Upload 4 files

app_bayesian.py

@@ -110,7 +110,7 @@ if 'dag_img' not in st.session_state:
 
 # 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("### 火系 vs 水系寶可夢配對勝率的貝氏階層分析")
 st.markdown("---")
 
 # Sidebar
@@ -211,23 +211,24 @@ with st.sidebar:
         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
+            - `Trial_Type`: 配對名稱（例如：Pair_1, Pair_2）
+            - `rt`: 火系（治療組）的勝場數
+            - `nt`: 火系的總場數
+            - `rc`: 水系（對照組）的勝場數
+            - `nc`: 水系的總場數
             
             **範例：**
             ```
-            Trial_Type,rc,nc,rt,nt
-            Water,45,100,62,100
-            Fire,38,100,55,100
-            Grass,42,100,58,100
+            Trial_Type,rt,nt,rc,nc
+            Pair_1,122,133,22,145
+            Pair_2,85,132,17,135
+            Pair_3,52,129,41,134
             ```
-            """)
-    
+            """)   
+            
     st.markdown("---")
     
+    
     # 關於系統
     with st.expander("ℹ️ 關於此系統"):
         st.markdown("""
@@ -241,12 +242,12 @@ with st.sidebar:
         - 📊 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
-        
+        - 🎮 屬性對抗策略建議
+
         **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
+        - 分析火系對水系的配對勝率
+        - 理解不同配對間的異質性
+        - 評估屬性優劣勢
         """)
 
 # 主要內容區 - 雙 Tab
@@ -259,17 +260,17 @@ with tab1:
     # 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"
+        default_data_path = "fire_water_converted.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）")
+            st.success(f"✅ 已載入預設資料集（{len(df)} 組配對）")
         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）")
+            st.success(f"✅ 已載入資料（{len(df)} 組配對）")
         else:
             df = None
             st.info("📁 請在左側上傳 CSV 檔案")

bayesian_core.py

@@ -245,37 +245,42 @@ class BayesianHierarchicalAnalyzer:
     
     def _interpret_results(self, or_mean, or_low, or_high, sigma_mean):
         """解釋分析結果"""
-        # 整體效應顯著性
+        # 整體效應顯著性       
         if or_low > 1:
-            overall_effect = "速度快的寶可夢顯著更容易獲勝"
+            overall_effect = "火系寶可夢相對於水系顯著更容易獲勝"
             overall_significance = "顯著正效應"
         elif or_high < 1:
-            overall_effect = "速度慢的寶可夢顯著更容易獲勝（罕見）"
+            overall_effect = "水系寶可夢相對於火系顯著更容易獲勝"
             overall_significance = "顯著負效應"
         else:
-            overall_effect = "速度對勝率無顯著影響"
-            overall_significance = "不顯著"
-        
-        # 效果大小
+            overall_effect = "火系與水系勝率無顯著差異"
+            overall_significance = "不顯著"        
+
+        # 效果大小           
         if or_mean > 2:
-            effect_size = "大效果 (OR > 2)"
+            effect_size = "大效果 (OR > 2) - 火系有明顯優勢"
         elif or_mean > 1.5:
-            effect_size = "中等效果 (OR > 1.5)"
+            effect_size = "中等效果 (OR > 1.5) - 火系有一定優勢"
         elif or_mean > 1:
-            effect_size = "小效果 (OR > 1)"
+            effect_size = "小效果 (OR > 1) - 火系略有優勢"
         elif or_mean == 1:
-            effect_size = "無差異 (OR = 1)"
+            effect_size = "無差異 (OR = 1) - 火系與水系勢均力敵"
+        elif or_mean > 0.67:
+            effect_size = "小效果 (OR < 1) - 水系略有優勢"
+        elif or_mean > 0.5:
+            effect_size = "中等效果 (OR < 0.67) - 水系有一定優勢"
         else:
-            effect_size = "反向效果 (OR < 1)"
+            effect_size = "大效果 (OR < 0.5) - 水系有明顯優勢"          
+                    
         
         # 異質性評估
         if sigma_mean > 0.5:
-            heterogeneity = "高異質性 - 不同屬性對速度的反應差異很大"
+            heterogeneity = "高異質性 - 不同配對的勝率差異很大"
         elif sigma_mean > 0.3:
-            heterogeneity = "中等異質性 - 不同屬性對速度的反應有一定差異"
+            heterogeneity = "中等異質性 - 不同配對的勝率有一定差異"
         else:
-            heterogeneity = "低異質性 - 不同屬性對速度的反應相似"
-        
+            heterogeneity = "低異質性 - 不同配對的勝率相對一致"        
+              
         return {
             'overall_effect': overall_effect,
             'overall_significance': overall_significance,

bayesian_llm_assistant.py

@@ -47,8 +47,8 @@ class BayesianLLMAssistant:
 
 你是一位精通貝氏階層模型和統合分析的統計專家，特別專注於寶可夢對戰統計分析。
 
-Your role is to help users understand Bayesian hierarchical model results analyzing how speed affects win rates across different Pokémon types.
-你的角色是幫助使用者理解貝氏階層模型分析結果，了解速度如何影響不同屬性寶可夢的勝率。
+Your role is to help users understand Bayesian hierarchical model results analyzing Fire vs Water Pokémon matchup win rates across different paired configurations.
+你的角色是幫助使用者理解貝氏階層模型分析結果，了解火系對水系寶可夢在不同配對組合下的勝率差異。
 
 **NEW CAPABILITY: DAG Diagram Generation | 新能力：DAG 圖生成**
 When users ask you to draw, create, or visualize a DAG (Directed Acyclic Graph) or model structure, you can generate Graphviz DOT code.
@@ -360,7 +360,8 @@ Format responses with proper markdown for better readability.
 
 ### Key Finding | 關鍵發現
 {
-    f"On average, faster Pokémon are {overall['or_mean']:.2f} times more likely to win than slower ones (95% HDI: [{overall['or_hdi_low']:.2f}, {overall['or_hdi_high']:.2f}]). 平均而言，速度較快的寶可夢獲勝的可能性是速度較慢者的 {overall['or_mean']:.2f} 倍（95% HDI: [{overall['or_hdi_low']:.2f}, {overall['or_hdi_high']:.2f}]）。"
+    f"On average, Fire-type Pokémon are {overall['or_mean']:.2f} times more likely to win compared to Water-type (95% HDI: [{overall['or_hdi_low']:.2f}, {overall['or_hdi_high']:.2f}]). 平均而言，火系寶可夢獲勝的可能性是水系的 {overall['or_mean']:.2f} 倍 （95% HDI: [{overall['or_hdi_low']:.2f}, {overall['or_hdi_high']:.2f}]）。"
+
     if overall['or_mean'] > 1
     else f"Interestingly, the data suggests no clear speed advantage or even a slight disadvantage. 有趣的是，資料顯示速度並無明顯優勢，甚至可能略有劣勢。"
 }

fire_water_converted.csv

@@ -0,0 +1,47 @@
+Trial_Type,rt,nt,rc,nc
+Pair_1,122,133,22,145
+Pair_2,85,132,17,135
+Pair_3,52,129,41,134
+Pair_4,69,126,76,132
+Pair_5,50,125,98,132
+Pair_6,103,122,67,131
+Pair_7,37,122,18,127
+Pair_8,90,119,60,127
+Pair_9,81,119,25,125
+Pair_10,107,117,105,125
+Pair_11,102,116,55,123
+Pair_12,56,116,37,123
+Pair_13,91,115,65,122
+Pair_14,65,115,74,122
+Pair_15,78,114,56,122
+Pair_16,97,112,75,121
+Pair_17,76,112,33,121
+Pair_18,94,112,27,121
+Pair_19,102,112,61,121
+Pair_20,59,111,59,121
+Pair_21,91,111,97,121
+Pair_22,41,110,52,120
+Pair_23,9,109,56,120
+Pair_24,38,109,66,119
+Pair_25,31,108,66,119
+Pair_26,80,108,32,119
+Pair_27,52,107,83,119
+Pair_28,69,107,44,119
+Pair_29,51,106,72,119
+Pair_30,85,106,104,119
+Pair_31,74,106,65,118
+Pair_32,30,105,109,118
+Pair_33,59,104,77,118
+Pair_34,4,104,42,117
+Pair_35,19,104,76,117
+Pair_36,81,103,103,117
+Pair_37,50,102,31,117
+Pair_38,65,102,54,116
+Pair_39,53,101,20,116
+Pair_40,89,101,41,116
+Pair_41,24,101,66,116
+Pair_42,75,98,67,115
+Pair_43,44,97,27,114
+Pair_44,52,96,39,114
+Pair_45,83,92,76,114
+Pair_46,69,89,105,114