# frofrom Qtorch.Functions import dLoader
from Qtorch.Models.Qmlp import Qmlp
from Qfunctions.divSet import divSet
from Qfunctions.loaData import load_data as dLoader
from sklearn.decomposition import PCA

import torch

def main():
  projet_name = '20241005Sound'
  label_names =["Accuracy", "Compress", "Distance", "Loss", "Metal", "Python"]
  # data = dLoader(projet_name, label_names, isDir=False)
  X_train, X_test, y_train, y_test, encoder = divSet(
    data=data, labels=label_names, test_size= 0.2
  )
  
  print(y_train)
  
  import pandas as pd
  pca = PCA(n_components=2)  # 保留两个主成分
  principalComponents = pca.fit_transform(X_train)
  df_pca2d = pd.DataFrame(data=principalComponents, columns=['PC1', 'PC2'])
  df_pca2d['labels'] = y_train

  pca = PCA(n_components=3)  # 保留三个主成分
  principalComponents = pca.fit_transform(X_train)
  df_pca3d = pd.DataFrame(data=principalComponents, columns=['PC1', 'PC2', 'PC3'])
  df_pca3d['labels'] = y_train

  # 保存为CSV文件
  import os
  folder = os.path.join("./Result", projet_name)
  df_pca2d.to_excel(os.path.join(folder, 'pca_2d_points_with_labels.xlsx'), index=False)
  df_pca3d.to_excel(os.path.join(folder, 'pca_3d_points_with_labels.xlsx'), index=False)


  
  model = Qmlp(
    X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test,
    hidden_layers=[64, 64],
    dropout_rate=0
    )
  model.fit(100)
  
  cm = model.get_cm()
  epoch_data = model.get_epoch_data()
  
  from Qfunctions.saveToxlsx import save_to_xlsx as stx
  stx(project_name=projet_name, file_name="cm", data=cm)
  stx(project_name=projet_name, file_name="acc_and_loss", data=epoch_data)
  print("Done")
  
if __name__ == '__main__':
  main()




# from sklearn.model_selection import train_test_split
# from sklearn.preprocessing import StandardScaler
# from sklearn.svm import SVC
# from sklearn.model_selection import GridSearchCV
# from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
# import pandas as pd

# if __name__ == '__main__':

#     project_name = '20240829Letters'
#     labels = None

#     data = ld(project_name, labels)
    

    
    
    # svm = SVM(
    #     data=data,
    #     labels=labels
    # )
    
    # svm.fit()

    # X, y = data.iloc[:, :-1], data.iloc[:, -1]
    
    # # 分割数据
    # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=None)

    # # 标准化数据
    # scaler = StandardScaler()
    # X_train_scaled = scaler.fit_transform(X_train)
    # X_test_scaled = scaler.transform(X_test)
    
    # # 创建 SVM 分类器
    # svm = SVC(kernel='rbf', random_state=42)

    # # 定义参数网格
    # param_grid = {
    #     'C': [0.1, 1, 10, 100],
    #     'gamma': ['scale', 'auto', 0.1, 1, 10]
    # }

    # # 使用网格搜索进行超参数调优
    # grid_search = GridSearchCV(svm, param_grid, cv=5, n_jobs=-1, verbose=2)
    # grid_search.fit(X_train_scaled, y_train)

    # # 打印最佳参数
    # print("Best parameters:", grid_search.best_params_)

    # # 使用最佳参数的模型
    # best_svm = grid_search.best_estimator_

    # # 计算训练集和测试集准确率
    # y_train_pred = best_svm.predict(X_train_scaled)
    # train_acc = accuracy_score(y_train, y_train_pred)

    # y_test_pred = best_svm.predict(X_test_scaled)
    # test_acc = accuracy_score(y_test, y_test_pred)

    # # 在测试集上进行预测
    # y_pred = best_svm.predict(X_test_scaled)

    # # 计算准确率
    # accuracy = accuracy_score(y_test, y_pred)
    # print(f"Accuracy: {accuracy}")

    # # 打印详细的分类报告
    # print(classification_report(y_test, y_pred))
    # # 计算并可视化混淆矩阵
    # cm = confusion_matrix(y_test, y_test_pred, normalize='true')
    
    # print(cm)
    # # model = QSVM(
    # #     data=data,
    # #     labels=labels
    # # )
    
    # # model.fit(300)
    # # model.save(project_name)
    
    
    # # 创建一个 Excel 写入器
    # # 将分类报告转换为DataFrame
    # # 获取分类报告
    
    # report = classification_report(y_test, y_test_pred, output_dict=True)

    # df_report = pd.DataFrame(report).transpose()
    # with pd.ExcelWriter(f'./Result/{project_name}/svm_results.xlsx') as writer:
    #     from sklearn.decomposition import PCA
    #     pca = PCA()
    #     X_pca = pca.fit_transform(X)
    #     # 创建 2D PCA 坐标的 DataFrame
    #     df_pca_2d = pd.DataFrame(data = X_pca[:, :2], columns = ['First Principal Component', 'Second Principal Component'])
    #     df_pca_2d['Label'] = y
    #     # 创建 3D PCA 坐标的 DataFrame
    #     df_pca_3d = pd.DataFrame(data = X_pca[:, :3], columns = ['First Principal Component', 'Second Principal Component', 'Third Principal Component'])
    #     df_pca_3d['Label'] = y

    #     # 将 2D PCA 坐标写入 Excel
    #     df_pca_2d.to_excel(writer, sheet_name='PCA 2D Coordinates', index=False)    
    #     df_pca_3d.to_excel(writer, sheet_name='PCA 3D Coordinates', index=False)    

        
    #     # 将分类报告写入Excel
    #     df_report.to_excel(writer, sheet_name='Classification Report')
        
    #     # 将最佳参数写入Excel
    #     pd.DataFrame([grid_search.best_params_]).to_excel(writer, sheet_name='Best Parameters')
        
    #     # 如果你想保存混淆矩阵
    #     from sklearn.metrics import confusion_matrix
    #     # 创建混淆矩阵并添加标签
    #     cm = confusion_matrix(y_test, y_test_pred, normalize='true')
    #     df_cm = pd.DataFrame(cm, index=labels, columns=labels)
    #     df_cm.index.name = 'True'
    #     df_cm.columns.name = 'Predicted'
        
    #     # 将混淆矩阵写入Excel
    #     df_cm.to_excel(writer, sheet_name='Confusion Matrix')
        
    #     # 如果你想保存训练集和测试集的准确率
    #     train_accuracy = best_svm.score(X_train_scaled, y_train)
    #     test_accuracy = best_svm.score(X_test_scaled, y_test)
    #     pd.DataFrame({
    #         'Train Accuracy': [train_accuracy],
    #         'Test Accuracy': [test_accuracy]
    #     }).to_excel(writer, sheet_name='Accuracy')

    #     print("Results have been saved to 'svm_results.xlsx'")