删除没用代码
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@ -4,5 +4,4 @@ def save_to_xlsx(project_name, file_name, data):
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os.makedirs(f'Result/{project_name}', exist_ok=True)
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data.to_excel(f'Result/{project_name}/{file_name}.xlsx', index=True)
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print("Save successed to " + f'Result/{project_name}/{file_name}.xlsx')
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return
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@ -0,0 +1,32 @@
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import torch
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import torch.nn as nn
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import torch.optim as optim
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class Simple1DCNN(nn.Module):
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def __init__(self, input_size, num_classes):
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super(Simple1DCNN, self).__init__()
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self.conv1 = nn.Conv1d(in_channels=1, out_channels=32, kernel_size=3, stride=1, padding=1)
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self.relu = nn.ReLU()
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self.pool = nn.MaxPool1d(kernel_size=2, stride=2)
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self.conv2 = nn.Conv1d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
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self.fc1 = nn.Linear(64 * (input_size // 4), 128) # 假设经过两次池化后，长度减半两次
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self.fc2 = nn.Linear(128, num_classes)
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def forward(self, x):
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x = self.pool(self.relu(self.conv1(x)))
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x = self.pool(self.relu(self.conv2(x)))
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x = x.view(-1, 64 * (self.input_size // 4)) # 展平特征图
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x = self.relu(self.fc1(x))
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x = self.fc2(x)
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return x
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# 实例化模型
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input_size = 100 # 假设n=100
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num_classes = 10 # 假设有10个类别
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model = Simple1DCNN(input_size, num_classes)
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# 定义损失函数和优化器
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.Adam(model.parameters(), lr=0.001)
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# 训练和评估模型的代码与之前类似，这里不再赘述。
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@ -1,23 +1,12 @@
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import torch
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import torch.nn as nn
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from torch.utils.data import DataLoader, TensorDataset
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from Qtorch.Models.Qnn import Qnn
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from sklearn.preprocessing import LabelEncoder
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from sklearn.metrics import confusion_matrix
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import pandas as pd
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LABEL_ENCODER = LabelEncoder()
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DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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class Qmlp(nn.Module):
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epoch_data = {
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'epoch': [],
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'train_loss': [],
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'train_accuracy': [],
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'test_accuracy': []
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}
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labels = None
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class Qmlp(Qnn):
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def __init__(self, X_train, y_train, X_test, y_test,
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hidden_layers,
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@ -26,15 +15,14 @@ class Qmlp(nn.Module):
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):
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super(Qmlp, self).__init__()
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self.LABEL_ENCODER = LabelEncoder()
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self.X_train, self.y_train, self.X_test, self.y_test = X_train, y_train, X_test, y_test
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self.labels = labels
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input_size = X_train.shape[1]
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# input_size = 5
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print(input_size)
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num_classes = len(set(y_train))
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self.layers = nn.ModuleList()
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# Input layer to first hidden layer
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@ -59,112 +47,6 @@ class Qmlp(nn.Module):
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x = layer(x)
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return x
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def __prepare_data(self):
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# Step 2: Prepare the data
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X_train_tensor = torch.tensor(self.X_train, dtype=torch.float32)
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self.y_train = LABEL_ENCODER.fit_transform(self.y_train)
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y_train_tensor = torch.tensor(self.y_train, dtype=torch.long)
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X_test_tensor = torch.tensor(self.X_test, dtype=torch.float32)
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self.y_test = LABEL_ENCODER.transform(self.y_test)
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y_test_tensor = torch.tensor(self.y_test, dtype=torch.long)
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train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
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test_dataset = TensorDataset(X_test_tensor, y_test_tensor)
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train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
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test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
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return train_loader, test_loader
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def __train_model(self, train_loader, test_loader, epochs_times=100):
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model = self.to(DEVICE)
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=0.0001, weight_decay=1e-5)
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scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=10)
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best_test_accuracy = 0
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patience = 100
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counter = 0
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accuracy_threshold = 0.99 # 99% 的准确率阈值
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for epoch in range(epochs_times):
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model.train()
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running_loss = 0.0
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correct_train = 0
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total_train = 0
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for inputs, labels in train_loader:
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inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
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optimizer.zero_grad()
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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running_loss += loss.item()
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_, predicted = torch.max(outputs.data, 1)
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total_train += labels.size(0)
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correct_train += (predicted == labels).sum().item()
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train_accuracy = correct_train / total_train
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train_loss = running_loss / len(train_loader)
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model.eval()
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correct_test = 0
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total_test = 0
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all_labels = []
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all_predicted = []
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all_prob = []
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with torch.no_grad():
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for inputs, labels in test_loader:
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inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
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outputs = model(inputs)
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prob = torch.nn.functional.softmax(outputs, dim=1)
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_, predicted = torch.max(outputs.data, 1)
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total_test += labels.size(0)
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correct_test += (predicted == labels).sum().item()
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all_labels.extend(labels.cpu().numpy())
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all_predicted.extend(predicted.cpu().numpy())
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all_prob.extend(prob.cpu().numpy())
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test_accuracy = correct_test / total_test
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print(f'Epoch [{epoch+1}/{epochs_times}], Loss: {train_loss:.4f}, Train Accuracy: {train_accuracy * 100:.2f}%, Test Accuracy: {test_accuracy*100:.2f}%')
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self.epoch_data['epoch'].append(epoch+1)
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self.epoch_data['train_loss'].append(train_loss)
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self.epoch_data['train_accuracy'].append(train_accuracy)
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self.epoch_data['test_accuracy'].append(test_accuracy)
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scheduler.step(train_loss)
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if test_accuracy > best_test_accuracy:
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best_test_accuracy = test_accuracy
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counter = 0
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else:
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counter += 1
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if counter >= patience and best_test_accuracy >= accuracy_threshold:
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print(f"Early stopping at epoch {epoch+1}")
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break
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self.cm = confusion_matrix(all_labels, all_predicted, normalize='true')
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print(self.cm)
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return
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def get_cm(self):
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return pd.DataFrame(self.cm, columns=self.labels, index=self.labels)
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def get_epoch_data(self):
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return pd.DataFrame(self.epoch_data)
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def fit(self, epoch_times = 100):
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train_loader, test_loader = self.__prepare_data()
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self.__train_model(train_loader, test_loader, epochs_times=epoch_times)
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return
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def __init_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Linear):
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@ -1,47 +1,158 @@
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import torch
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import torch.nn as nn
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import pandas as pd
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from abc import ABC, abstractmethod
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from sklearn.metrics import confusion_matrix as cm
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from sklearn.decomposition import PCA
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from sklearn.metrics import confusion_matrix
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from torch.utils.data import DataLoader, TensorDataset
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from Qfunctions.divSet import divSet as ds
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from Qfunctions.saveToxlsx import save_to_xlsx as stx
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# from Qfunctions.divSet import divSet as ds
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# from Qfunctions.saveToxlsx import save_to_xlsx as stx
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class Qnn(nn.Module, ABC):
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class Qnn(nn.Module):
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def __init__(self):
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def __init__(self, labels=None):
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super(Qnn, self).__init__()
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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self.DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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# 保存原始label， 混淆矩阵使用
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self.original_labels = labels
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self.X_train, self.y_train, self.X_test, self.y_test = None, None, None, None
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# 定义结果
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self.result = {
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'acc_and_loss' : {
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'epoch' : [],
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'loss': [],
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'train_accuracy': [],
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'test_accuracy': [],
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},
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'confusion_matrix': None,
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self.labels = None
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self.LABEL_ENCODER = None
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self.epoch_data = {
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'epoch': [],
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'train_loss': [],
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'train_accuracy': [],
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'test_accuracy': []
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}
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def accuracy(self, output, target):
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pass
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self.pca_2d, self.pca_3d = None, None
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# 定义损失函数
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def hinge_loss(self, output, target):
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pass
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def confusion_matrix(self, test_outputs):
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predicted = torch.argmax(test_outputs, dim=1)
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true_label = torch.argmax(self.y_test, dim=1)
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return cm(predicted.cpu(), true_label.cpu())
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def __prepare_data(self):
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# 将data转换为tensor形式
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X_train_tensor = torch.tensor(self.X_train, dtype=torch.float32)
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self.y_train = self.LABEL_ENCODER.fit_transform(self.y_train)
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y_train_tensor = torch.tensor(self.y_train, dtype=torch.long)
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X_test_tensor = torch.tensor(self.X_test, dtype=torch.float32)
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self.y_test = self.LABEL_ENCODER.transform(self.y_test)
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y_test_tensor = torch.tensor(self.y_test, dtype=torch.long)
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train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
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test_dataset = TensorDataset(X_test_tensor, y_test_tensor)
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train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
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test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
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return train_loader, test_loader
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def __train_model(self, train_loader, test_loader, epochs_times=100):
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model = self.to(self.DEVICE)
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=0.0001, weight_decay=1e-5)
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scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=10)
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best_test_accuracy = 0
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patience = 100
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counter = 0
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accuracy_threshold = 0.99 # 99% 的准确率阈值
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for epoch in range(epochs_times):
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model.train()
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running_loss = 0.0
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correct_train = 0
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total_train = 0
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for inputs, labels in train_loader:
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inputs, labels = inputs.to(self.DEVICE), labels.to(self.DEVICE)
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optimizer.zero_grad()
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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running_loss += loss.item()
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_, predicted = torch.max(outputs.data, 1)
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total_train += labels.size(0)
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correct_train += (predicted == labels).sum().item()
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train_accuracy = correct_train / total_train
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train_loss = running_loss / len(train_loader)
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model.eval()
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correct_test = 0
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total_test = 0
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all_labels = []
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all_predicted = []
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all_prob = []
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with torch.no_grad():
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for inputs, labels in test_loader:
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inputs, labels = inputs.to(self.DEVICE), labels.to(self.DEVICE)
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outputs = model(inputs)
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prob = torch.nn.functional.softmax(outputs, dim=1)
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_, predicted = torch.max(outputs.data, 1)
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total_test += labels.size(0)
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correct_test += (predicted == labels).sum().item()
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all_labels.extend(labels.cpu().numpy())
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all_predicted.extend(predicted.cpu().numpy())
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all_prob.extend(prob.cpu().numpy())
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test_accuracy = correct_test / total_test
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print(f'Epoch [{epoch+1}/{epochs_times}], Loss: {train_loss:.4f}, Train Accuracy: {train_accuracy * 100:.2f}%, Test Accuracy: {test_accuracy*100:.2f}%')
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self.epoch_data['epoch'].append(epoch+1)
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self.epoch_data['train_loss'].append(train_loss)
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self.epoch_data['train_accuracy'].append(train_accuracy)
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self.epoch_data['test_accuracy'].append(test_accuracy)
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scheduler.step(train_loss)
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if test_accuracy > best_test_accuracy:
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best_test_accuracy = test_accuracy
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counter = 0
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else:
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counter += 1
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if counter >= patience and best_test_accuracy >= accuracy_threshold:
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print(f"Early stopping at epoch {epoch+1}")
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break
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self.cm = confusion_matrix(all_labels, all_predicted, normalize='true')
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print(self.cm)
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return
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def fit(self, epoch_times = 100):
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train_loader, test_loader = self.__prepare_data()
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self.__train_model(train_loader, test_loader, epochs_times=epoch_times)
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return
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def get_PCA(self):
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# PCA 2D 图像
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pca_2d = PCA(n_components=2) # 保留两个主成分
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principalComponents = pca_2d.fit_transform(self.X_train)
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df_pca2d =pd.DataFrame(data=principalComponents, columns=['PC1', 'PC2'])
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df_pca2d['labels'] = self.y_train
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# PCA 3D 图像
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pca_3d = PCA(n_components=3)
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principalComponents = pca_3d.fit_transform(self.X_train)
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df_pca3d = pd.DataFrame(data=principalComponents, columns=['PC1', 'PC2', 'PC3'])
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df_pca3d['labels'] = self.y_train
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return df_pca2d, df_pca3d
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def get_cm(self):
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return pd.DataFrame(self.cm, columns=self.labels, index=self.labels)
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def get_epoch_data(self):
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return pd.DataFrame(self.epoch_data)
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def fit(self, epochs = 100):
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self.train_model(epochs)
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38
main.py
38
main.py
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@ -1,50 +1,34 @@
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# frofrom Qtorch.Functions import dLoader
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from Qtorch.Models.Qmlp import Qmlp
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from Qfunctions.divSet import divSet
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from Qfunctions.loaData import load_data as dLoader
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from sklearn.decomposition import PCA
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from Qfunctions.loaData import load_data
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from Qfunctions.saveToxlsx import save_to_xlsx as save_to_xlsx
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def main():
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projet_name = '20241112Numbers' # 输入元数据文件夹名称
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label_names =['1', '2', '3', '4', '5', '6', '7' ,'8', '9'] # 请在[]内输入每一个分类的名称
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data = dLoader(projet_name, label_names, isDir=False, fileClass='xls')
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data = load_data(projet_name, label_names, isDir=False, fileClass='xls')
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X_train, X_test, y_train, y_test, encoder = divSet(
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data=data, labels=label_names, test_size= 0.3
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)
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import pandas as pd
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pca = PCA(n_components=2) # 保留两个主成分
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principalComponents = pca.fit_transform(X_train)
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df_pca2d = pd.DataFrame(data=principalComponents, columns=['PC1', 'PC2'])
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df_pca2d['labels'] = y_train
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pca = PCA(n_components=3) # 保留三个主成分
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principalComponents = pca.fit_transform(X_train)
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df_pca3d = pd.DataFrame(data=principalComponents, columns=['PC1', 'PC2', 'PC3'])
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df_pca3d['labels'] = y_train
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# 保存为xlsx文件
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import os
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folder = os.path.join("./Result", projet_name)
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if not os.path.exists(folder):
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os.makedirs(folder)
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df_pca2d.to_excel(os.path.join(folder, 'pca_2d_points_with_labels.xlsx'), index=False)
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df_pca3d.to_excel(os.path.join(folder, 'pca_3d_points_with_labels.xlsx'), index=False)
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model = Qmlp(
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X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test,
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hidden_layers=[128, 128],
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dropout_rate=0
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)
|
||||
|
||||
pca_2d, pca_3d = model.get_PCA()
|
||||
|
||||
|
||||
model.fit(300)
|
||||
|
||||
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)
|
||||
save_to_xlsx(project_name=projet_name, file_name="pca_2d", data=pca_2d)
|
||||
save_to_xlsx(project_name=projet_name, file_name="pca_3d", data=pca_3d)
|
||||
save_to_xlsx(project_name=projet_name, file_name="cm", data=cm )
|
||||
save_to_xlsx(project_name=projet_name, file_name="acc_and_loss", data=epoch_data)
|
||||
|
||||
print("Done")
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue