初始提交

classification_model/Classification/Cls_超参数.py

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+
+from classification_model.Classification.CNN_HYper import optimize_CNN
+from classification_model.Classification.CNN_Transfomer import TransformerTrainAndTest
+from classification_model.Classification.CNN_SAE import SAETrainAndTest
+from classification_model.Classification.SAE import SAE
+from classification_model.Classification.CNN_deepseek import CNN_deepseek
+from multiprocessing import Pool, cpu_count
+
+# 贝叶斯优化模型调用
+from classification_model.Classification.ClassicClsHY import optimize_SVM, optimize_KNN, optimize_XGBoost, optimize_RF, optimize_CatBoost, optimize_LogisticRegression, optimize_ANN
+
+def QualitativeAnalysis(model, X_train, X_test, y_train, y_test, n_jobs=-1):
+    """
+    根据模型名称调用不同的分类模型，并返回训练集和测试集的评估指标。
+
+    参数:
+    - model: 要使用的分类模型名称
+    - X_train, X_test: 训练集和测试集的特征数据
+    - y_train, y_test: 训练集和测试集的标签数据
+    - n_jobs: 使用的核心数量，适用于支持多线程的模型
+
+    返回:
+    - train_metrics: 包含训练集 accuracy, precision, recall, f1_score 的字典
+    - test_metrics: 包含测试集 accuracy, precision, recall, f1_score 的字典
+    """
+
+
+    if model == "ANN":
+        best_params, train_metrics, test_metrics = optimize_ANN(X_train, y_train, X_test, y_test)
+    elif model == "SVM":
+        best_params, train_metrics, test_metrics = optimize_SVM(X_train, y_train, X_test, y_test)
+    elif model == "RF":
+        best_params, train_metrics, test_metrics = optimize_RF(X_train, y_train, X_test, y_test)
+    elif model == "optimize_CNN":
+        best_params,train_metrics, test_metrics = optimize_CNN(X_train, X_test, y_train, y_test,  model_path=r'H:\arithmetic\python\opensa-main(local)\opensa-main\OpenSA\tensorboard_logs\model_best.pth')
+    elif model == "LogisticRegression":
+        best_params, train_metrics, test_metrics = optimize_LogisticRegression(X_train, y_train, X_test, y_test)
+    elif model == "XGBoost":
+        best_params, train_metrics, test_metrics = optimize_XGBoost(X_train, y_train, X_test, y_test)
+    elif model == "CatBoost":
+        best_params, train_metrics, test_metrics = optimize_CatBoost(X_train, y_train, X_test, y_test)
+    elif model == 'KNN':
+        best_params, train_metrics, test_metrics = optimize_KNN(X_train, y_train, X_test, y_test)
+    else:
+        print("No such model for Qualitative Analysis")
+        return None, None
+
+    return  best_params,train_metrics, test_metrics