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