Update file diabetes.py

diabetes.py

@@ -105,7 +105,6 @@ class DiabetesRiskApp(QMainWindow):
     # Data preprocessing including outlier handling and normalization
     def data_preprocess(self):
         if self.data is not None:
-             # Select target variable
             target_variable = self.comboOutcome.currentText()
             if target_variable:
                 # Separate target variable and features
@@ -231,7 +230,6 @@ class DiabetesRiskApp(QMainWindow):
             if y.dtype != 'int' and y.dtype != 'object':
                 y = (y > y.mean()).astype(int)
         
-            # Split into training and testing sets
             X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
         
             param_grid = {
@@ -242,7 +240,6 @@ class DiabetesRiskApp(QMainWindow):
             lr_model = GridSearchCV(LogisticRegression(random_state=42), param_grid, cv=3, scoring='roc_auc')
             lr_model.fit(X_train, y_train)
 
-            # Evaluate model performance on test set
             best_model = lr_model.best_estimator_
             y_pred = best_model.predict(X_test)
             y_pred_proba = best_model.predict_proba(X_test)[:, 1]
@@ -263,7 +260,6 @@ class DiabetesRiskApp(QMainWindow):
                                 f"Best Parameters: {lr_model.best_params_}\nAccuracy: {accuracy:.2f}\nAUC: {roc_auc:.2f}")
     
     def display_model_results(self, accuracy, fpr, tpr, roc_auc):
-        # Show model performance metrics and ROC curve
         plt.figure()
         plt.plot(fpr, tpr, label='AUC = %0.2f' % roc_auc)
         plt.plot([0, 1], [0, 1], 'r--')
@@ -287,17 +283,14 @@ class DiabetesRiskApp(QMainWindow):
             X = self.data.drop(columns=[outcome_col])
             y = self.data[outcome_col]
 
-            # 连续目标变量二值化
             if y.dtype != 'int' and y.dtype != 'object':
                 y = (y > y.mean()).astype(int)
         
             X = pd.get_dummies(X, drop_first=True)
             X = X.astype(float)
             
-            # Split into training and testing sets
             X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
         
-            # Define list of models
             models = [
                 ('Logistic Regression', LogisticRegression(max_iter=1000)),
                 ('Random Forest', RandomForestClassifier(n_estimators=100, random_state=42)),
@@ -312,13 +305,11 @@ class DiabetesRiskApp(QMainWindow):
             model_results = []
             plt.figure(figsize=(10, 8))
             
-            # Model comparison analysis
             for name, model in models:
                 model.fit(X_train, y_train)
                 y_pred = model.predict(X_test)
                 y_proba = model.predict_proba(X_test)[:, 1] if hasattr(model, "predict_proba") else None
 
-         
                 accuracy = accuracy_score(y_test, y_pred)
                 precision = precision_score(y_test, y_pred)
                 recall = recall_score(y_test, y_pred)
@@ -344,17 +335,14 @@ class DiabetesRiskApp(QMainWindow):
             self.display_model_comparison(model_results)
 
     def display_model_comparison(self, model_results):
-        # Create dialog
         dialog = QDialog(self)
         dialog.setWindowTitle("Model Comparison Results")
         layout = QVBoxLayout(dialog)
 
-        # Create table
         table = QTableWidget()
         table.setColumnCount(6)
         table.setHorizontalHeaderLabels(["Model Name", "Accuracy", "Precision", "Recall", "F1 Score", "AUC"])
 
-        # Adjust column width
         header = table.horizontalHeader()
         header.setSectionResizeMode(0, QHeaderView.Stretch)
         header.setSectionResizeMode(1, QHeaderView.ResizeToContents)
@@ -363,7 +351,6 @@ class DiabetesRiskApp(QMainWindow):
         header.setSectionResizeMode(4, QHeaderView.ResizeToContents)
         header.setSectionResizeMode(5, QHeaderView.ResizeToContents)
 
-        # Fill table data
         table.setRowCount(len(model_results))
         for row, (name, accuracy, precision, recall, f1, roc_auc) in enumerate(model_results):
             table.setItem(row, 0, QTableWidgetItem(name))