調整畫面小數點呈現

app.py

@@ -19,7 +19,12 @@ warnings.filterwarnings("ignore", category=FutureWarning)
 # plt.rcParams['font.family'] = ['SimHei']
 plt.rcParams['axes.unicode_minus'] = False
 
+def processDisplayDataframe(df):
+    # 假設 df 已經存在
+    num_cols = df.select_dtypes(include=np.number).columns  # 先抓出數值欄位名稱
 
+    df[num_cols] = df[num_cols].map(lambda x: f"{x:.4f}")
+    return df
 
 def load_data():
     """
@@ -48,6 +53,9 @@ def update_eda_section(selected_features):
     corrs.columns = ['Feature', 'Correlation with Response']
     first_feature_plot = generate_feature_plot(selected_features[0])
     plot_selector_update = gr.update(choices=selected_features, value=selected_features[0])
+    
+    stats = processDisplayDataframe(stats)
+    corrs = processDisplayDataframe(corrs)
     return stats, corrs, plot_selector_update, first_feature_plot
 
 def generate_feature_plot(feature):
@@ -122,7 +130,13 @@ def train_and_evaluate(history_log, model_name, features, dt_criterion, dt_max_d
 
     # --- 3. 評估與繪圖 ---
     accuracy = accuracy_score(y_test, y_pred)
-    report = classification_report(y_test, y_pred, target_names=['not purchase insurance (0)', 'purchase insurance (1)'])
+    report_dict = classification_report(y_test, y_pred, target_names=['not purchase insurance (0)', 'purchase insurance (1)'], output_dict=True)
+    # 2. 轉成 DataFrame（每個類別一列）
+    df_report = pd.DataFrame(report_dict).T  # T = transpose，讓 index 變成類別名稱
+    df_report.insert(0, "index",  df_report.index)
+    # print(df_report)
+    df_report = processDisplayDataframe(df_report)
+    
     auc_score = f"ROC-AUC 分數: {roc_auc_score(y_test, y_pred_proba):.4f}"
     cm = confusion_matrix(y_test, y_pred)
     fig_cm, ax_cm = plt.subplots(); sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=ax_cm, xticklabels=['Predicted 0', 'Predicted 1'], yticklabels=['Actual 0', 'Actual 1']); ax_cm.set_title('Confusion Matrix'); ax_cm.set_xlabel('Predicted Label'); ax_cm.set_ylabel('Actual Label'); plt.tight_layout()
@@ -147,7 +161,7 @@ def train_and_evaluate(history_log, model_name, features, dt_criterion, dt_max_d
     updated_log = [new_log_entry] + history_log
     log_df = pd.DataFrame(updated_log, columns=LOG_COLUMNS)
 
-    return report, auc_score, fig_cm, fig_imp, log_df, updated_log
+    return df_report, auc_score, fig_cm, fig_imp, log_df, updated_log
 
 # --- Gradio 介面設計 ---
 LOG_COLUMNS = ["時間", "模型", "特徵", "參數", "準確率"]
@@ -156,7 +170,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     # 用於儲存日誌的隱藏狀態元件
     log_state = gr.State([])
 
-    gr.Markdown("# 互動式投保預測模型分析器")
+    gr.Markdown("# 投保預測模型建置專案")
     gr.Markdown("在左側選擇特徵並點擊按鈕進行探索，或調整參數後點擊按鈕以訓練模型。")
     with gr.Row():
         with gr.Column(scale=1):
@@ -183,7 +197,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             run_btn = gr.Button("🚀 執行模型訓練", variant="primary")
         with gr.Column(scale=2):
             gr.Markdown("## 3. 模型評估結果")
-            model_output_report = gr.Textbox(label="分類報告", lines=10)
+            model_output_report = gr.DataFrame(label="分類報告")
             model_output_auc = gr.Textbox(label="AUC 分數")
             model_plot_cm = gr.Plot(label="混淆矩陣")
             model_plot_importance = gr.Plot(label="特徵重要性/係數")