49 lines
2.5 KiB
Python
49 lines
2.5 KiB
Python
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from classification_model.Classification.CNN_HYper import optimize_CNN
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from classification_model.Classification.CNN_Transfomer import TransformerTrainAndTest
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from classification_model.Classification.CNN_SAE import SAETrainAndTest
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from classification_model.Classification.SAE import SAE
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from classification_model.Classification.CNN_deepseek import CNN_deepseek
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from multiprocessing import Pool, cpu_count
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# 贝叶斯优化模型调用
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from classification_model.Classification.ClassicClsHY import optimize_SVM, optimize_KNN, optimize_XGBoost, optimize_RF, optimize_CatBoost, optimize_LogisticRegression, optimize_ANN
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def QualitativeAnalysis(model, X_train, X_test, y_train, y_test, n_jobs=-1):
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"""
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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。
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参数:
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- model: 要使用的分类模型名称
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- X_train, X_test: 训练集和测试集的特征数据
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- y_train, y_test: 训练集和测试集的标签数据
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- n_jobs: 使用的核心数量，适用于支持多线程的模型
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返回:
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- train_metrics: 包含训练集 accuracy, precision, recall, f1_score 的字典
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- test_metrics: 包含测试集 accuracy, precision, recall, f1_score 的字典
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"""
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if model == "ANN":
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best_params, train_metrics, test_metrics = optimize_ANN(X_train, y_train, X_test, y_test)
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elif model == "SVM":
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best_params, train_metrics, test_metrics = optimize_SVM(X_train, y_train, X_test, y_test)
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elif model == "RF":
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best_params, train_metrics, test_metrics = optimize_RF(X_train, y_train, X_test, y_test)
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elif model == "optimize_CNN":
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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')
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elif model == "LogisticRegression":
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best_params, train_metrics, test_metrics = optimize_LogisticRegression(X_train, y_train, X_test, y_test)
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elif model == "XGBoost":
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best_params, train_metrics, test_metrics = optimize_XGBoost(X_train, y_train, X_test, y_test)
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elif model == "CatBoost":
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best_params, train_metrics, test_metrics = optimize_CatBoost(X_train, y_train, X_test, y_test)
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elif model == 'KNN':
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best_params, train_metrics, test_metrics = optimize_KNN(X_train, y_train, X_test, y_test)
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else:
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print("No such model for Qualitative Analysis")
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return None, None
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return best_params,train_metrics, test_metrics
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