import torch
import torch.nn as nn
import pandas as pd
from abc import ABC, abstractmethod

from sklearn.metrics import confusion_matrix as cm

from torch.utils.data import DataLoader, TensorDataset
from Qfunctions.divSet import divSet as ds
from Qfunctions.saveToxlsx import save_to_xlsx as stx


class Qnn(nn.Module, ABC):
  
  def __init__(self, labels=None):
    super(Qnn, self).__init__()

    self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    # 保存原始label， 混淆矩阵使用
    self.original_labels = labels
    
    # 定义结果
    self.result = {
      'acc_and_loss' : {
        'epoch' : [],
        'loss': [],
        'train_accuracy': [],
        'test_accuracy': [],
      },
      'confusion_matrix': None,
    }
    
  def accuracy(self, output, target):
    pass

  # 定义损失函数
  def hinge_loss(self, output, target):
    pass
    
  def confusion_matrix(self, test_outputs):
    predicted = torch.argmax(test_outputs, dim=1)
    true_label = torch.argmax(self.y_test, dim=1)
    return cm(predicted.cpu(), true_label.cpu())

  def fit(self, epochs = 100):
    self.train_model(epochs)