Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import VotingClassifier, StackingClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, auc, classification_report
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# 設定 Streamlit 介面標題
+st.title('分類模型比較：堆疊與投票分類器')
+
+# 讓使用者上傳資料
+uploaded_file = st.file_uploader("請上傳 CSV 檔案", type=["csv"])
+
+if uploaded_file is not None:
+    df = pd.read_csv(uploaded_file)
+
+    # 定義特徵與目標變數
+    X = df.drop(columns=['Target_goal'])
+    y = df['Target_goal']
+
+    # 分割數據集
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # 標準化數據
+    scaler = StandardScaler()
+    X_train = scaler.fit_transform(X_train)
+    X_test = scaler.transform(X_test)
+
+    # 定義基礎模型
+    estimators = [
+        ('lr', LogisticRegression()),
+        ('dt', DecisionTreeClassifier()),
+        ('rf', RandomForestClassifier()),
+        ('gb', GradientBoostingClassifier()),
+        ('svc', SVC(probability=True))
+    ]
+
+    # 堆疊分類器
+    stacking_clf = StackingClassifier(
+        estimators=estimators,
+        final_estimator=LogisticRegression()
+    )
+    stacking_clf.fit(X_train, y_train)
+    y_pred_stack = stacking_clf.predict(X_test)
+    y_pred_stack_proba = stacking_clf.predict_proba(X_test)[:, 1]
+    
+    # 堆疊分類器準確性
+    accuracy_stack = accuracy_score(y_test, y_pred_stack)
+    st.write(f'堆疊分類器的準確性: {accuracy_stack:.2f}')
+    
+    # 堆疊分類器的分類報告
+    st.write("堆疊分類器的分類報告:")
+    st.text(classification_report(y_test, y_pred_stack))
+
+    # 投票分類器
+    voting_clf = VotingClassifier(
+        estimators=estimators,
+        voting='soft'
+    )
+    voting_clf.fit(X_train, y_train)
+    y_pred_vote = voting_clf.predict(X_test)
+    y_pred_vote_proba = voting_clf.predict_proba(X_test)[:, 1]
+    
+    # 投票分類器準確性
+    accuracy_vote = accuracy_score(y_test, y_pred_vote)
+    st.write(f'投票分類器的準確性: {accuracy_vote:.2f}')
+    
+    # 投票分類器的分類報告
+    st.write("投票分類器的分類報告:")
+    st.text(classification_report(y_test, y_pred_vote))
+
+    # 混淆矩陣可視化
+    st.write("堆疊分類器的混淆矩陣：")
+    conf_matrix_stack = confusion_matrix(y_test, y_pred_stack)
+    fig, ax = plt.subplots()
+    sns.heatmap(conf_matrix_stack, annot=True, fmt='d', cmap='Blues', ax=ax)
+    ax.set_title('堆疊分類器的混淆矩陣')
+    st.pyplot(fig)
+
+    st.write("投票分類器的混淆矩陣：")
+    conf_matrix_vote = confusion_matrix(y_test, y_pred_vote)
+    fig, ax = plt.subplots()
+    sns.heatmap(conf_matrix_vote, annot=True, fmt='d', cmap='Blues', ax=ax)
+    ax.set_title('投票分類器的混淆矩陣')
+    st.pyplot(fig)
+
+    # ROC 曲線
+    fpr_stack, tpr_stack, _ = roc_curve(y_test, y_pred_stack_proba)
+    roc_auc_stack = auc(fpr_stack, tpr_stack)
+
+    fpr_vote, tpr_vote, _ = roc_curve(y_test, y_pred_vote_proba)
+    roc_auc_vote = auc(fpr_vote, tpr_vote)
+
+    fig, ax = plt.subplots()
+    ax.plot(fpr_stack, tpr_stack, color='blue', lw=2, label='堆疊分類器 (AUC = %0.2f)' % roc_auc_stack)
+    ax.plot(fpr_vote, tpr_vote, color='red', lw=2, label='投票分類器 (AUC = %0.2f)' % roc_auc_vote)
+    ax.plot([0, 1], [0, 1], color='gray', lw=1, linestyle='--')
+    ax.set_xlim([0.0, 1.0])
+    ax.set_ylim([0.0, 1.05])
+    ax.set_xlabel('假陽性率（False Positive Rate）')
+    ax.set_ylabel('真陽性率（True Positive Rate）')
+    ax.set_title('ROC 曲線')
+    ax.legend(loc="lower right")
+    st.pyplot(fig)