!--        </small>-->
+<!--        <br>-->
+        <small id="caution" style="color: #8C1515;">
+            <b> Length-controlled</b> (LC) win rates alleviate length biases of GPT-4, but it may favor models finetuned on its outputs.
+        </small>
+        <br>
+        <a href="https://github.com/Fss2652530458/AutoFS">
+            <img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub logo" style="height: 1.5em;/* margin-bottom: 0; */">
+        </a>
+    </div>
+
+
+<!--           选择器                    -->
+    <div class="toggle-line">
+
+        Version:
+        <div class="switch-toggle switch-evaluator" style="margin-right: 4em">
+            <input id="alpaca_eval" name="version" type="radio"/>
+            <label for="alpaca_eval" onclick="">AlpacaEval</label>
+            <input id="alpaca_eval_2" name="version" type="radio" checked="checked"/>
+            <label for="alpaca_eval_2" onclick="">AlpacaEval 2.0</label>
+        </div>
+
+        Filter:
+        <div class="switch-toggle switch-filter">
+            <input id="community" name="filter" type="radio"/>
+            <label for="community" onclick="">Community</label>
+            <input id="verified" name="filter" type="radio" checked="checked"/>
+            <label for="verified" onclick="">Verified</label>
+<!--            <input id="minimal" name="compactness" type="radio"/>-->
+<!--            <label for="minimal" onclick="">Minimal</label>-->
+        </div>
+
+
+
+    </div>
+<!--            Baseline小灰字-->
+    <div class="container" style="text-align: center; margin-bottom: 10px; margin-top: -10px;">
+        <small id="alpaca_eval_info" style="color: #777;">
+            Baseline: GPT-4 Preview (11/06) &nbsp; | &nbsp; Auto-annotator: GPT-4 Preview (11/06)
+        </small>
+    </div>
+
+
+<!--            排行榜本体-->
+    <table id="leaderboard">
+        <tr>
+            <th class="rank">Rank</th>
+            <th class="name" onclick="sortTable('algorithm')">Algorithm <span id="arrow-algorithm">↕</span></th>
+            <th class="lenWinRate" onclick="sortTable('num_features')">#Features <span id="arrow-num_features">↕</span></th>
+            <th class="winRate" onclick="sortTable('mean_f1')">Mean F1 <span id="arrow-mean_f1">↕</span></th>
+            <th class="winRate" onclick="sortTable('mean_auc')">Mean AUC <span id="arrow-mean_auc">↕</span></th>
+            <th class="winRate" onclick="sortTable('time')">Time (s) <span id="arrow-time">↕</span></th>
+        </tr>
+    </table>
+
+    <!--            文档简介-->
+    <div id="documentation">
+        <div style="text-align: center;">
+            <a href="https://github.com/tatsu-lab/alpaca_eval" style="display: inline-block;">
+                <i class="fab fa-fw fa-github" aria-hidden="true"></i> Github
+            </a>
+        </div>
+        <br>
+        <h2>About AlpacaEval</h2>
+        <p>
+            <a href="https://github.com/tatsu-lab/alpaca_eval" target="_blank">AlpacaEval</a>
+            an LLM-based automatic evaluation that is fast, cheap, and reliable.
+            It is based on the
+            <a href="https://crfm.stanford.edu/2023/05/22/alpaca-farm.html">AlpacaFarm</a>
+            evaluation set,
+            which tests the ability of models to follow general user instructions.
+            These responses are then compared to reference responses (Davinci003 for AlpacaEval, GPT-4 Preview for AlpacaEval 2.0) by
+            the provided GPT-4 based auto-annotators,
+            which results in the win rates presented above.
+            AlpacaEval displays a high agreement rate with ground truth human annotations,
+            and leaderboard rankings on AlpacaEval are very correlated with leaderboard rankings
+            based on human annotators.
+            Please see our
+            <a href="https://github.com/tatsu-lab/alpaca_eval#analysis" target="_blank">documentation</a>
+            for more details on our analysis.
+        </p>
+        <h2>Adding new models</h2>
+        <p>
+            We welcome new model contributions to the leaderboard from the community!
+            To do so, please follow the steps in the
+            <a href="https://github.com/tatsu-lab/alpaca_eval#contributing" target="_blank">contributions
+                section</a>.
+            Specifically, you'll need to run the model on the evaluation set,
+            auto-annotate the outputs, and submit a PR with the model config and leaderboard results.
+            We've also set up a
+            <a href="https://discord.gg/GJMxJSVZZM" target="_blank">Discord</a>
+            for community support and discussion.
+        </p>
+        <h2>Adding new evaluators or eval sets </h2>
+        <p>
+            We also welcome contributions for new evaluators or new eval sets!
+            For making new evaluators, we release our ground-truth
+            <a href="https://github.com/tatsu-lab/alpaca_eval#data-release" target="_blank">human annotations</a>
+            and <a href="https://github.com/tatsu-lab/alpaca_eval#analyzing-an-evaluator" target="_blank">comparison
+            metrics</a>.
+            We also release a
+            <a href="https://github.com/tatsu-lab/alpaca_eval#analyzing-an-eval-set" target="_blank">rough guide</a>
+            to follow for making new eval sets.
+            We specifically encourage contributions for harder instructions distributions and for safety testing of
+            LLMs.
+        </p>
+        <h2>AlpacaEval limitations</h2>
+        <p>
+           这里是简介
+        </p>
+    </div>
+
+</div>
+
+<script>
+    const alpacaEvalRadio = document.getElementById('alpaca_eval');
+    const alpacaEval2Radio = document.getElementById('alpaca_eval_2');
+
+    const communityRadio = document.getElementById('community');
+    const verifiedRadio = document.getElementById('verified');
+    // const minimalRadio = document.getElementById('minimal');
+
+    const table = document.getElementById('leaderboard');
+
+    const urls = {
+        'alpaca_eval': 'https://raw.githubusercontent.com/tatsu-lab/alpaca_eval/main/docs/data_AlpacaEval/alpaca_eval_gpt4_leaderboard.csv',
+        'alpaca_eval_2': 'https://raw.githubusercontent.com/tatsu-lab/alpaca_eval/main/docs/data_AlpacaEval_2/weighted_alpaca_eval_gpt4_turbo_leaderboard.csv',
+        // 'claude': 'https://raw.githubusercontent.com/tatsu-lab/alpaca_eval/main/docs/claude_leaderboard.csv',
+    }
+
+    let currentUrl = urls['alpaca_eval_2'];
+
+    function updateTable(url) {
+        while (table.rows.length > 1) {
+            table.deleteRow(1);
+        }
+
+        Papa.parse(url, {
+            download: true,
+            header: true,
+            complete: function (results) {
+                console.log(results.data);
+                let rank = 0; // Initialize rank counter
+                results.data.forEach(row => {
+                    if (row['name'] || row['win_rate'] || row['length_controlled_winrate']) { //|| row['avg_length']
+                        let filter = row['filter'];
+
+                        if ((communityRadio.checked && (filter === 'verified' || filter === 'minimal' || filter === 'community')) ||
+                            (verifiedRadio.checked && (filter === 'verified' || filter === 'minimal'))) {
+
+                            const tr = document.createElement('tr');
+                            const rankTd = document.createElement('td');
+                            const nameTd = document.createElement('td');
+                            const winRateTd = document.createElement('td');
+                            //const lengthTd = document.createElement('td');
+                            const lenWinRateTd = document.createElement('td');
+
+                            rankTd.classList.add('rank');
+                            nameTd.classList.add('name');
+                            winRateTd.classList.add('winRate');
+                            lenWinRateTd.classList.add('lenWinRate');
+
+                            // Set the rank value
+                            rank++;
+                            rankTd.textContent = rank;
+
+                            if (row['link'] && row['link'].trim() !== '') {
+                                const a = document.createElement('a');
+                                a.textContent = row['name'];
+                                a.href = row['link'];
+                                a.target = "_blank";
+                                nameTd.appendChild(a);
+                            } else {
+                                nameTd.textContent = row['name'];
+                            }
+
+
+                            if (row['samples'] && row['samples'].trim() !== '') {
+                                const samplesLink = document.createElement('a');
+                                samplesLink.textContent = " 📄"; // adding a space before emoji to separate from name
+                                samplesLink.href = row['samples'];
+                                samplesLink.target = "_blank";
+                                samplesLink.style.textDecoration = "none";
+                                nameTd.appendChild(samplesLink);
+                            }
+
+                            winRateTd.textContent = Number(row['win_rate']).toFixed(1) + '%';
+
+                            if (row['length_controlled_winrate'] === '') {
+                                lenWinRateTd.textContent = 'N/A';
+                            } else {
+                                lenWinRateTd.textContent = Number(row['length_controlled_winrate']).toFixed(1) + '%';
+                            }
+                            //lenWinRateTd.textContent = Number(row['length_controlled_winrate']).toFixed(1) + '%';
+                            //lengthTd.textContent = Math.round(Number(row['avg_length'])).toString() ;
+
+
+                            tr.appendChild(rankTd);
+                            tr.appendChild(nameTd);
+                            tr.appendChild(lenWinRateTd);
+                            tr.appendChild(winRateTd);
+                            //tr.appendChild(lengthTd);
+
+                            table.appendChild(tr);
+                        }
+                    }
+                });
+            }
+        });
+    }
+
+    function updateInfoMessage(version) {
+        let infoText;
+        if (version === 'alpaca_eval_2') {
+            infoText = 'Baseline: GPT-4 Preview (11/06) &nbsp; | &nbsp; Auto-annotator: GPT-4 Preview (11/06)';
+        } else if (version === 'alpaca_eval') {
+            infoText = 'Baseline: Davinci003 &nbsp; | &nbsp; Auto-annotator: GPT-4';
+        }
+        document.getElementById('alpaca_eval_info').innerHTML = infoText;
+    }
+
+    updateTable(urls['alpaca_eval_2']);
+
+    alpacaEval2Radio.addEventListener('click', function () {
+        currentUrl = urls['alpaca_eval_2'];
+        updateTable(currentUrl);
+        updateInfoMessage('alpaca_eval_2');
+    });
+
+    alpacaEvalRadio.addEventListener('click', function () {
+        currentUrl = urls['alpaca_eval'];
+        updateTable(currentUrl);
+        updateInfoMessage('alpaca_eval');
+    });
+
+    communityRadio.addEventListener('click', function () {
+        updateTable(currentUrl);
+    });
+
+    verifiedRadio.addEventListener('click', function () {
+        updateTable(currentUrl);
+    });
+
+    // minimalRadio.addEventListener('click', function () {
+    //     updateTable(currentUrl);
+    // });
+
+    updateCautionMessage('alpaca_eval_2');
+</script>
+
+
+</body>
+
+</html>

FeatureSelect-Methods-Leaderboard/complex_com.py

@@ -0,0 +1,141 @@
+import math
+from typing import Dict, Optional
+
+
+class ComplexityScorer:
+    """
+    Complexity scoring for feature selection algorithms
+    based on instantiated time and space complexity.
+    """
+
+    def __init__(self, alpha: float = 0.7, log_base: float = math.e):
+        """
+        Parameters
+        ----------
+        alpha : float
+            Weight for time complexity in the final score.
+            Typical values: 0.6 ~ 0.8
+        log_base : float
+            Base of logarithm (default: natural log).
+        """
+        self.alpha = alpha
+        self.log_base = log_base
+
+    # ------------------------------
+    # Core scoring functions
+    # ------------------------------
+
+    def _log(self, x: float) -> float:
+        """Logarithm with configurable base."""
+        if self.log_base == math.e:
+            return math.log(x)
+        return math.log(x, self.log_base)
+
+    def complexity_score(self, value: float, min_value: float) -> float:
+        """
+        Generic complexity-to-score mapping.
+
+        Parameters
+        ----------
+        value : float
+            Instantiated complexity value of an algorithm.
+        min_value : float
+            Minimum complexity among all compared algorithms.
+
+        Returns
+        -------
+        score : float
+            Normalized score in (0, 1].
+        """
+        if value <= 0 or min_value <= 0:
+            raise ValueError("Complexity values must be positive.")
+
+        ratio = value / min_value
+        return 1.0 / (1.0 + self._log(ratio))
+
+    def time_score(self, f_t: float, f_t_min: float) -> float:
+        """Time complexity score."""
+        return self.complexity_score(f_t, f_t_min)
+
+    def space_score(self, f_s: float, f_s_min: float) -> float:
+        """Space complexity score."""
+        return self.complexity_score(f_s, f_s_min)
+
+    def total_score(
+        self,
+        f_t: float,
+        f_t_min: float,
+        f_s: float,
+        f_s_min: float,
+    ) -> float:
+        """Combined complexity score."""
+        s_t = self.time_score(f_t, f_t_min)
+        s_s = self.space_score(f_s, f_s_min)
+        return self.alpha * s_t + (1.0 - self.alpha) * s_s
+
+
+# --------------------------------------
+# Utility: instantiate complexity formula
+# --------------------------------------
+
+def instantiate_complexity(
+    formula: str,
+    n: int,
+    d: int,
+    k: Optional[int] = None
+) -> float:
+    """
+    Instantiate asymptotic complexity formula.
+
+    Supported variables: n, d, k
+
+    Examples
+    --------
+    "n * d**2"
+    "n * d * k"
+    "d**2"
+    "d + k"
+    """
+    local_vars = {"n": n, "d": d}
+    if k is not None:
+        local_vars["k"] = k
+
+    try:
+        return float(eval(formula, {"__builtins__": {}}, local_vars))
+    except Exception as e:
+        raise ValueError(f"Invalid complexity formula: {formula}") from e
+
+
+n, d, k = 1000, 50, 10
+
+algorithms = {
+    "mRMR": {"time": "n * d**2", "space": "d**2"},
+    "JMIM": {"time": "n * d * k", "space": "d * k"},
+    "CFR": {"time": "n * d * k","space": "d + k"},
+    "DCSF": {"time": "n * d * k", "space": "d + k"},
+    "IWFS": {"time": "n * d * k", "space": "d + k"},
+    "MRI": {"time": "n * d * k", "space": "d + k"},
+    "MRMD": {"time": "n * d**2", "space": "d**2"},
+    "UCRFS": {"time": "n * d + n**2", "space": "n**2"},
+
+
+}
+
+# Instantiate complexities
+time_vals = []
+space_vals = {}
+
+for name, comp in algorithms.items():
+    f_t = instantiate_complexity(comp["time"], n, d, k)
+    f_s = instantiate_complexity(comp["space"], n, d, k)
+    time_vals.append(f_t)
+    space_vals[name] = (f_t, f_s)
+
+f_t_min = min(time_vals)
+f_s_min = min(v[1] for v in space_vals.values())
+
+scorer = ComplexityScorer(alpha=0.7)
+
+for name, (f_t, f_s) in space_vals.items():
+    score = scorer.total_score(f_t, f_t_min, f_s, f_s_min)
+    print(f"{name}: complexity score = {score:.4f}")

FeatureSelect-Methods-Leaderboard/debug_data.py

@@ -0,0 +1,36 @@
+import os
+import json
+import sys
+import traceback
+
+# Mock paths
+PROJECT_ROOT = os.path.abspath(os.getcwd())
+RESULT_DIR = os.path.join(PROJECT_ROOT, "results")
+print(f"Result Dir: {RESULT_DIR}")
+
+dataset = "Authorship"
+path = os.path.join(RESULT_DIR, f"{dataset}.json")
+print(f"Path: {path}")
+print(f"Exists: {os.path.exists(path)}")
+
+if os.path.exists(path):
+    try:
+        with open(path, 'r', encoding='utf-8') as f:
+            data = json.load(f)
+            print(f"Data loaded, length: {len(data)}")
+            
+            # Try ranking
+            sys.path.append(PROJECT_ROOT)
+            try:
+                from leaderboard import rank_results
+                ranked = rank_results(data)
+                print(f"Ranked data length: {len(ranked)}")
+                if len(ranked) > 0:
+                    print("First item:", ranked[0])
+            except Exception as e:
+                print(f"Ranking failed: {e}")
+                traceback.print_exc()
+    except Exception as e:
+        print(f"Failed to read/parse json: {e}")
+else:
+    print("File not found!")

FeatureSelect-Methods-Leaderboard/leaderboard.py

@@ -0,0 +1,124 @@
+import numpy as np
+import pandas as pd
+# def rank_results(
+#     results,
+#     metric="f1",
+#     clf_average="mean",
+#     weights=None
+# ):
+#     """
+#     对 FSExecutor 输出结果进行排行榜排序
+
+#     Parameters
+#     ----------
+#     results : list of dict
+#         每个 dict 是一个算法在一个数据集上的结果
+#     metric : str
+#         使用的指标: 'f1' or 'auc'
+#     clf_average : str
+#         'mean' 或 'max'，表示跨分类器如何聚合
+#     weights : dict or None
+#         多指标加权，例如 {'f1':0.5, 'auc':0.5}
+
+#     Returns
+#     -------
+#     ranked_df : pd.DataFrame
+#     """
+
+#     rows = []
+
+#     for res in results:
+#         algo = res["algorithm"]
+#         metrics = res["metrics"]
+
+#         # --------- 单指标 ----------
+#         if weights is None:
+#             vals = []
+#             for clf, m in metrics.items():
+#                 if metric in m:
+#                     vals.append(m[metric])
+
+#             if not vals:
+#                 raise ValueError(f"No metric {metric} for {algo}")
+
+#             score = np.mean(vals) if clf_average == "mean" else np.max(vals)
+
+#         # --------- 多指标加权 ----------
+#         else:
+#             score = 0.0
+#             for m_name, w in weights.items():
+#                 vals = [
+#                     metrics[clf][m_name]
+#                     for clf in metrics
+#                     if m_name in metrics[clf]
+#                 ]
+#                 score += w * np.mean(vals)
+
+#         rows.append({
+#             "algorithm": algo,
+#             "score": score,
+#             "num_features": res["num_features"],
+#             "time": res.get("time", None)
+#         })
+
+#     df = pd.DataFrame(rows)
+
+#     # --------- 按 score 排序 ----------
+#     df = df.sort_values(
+#         by="score",
+#         ascending=False
+#     ).reset_index(drop=True)
+
+#     df["rank"] = df.index + 1
+
+#     return df
+
+def aggregate_metrics(metrics, w_f1=0.7, w_auc=0.3):
+    """
+    metrics:
+        {
+            "nb":  {"f1": x, "auc": y},
+            "svm": {"f1": x, "auc": y},
+            "rf":  {"f1": x, "auc": y},
+        }
+    """
+    f1s = [m["f1"] for m in metrics.values()]
+    aucs = [m["auc"] for m in metrics.values()]
+
+    mean_f1 = sum(f1s) / len(f1s)
+    mean_auc = sum(aucs) / len(aucs)
+
+    return w_f1 * mean_f1 + w_auc * mean_auc,mean_f1,mean_auc
+
+def rank_results(
+    results,
+):
+    """
+    results: list[dict]
+    return: 排序后的 list[dict]（每个 dict 会新增 score 字段）
+    """
+
+    ranked = []
+
+    for r in results:
+        # 1. 性能融合
+        perf_score,mean_f1,mean_auc = aggregate_metrics(r["metrics"])
+
+        # 2. 惩罚项
+        # feature_penalty = alpha * r["num_features"]
+        # time_penalty = beta * r["time"]
+
+        # final_score = perf_score - feature_penalty - time_penalty
+        final_score = perf_score
+        ranked.append({
+            **r,
+            "mean_f1":mean_f1,
+            "mean_auc":mean_auc,
+            "score": final_score,
+            "perf_score": perf_score
+        })
+
+    # 3. 排序（score 越大越好）
+    ranked.sort(key=lambda x: x["score"], reverse=True)
+
+    return ranked

FeatureSelect-Methods-Leaderboard/requirements.txt

@@ -0,0 +1,3 @@
+Flask
+pandas
+numpy

FeatureSelect-Methods-Leaderboard/results/Authorship.json

@@ -0,0 +1,349 @@
+[
+  {
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+        "auc": 0.9768411621948705
+      }
+    },
+    "time": 7.003696441650391,
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+      15,
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+    ],
+    "union_num_features": 4,
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+    },
+    "time": 12.251755952835083,
+    "algorithm": "CSMDCCMR"
+  }
+]

FeatureSelect-Methods-Leaderboard/results/Colon.json

@@ -0,0 +1,254 @@
+[
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FeatureSelect-Methods-Leaderboard/results/Factors.json

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