Upload app_bayesian.py

app_bayesian.py

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+import streamlit as st
+import pandas as pd
+import uuid
+from datetime import datetime, timedelta
+import atexit
+import os
+import sys
+
+# 頁面配置
+st.set_page_config(
+    page_title="Bayesian Hierarchical Model - Pokémon Speed Analysis",
+    page_icon="🎲",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# 自定義 CSS
+st.markdown("""
+<style>
+    .streamlit-expanderHeader {
+        background-color: #e8f1f8;
+        border: 1px solid #b0cfe8;
+        border-radius: 5px;
+        font-weight: 600;
+        color: #1b4f72;
+    }
+    .streamlit-expanderHeader:hover {
+        background-color: #d0e7f8;
+    }
+    .stMetric {
+        background-color: #f8fbff;
+        padding: 10px;
+        border-radius: 5px;
+        border: 1px solid #d0e4f5;
+    }
+    .stButton > button {
+        width: 100%;
+        border-radius: 20px;
+        font-weight: 600;
+        transition: all 0.3s ease;
+    }
+    .stButton > button:hover {
+        transform: translateY(-2px);
+        box-shadow: 0 4px 8px rgba(0,0,0,0.2);
+    }
+    .success-box {
+        background-color: #d4edda;
+        border: 1px solid #c3e6cb;
+        border-radius: 5px;
+        padding: 10px;
+        margin: 10px 0;
+    }
+    .warning-box {
+        background-color: #fff3cd;
+        border: 1px solid #ffeaa7;
+        border-radius: 5px;
+        padding: 10px;
+        margin: 10px 0;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# 導入自定義模組
+from bayesian_core import BayesianHierarchicalAnalyzer
+from bayesian_llm_assistant import BayesianLLMAssistant
+from bayesian_utils import (
+    plot_trace,
+    plot_posterior,
+    plot_forest,
+    plot_model_dag,
+    create_summary_table,
+    create_trial_results_table,
+    export_results_to_text,
+    plot_odds_ratio_comparison
+)
+
+# 清理函數
+def cleanup_old_sessions():
+    """清理超過 1 小時的 session"""
+    current_time = datetime.now()
+    for session_id in list(BayesianHierarchicalAnalyzer._session_results.keys()):
+        result = BayesianHierarchicalAnalyzer._session_results.get(session_id)
+        if result:
+            result_time = datetime.fromisoformat(result['timestamp'])
+            if current_time - result_time > timedelta(hours=1):
+                BayesianHierarchicalAnalyzer.clear_session_results(session_id)
+
+# 註冊清理函數
+atexit.register(cleanup_old_sessions)
+
+# 初始化 session state
+if 'session_id' not in st.session_state:
+    st.session_state.session_id = str(uuid.uuid4())
+if 'analysis_results' not in st.session_state:
+    st.session_state.analysis_results = None
+if 'chat_history' not in st.session_state:
+    st.session_state.chat_history = []
+if 'analyzer' not in st.session_state:
+    st.session_state.analyzer = None
+if 'trace_img' not in st.session_state:
+    st.session_state.trace_img = None
+if 'posterior_img' not in st.session_state:
+    st.session_state.posterior_img = None
+if 'forest_img' not in st.session_state:
+    st.session_state.forest_img = None
+if 'dag_img' not in st.session_state:
+    st.session_state.dag_img = None
+
+# 標題
+st.title("🎲 Bayesian Hierarchical Model Analysis")
+st.markdown("### 寶可夢速度對勝率影響的貝氏階層分析")
+st.markdown("---")
+
+# Sidebar
+with st.sidebar:
+    st.header("⚙️ 配置設定")
+    
+    # Google Gemini API Key
+    api_key = st.text_input(
+        "Google Gemini API Key",
+        type="password",
+        help="輸入您的 Google Gemini API Key 以使用 AI 助手"
+    )
+    
+    if api_key:
+        st.session_state.api_key = api_key
+        st.success("✅ API Key 已載入")
+    
+    st.markdown("---")
+    
+    # MCMC 參數設定
+    st.subheader("🔬 MCMC 參數")
+    
+    n_samples = st.number_input(
+        "抽樣數 (Samples)",
+        min_value=500,
+        max_value=10000,
+        value=2000,
+        step=500,
+        help="每條鏈的抽樣數量"
+    )
+    
+    n_tune = st.number_input(
+        "調整期 (Tune)",
+        min_value=200,
+        max_value=5000,
+        value=1000,
+        step=200,
+        help="調整期的樣本數"
+    )
+    
+    n_chains = st.selectbox(
+        "鏈數 (Chains)",
+        options=[1, 2, 4],
+        index=1,
+        help="平行運行的鏈數"
+    )
+    
+    target_accept = st.slider(
+        "目標接受率",
+        min_value=0.80,
+        max_value=0.99,
+        value=0.95,
+        step=0.01,
+        help="NUTS 採樣器的目標接受率"
+    )
+    
+    st.markdown("---")
+    
+    # 清理按鈕
+    if st.button("🧹 清理過期資料"):
+        cleanup_old_sessions()
+        st.success("✅ 清理完成")
+        st.rerun()
+    
+    st.markdown("---")
+    
+    # 資料來源選擇
+    st.subheader("📊 資料來源")
+    data_source = st.radio(
+        "選擇資料來源：",
+        ["使用預設資料集", "上傳您的資料"]
+    )
+    
+    uploaded_file = None
+    if data_source == "上傳您的資料":
+        uploaded_file = st.file_uploader(
+            "上傳 CSV 檔案",
+            type=['csv'],
+            help="上傳寶可夢速度對戰資料"
+        )
+        
+        with st.expander("📖 資料格式說明"):
+            st.markdown("""
+            **必要欄位格式：**
+            - `Trial_Type`: 屬性名稱（例如：Water, Fire, Grass）
+            - `rc`: 控制組（速度慢）的勝場數
+            - `nc`: 控制組的總場數
+            - `rt`: 實驗組（速度快）的勝場數
+            - `nt`: 實驗組的總場數
+            
+            **範例：**
+            ```
+            Trial_Type,rc,nc,rt,nt
+            Water,45,100,62,100
+            Fire,38,100,55,100
+            Grass,42,100,58,100
+            ```
+            """)
+    
+    st.markdown("---")
+    
+    # 關於系統
+    with st.expander("ℹ️ 關於此系統"):
+        st.markdown("""
+        **貝氏階層模型分析系統**
+        
+        本系統使用貝氏階層模型來分析速度對寶可夢勝率的影響，
+        並考慮不同屬性之間的異質性。
+        
+        **主要功能：**
+        - 🎲 貝氏推論與後驗分佈
+        - 📊 階層模型（借用資訊）
+        - 📈 4 種視覺化圖表
+        - 💬 AI 助手解釋
+        - 🎮 對戰策略建議
+        
+        **適用場景：**
+        - 分析速度對不同屬性的影響
+        - 理解屬性間的異質性
+        - 制定基於統計的對戰策略
+        """)
+
+# 主要內容區 - 雙 Tab
+tab1, tab2 = st.tabs(["📊 貝氏分析", "💬 AI 助手"])
+
+# Tab 1: 貝氏分析
+with tab1:
+    st.header("📊 貝氏階層模型分析")
+    
+    # 載入資料
+    if data_source == "使用預設資料集":
+        # 檢查預設資料是否存在
+        default_data_path = "pokemon_speed_meta_results.csv"
+        if os.path.exists(default_data_path):
+            df = pd.read_csv(default_data_path)
+            st.success(f"✅ 已載入預設資料集（{len(df)} 個屬性）")
+        else:
+            st.warning("⚠️ 找不到預設資料集，請上傳您的資料")
+            df = None
+    else:
+        if uploaded_file is not None:
+            df = pd.read_csv(uploaded_file)
+            st.success(f"✅ 已載入資料（{len(df)} 個屬性）")
+        else:
+            df = None
+            st.info("📁 請在左側上傳 CSV 檔案")
+    
+    if df is not None:
+        # 顯示資料預覽
+        with st.expander("👀 資料預覽"):
+            st.dataframe(df, use_container_width=True)
+        
+        st.markdown("---")
+        
+        # 分析按鈕
+        col1, col2, col3 = st.columns([1, 2, 1])
+        
+        with col2:
+            analyze_button = st.button(
+                "🔬 開始貝氏分析",
+                type="primary",
+                use_container_width=True
+            )
+        
+        # 執行分析
+        if analyze_button:
+            with st.spinner(f"正在執行貝氏分析... (抽樣 {n_samples} × {n_chains} 條鏈)"):
+                try:
+                    # 初始化分析器
+                    if st.session_state.analyzer is None:
+                        st.session_state.analyzer = BayesianHierarchicalAnalyzer(st.session_state.session_id)
+                    
+                    # 載入資料
+                    st.session_state.analyzer.load_data(df)
+                    
+                    # 執行分析
+                    results = st.session_state.analyzer.run_analysis(
+                        n_samples=n_samples,
+                        n_tune=n_tune,
+                        n_chains=n_chains,
+                        target_accept=target_accept
+                    )
+                    
+                    st.session_state.analysis_results = results
+                    
+                    # 生成圖表
+                    with st.spinner("生成視覺化圖表..."):
+                        st.session_state.trace_img = plot_trace(st.session_state.analyzer.trace)
+                        st.session_state.posterior_img = plot_posterior(st.session_state.analyzer.trace)
+                        st.session_state.forest_img = plot_forest(
+                            st.session_state.analyzer.trace,
+                            results['trial_labels']
+                        )
+                        st.session_state.dag_img = plot_model_dag(st.session_state.analyzer)
+                    
+                    st.success("✅ 分析完成！")
+                    st.balloons()
+                    
+                except Exception as e:
+                    st.error(f"❌ 分析失敗: {str(e)}")
+        
+        # 顯示結果
+        if st.session_state.analysis_results is not None:
+            results = st.session_state.analysis_results
+            
+            st.markdown("---")
+            st.subheader("📊 分析結果")
+            
+            # 創建 4 個子頁面
+            result_tabs = st.tabs([
+                "📊 概覽",
+                "📈 Trace & Posterior",
+                "🌲 Forest Plot",
+                "🔍 DAG 模型圖",
+                "📋 詳細報告"
+            ])
+            
+            # Tab: 概覽
+            with result_tabs[0]:
+                st.markdown("### 🎯 整體效應摘要")
+                
+                overall = results['overall']
+                interp = results['interpretation']
+                
+                # 關鍵指標
+                col1, col2, col3 = st.columns(3)
+                
+                with col1:
+                    st.metric(
+                        "d (整體效應)",
+                        f"{overall['d_mean']:.4f}",
+                        delta=f"HDI: [{overall['d_hdi_low']:.3f}, {overall['d_hdi_high']:.3f}]"
+                    )
+                
+                with col2:
+                    st.metric(
+                        "勝算比 (OR)",
+                        f"{overall['or_mean']:.3f}",
+                        delta=f"HDI: [{overall['or_hdi_low']:.3f}, {overall['or_hdi_high']:.3f}]"
+                    )
+                
+                with col3:
+                    st.metric(
+                        "sigma (異質性)",
+                        f"{overall['sigma_mean']:.4f}",
+                        delta=f"HDI: [{overall['sigma_hdi_low']:.3f}, {overall['sigma_hdi_high']:.3f}]"
+                    )
+                
+                st.markdown("---")
+                
+                # 結果解釋
+                st.markdown("### 📖 結果解釋")
+                
+                st.info(f"""
+                **整體效應**: {interp['overall_effect']}
+                
+                **顯著性**: {interp['overall_significance']}
+                
+                **效果大小**: {interp['effect_size']}
+                
+                **異質性**: {interp['heterogeneity']}
+                """)
+                
+                st.markdown("---")
+                
+                # 收斂診斷
+                st.markdown("### 🔍 模型收斂診斷")
+                
+                diag = results['diagnostics']
+                
+                col1, col2 = st.columns(2)
+                
+                with col1:
+                    st.markdown("**R-hat 診斷** (應 < 1.1):")
+                    if diag['rhat_d']:
+                        st.metric("R-hat (d)", f"{diag['rhat_d']:.4f}", 
+                                 delta="✓ 良好" if diag['rhat_d'] < 1.1 else "✗ 需改善")
+                    if diag['rhat_sigma']:
+                        st.metric("R-hat (sigma)", f"{diag['rhat_sigma']:.4f}",
+                                 delta="✓ 良好" if diag['rhat_sigma'] < 1.1 else "✗ 需改善")
+                
+                with col2:
+                    st.markdown("**有效樣本數 (ESS)**:")
+                    if diag['ess_d']:
+                        st.metric("ESS (d)", f"{int(diag['ess_d'])}")
+                    if diag['ess_sigma']:
+                        st.metric("ESS (sigma)", f"{int(diag['ess_sigma'])}")
+                
+                if diag['converged']:
+                    st.success("✅ 模型已收斂，結果可信")
+                else:
+                    st.warning("⚠️ 模型可能未完全收斂，建議增加抽樣數或鏈數")
+                
+                st.markdown("---")
+                
+                # 摘要表格
+                st.markdown("### 📊 統計摘要表")
+                summary_df = create_summary_table(results)
+                st.dataframe(summary_df, use_container_width=True)
+                
+                st.markdown("---")
+                
+                # 各屬性結果
+                st.markdown("### 🎮 各屬性詳細結果")
+                trial_df = create_trial_results_table(results)
+                st.dataframe(trial_df, use_container_width=True)
+                
+                st.markdown("---")
+                
+                # 勝算比比較圖
+                st.markdown("### 📊 各屬性速度效應比較")
+                or_fig = plot_odds_ratio_comparison(results)
+                st.plotly_chart(or_fig, use_container_width=True)
+            
+            # Tab: Trace & Posterior
+            with result_tabs[1]:
+                st.markdown("### 📈 Trace Plot（收斂診斷）")
+                st.markdown("""
+                **Trace Plot 用途**：
+                - 檢查 MCMC 抽樣是否收斂
+                - 左圖：抽樣軌跡（應該像「毛毛蟲」）
+                - 右圖：後驗分佈密度
+                """)
+                
+                if st.session_state.trace_img:
+                    st.image(st.session_state.trace_img, use_column_width=True)
+                else:
+                    st.info("請先執行分析以生成 Trace Plot")
+                
+                st.markdown("---")
+                
+                st.markdown("### 📊 Posterior Plot（後驗分佈）")
+                st.markdown("""
+                **Posterior Plot 用途**：
+                - 顯示參數的後驗分佈
+                - 包含 95% HDI（最高密度區間）
+                - 顯示平均值
+                """)
+                
+                if st.session_state.posterior_img:
+                    st.image(st.session_state.posterior_img, use_column_width=True)
+                else:
+                    st.info("請先執行分析以生成 Posterior Plot")
+            
+            # Tab: Forest Plot
+            with result_tabs[2]:
+                st.markdown("### 🌲 Forest Plot（各屬性效應）")
+                st.markdown("""
+                **Forest Plot 用途**：
+                - 顯示每個屬性的速度效應（delta）
+                - 點：平均效應
+                - 線：95% HDI
+                - ★ 標記：顯著正效應（HDI 不包含 0）
+                - ☆ 標記：顯著負效應
+                """)
+                
+                if st.session_state.forest_img:
+                    st.image(st.session_state.forest_img, use_column_width=True)
+                else:
+                    st.info("請先執行分析以生成 Forest Plot")
+            
+            # Tab: DAG 模型圖
+            with result_tabs[3]:
+                st.markdown("### 🔍 模型結構圖 (DAG)")
+                st.markdown("""
+                **DAG（有向無環圖）用途**：
+                - 視覺化模型的階層結構
+                - 顯示變數之間的依賴關係
+                - 圓形/橢圓：隨機變數
+                - 矩形：觀測資料
+                - 菱形：推導變數
+                """)
+                
+                if st.session_state.dag_img:
+                    st.image(st.session_state.dag_img, use_column_width=True)
+                else:
+                    st.warning("⚠️ 無法生成 DAG 圖（可能需要安裝 Graphviz）")
+                    st.markdown("""
+                    **安裝 Graphviz:**
+                    - Windows: `choco install graphviz`
+                    - Mac: `brew install graphviz`
+                    - Ubuntu: `sudo apt-get install graphviz`
+                    """)
+            
+            # Tab: 詳細報告
+            with result_tabs[4]:
+                st.markdown("### 📋 完整分析報告")
+                
+                # 生成文字報告
+                text_report = export_results_to_text(results)
+                
+                st.text_area(
+                    "報告內容",
+                    text_report,
+                    height=500
+                )
+                
+                # 下載按鈕
+                st.download_button(
+                    label="📥 下載完整報告 (.txt)",
+                    data=text_report,
+                    file_name=f"bayesian_report_{results['timestamp'][:10]}.txt",
+                    mime="text/plain"
+                )
+
+# Tab 2: AI 助手
+with tab2:
+    st.header("💬 AI 分析助手")
+    
+    if not st.session_state.get('api_key'):
+        st.warning("⚠️ 請在左側輸入您的 Google Gemini API Key 以使用 AI 助手")
+    elif st.session_state.analysis_results is None:
+        st.info("ℹ️ 請先在「貝氏分析」頁面執行分析")
+    else:
+        # 初始化 LLM 助手
+        if 'llm_assistant' not in st.session_state:
+            st.session_state.llm_assistant = BayesianLLMAssistant(
+                api_key=st.session_state.api_key,
+                session_id=st.session_state.session_id
+            )
+        
+        # 聊天容器
+        chat_container = st.container()
+        
+        with chat_container:
+            for message in st.session_state.chat_history:
+                with st.chat_message(message["role"]):
+                    st.markdown(message["content"])
+        
+        # 使用者輸入
+        if prompt := st.chat_input("詢問關於分析結果的任何問題..."):
+            # 添加使用者訊息
+            st.session_state.chat_history.append({
+                "role": "user",
+                "content": prompt
+            })
+            
+            with st.chat_message("user"):
+                st.markdown(prompt)
+            
+            # AI 回應
+            with st.chat_message("assistant"):
+                with st.spinner("思考中..."):
+                    try:
+                        response = st.session_state.llm_assistant.get_response(
+                            user_message=prompt,
+                            analysis_results=st.session_state.analysis_results
+                        )
+                        st.markdown(response)
+                    except Exception as e:
+                        error_msg = f"❌ 錯誤: {str(e)}\n\n請檢查 API key 或重新表達問題。"
+                        st.error(error_msg)
+                        response = error_msg
+            
+            # 添加助手回應
+            st.session_state.chat_history.append({
+                "role": "assistant",
+                "content": response
+            })
+        
+        st.markdown("---")
+        
+        # 快速問題按鈕
+        st.subheader("💡 快速問題")
+        
+        quick_questions = [
+            "📊 給我這次分析的總結",
+            "🎯 解釋 d 和勝算比",
+            "🔍 解釋 sigma（異質性）",
+            "❓ 什麼是階層模型？",
+            "🆚 貝氏 vs 頻率論",
+            "⚔️ 對戰策略建議",
+            "🎮 比較不同屬性"
+        ]
+        
+        cols = st.columns(4)
+        for idx, question in enumerate(quick_questions):
+            col_idx = idx % 4
+            if cols[col_idx].button(question, key=f"quick_{idx}"):
+                # 根據問題選擇對應的方法
+                if "總結" in question:
+                    response = st.session_state.llm_assistant.generate_summary(
+                        st.session_state.analysis_results
+                    )
+                elif "d 和勝算比" in question:
+                    response = st.session_state.llm_assistant.explain_metric(
+                        'd',
+                        st.session_state.analysis_results
+                    )
+                elif "sigma" in question or "異質性" in question:
+                    response = st.session_state.llm_assistant.explain_metric(
+                        'sigma',
+                        st.session_state.analysis_results
+                    )
+                elif "階層模型" in question:
+                    response = st.session_state.llm_assistant.explain_hierarchical_model()
+                elif "貝氏" in question and "頻率論" in question:
+                    response = st.session_state.llm_assistant.explain_bayesian_vs_frequentist()
+                elif "策略" in question:
+                    response = st.session_state.llm_assistant.battle_strategy_advice(
+                        st.session_state.analysis_results
+                    )
+                elif "比較" in question:
+                    response = st.session_state.llm_assistant.compare_types(
+                        st.session_state.analysis_results
+                    )
+                else:
+                    response = st.session_state.llm_assistant.get_response(
+                        question,
+                        st.session_state.analysis_results
+                    )
+                
+                st.session_state.chat_history.append({
+                    "role": "user",
+                    "content": question
+                })
+                
+                st.session_state.chat_history.append({
+                    "role": "assistant",
+                    "content": response
+                })
+                
+                st.rerun()
+        
+        # 重置對話按鈕
+        st.markdown("---")
+        if st.button("🔄 重置對話"):
+            st.session_state.llm_assistant.reset_conversation()
+            st.session_state.chat_history = []
+            st.success("✅ 對話已重置")
+            st.rerun()
+
+# Footer
+st.markdown("---")
+st.markdown(
+    f"""
+    <div style='text-align: center'>
+        <p>🎲 Bayesian Hierarchical Model Analysis for Pokémon Speed | Built with Streamlit & PyMC</p>
+        <p>Session ID: {st.session_state.session_id[:8]} | Powered by Google Gemini 2.0 Flash</p>
+    </div>
+    """,
+    unsafe_allow_html=True
+)