remove unuse conde

Qtorch/Models/Qmlp.py

@@ -60,7 +60,6 @@ class Qmlp(nn.Module):
     return x
   
   def __prepare_data(self):
-
     # Step 2: Prepare the data
     X_train_tensor = torch.tensor(self.X_train, dtype=torch.float32)
     self.y_train = LABEL_ENCODER.fit_transform(self.y_train)
@@ -151,14 +150,7 @@ class Qmlp(nn.Module):
           print(f"Early stopping at epoch {epoch+1}")
           break
 
-    if self.labels:
-      # labels_encoded = LABEL_ENCODER.fit(self.labels)
     self.cm = confusion_matrix(all_labels, all_predicted, normalize='true')
-    else:
-      self.cm = confusion_matrix(all_labels, all_predicted, normalize='true')
-
-    
-    # self.cm = confusion_matrix(all_labels, all_predicted, normalize='true')
     print(self.cm)
     return
 
@@ -169,10 +161,10 @@ class Qmlp(nn.Module):
     return pd.DataFrame(self.epoch_data)
 
   def fit(self, epoch_times = 100):
-
     train_loader, test_loader = self.__prepare_data()
     self.__train_model(train_loader, test_loader, epochs_times=epoch_times)
     return 
+
   def __init_weights(self):
     for m in self.modules():
         if isinstance(m, nn.Linear):

Qtorch/Models/Qnn.py

@@ -12,29 +12,14 @@ from Qfunctions.saveToxlsx import save_to_xlsx as stx
 
 class Qnn(nn.Module, ABC):
   
-  def __init__(self, data, labels=None, test_size=0.2, random_state=None):
+  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labe， 混淆矩阵使用
+    # 保存原始label， 混淆矩阵使用
     self.original_labels = labels
     
-    # 划分训练集和测试集
-    X_train, X_test, y_train, y_test, self.labels = ds(
-      data=data, 
-      labels=labels,
-      test_size=test_size,
-      random_state=random_state
-    )
-
-    self.train_loader, self.test_loader = self.__prepare_data(
-      X_train=X_train,
-      y_train=y_train,
-      X_test=X_test,
-      y_test=y_test
-    )
-    
     # 定义结果
     self.result = {
       'acc_and_loss' : {
@@ -53,10 +38,6 @@ class Qnn(nn.Module, ABC):
   def hinge_loss(self, output, target):
     pass
     
-  @abstractmethod
-  def train_model(self, train_loader, test_loader, epochs):
-    pass
-    
   def confusion_matrix(self, test_outputs):
     predicted = torch.argmax(test_outputs, dim=1)
     true_label = torch.argmax(self.y_test, dim=1)
@@ -64,42 +45,3 @@ class Qnn(nn.Module, ABC):
 
   def fit(self, epochs = 100):
     self.train_model(epochs)
-
-  def save(self, project_name):
-    for filename, data in self.result.items():
-      if filename == 'confusion_matrix':
-        data = pd.DataFrame(data, columns=self.original_labels, index=self.original_labels)
-        stx(project_name, filename, data)
-      else:
-        data = pd.DataFrame(data)
-        stx(project_name, filename, data)
-
-  def __prepare_data(self, X_train, y_train, X_test, y_test):
-
-    X_train_tensor = torch.tensor(X_train, dtype=torch.float32)
-    y_train_tensor = torch.tensor(y_train, dtype=torch.long)
-    
-    X_test_tensor  = torch.tensor(X_test, dtype=torch.float32)
-    y_test_tensor  = torch.tensor(y_test, dtype=torch.long)
-    
-    train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
-    test_dataset  = TensorDataset(X_test_tensor, y_test_tensor)
-    
-    train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
-    test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
-
-    print(train_loader, test_loader)
-    
-    return train_loader, test_loader
-
-    
-
-
-        
-
-
-
-
-
-
-

main.py

@@ -5,9 +5,9 @@ from Qfunctions.loaData import load_data as dLoader
 from sklearn.decomposition import PCA
 
 def main():
-  projet_name = '###########'                 # 输入元数据文件夹名称
+  projet_name = '20241112Numbers'                            # 输入元数据文件夹名称
-  label_names =[]                             # 请在[]内输入每一个分类的名称
+  label_names =['1', '2', '3', '4', '5', '6', '7' ,'8', '9'] # 请在[]内输入每一个分类的名称
-  data = dLoader(projet_name, label_names, isDir=False)
+  data = dLoader(projet_name, label_names, isDir=False, fileClass='xls')
   X_train, X_test, y_train, y_test, encoder = divSet(
     data=data, labels=label_names, test_size= 0.3
   )