From 884db9b9265fac690cbedc95b8a3c67d068b1d42 Mon Sep 17 00:00:00 2001 From: newbie Date: Mon, 7 Oct 2024 09:54:32 +0800 Subject: [PATCH] init --- .gitignore | 2 + .vscode/launch.json | 15 ++ Qfunctions/__init__.py | 0 Qfunctions/__pycache__/Qtorch.cpython-312.pyc | Bin 0 -> 6700 bytes .../__pycache__/__init__.cpython-312.pyc | Bin 0 -> 144 bytes Qfunctions/__pycache__/divSet.cpython-312.pyc | Bin 0 -> 1317 bytes .../__pycache__/loaData.cpython-312.pyc | Bin 0 -> 3736 bytes .../__pycache__/saveToxlsx.cpython-312.pyc | Bin 0 -> 620 bytes Qfunctions/__pycache__/test.cpython-312.pyc | Bin 0 -> 1079 bytes Qfunctions/divSet.py | 28 +++ Qfunctions/loaData.py | 97 +++++++++ Qfunctions/saveToxlsx.py | 13 ++ Qfunctions/test.py | 17 ++ Qtorch/Models/QSVM.py | 14 ++ Qtorch/Models/QSVM_BRF.py | 74 +++++++ Qtorch/Models/Qmlp.py | 180 +++++++++++++++++ Qtorch/Models/Qnn.py | 105 ++++++++++ Qtorch/Models/__init__.py | 0 .../Models/__pycache__/QSVM.cpython-312.pyc | Bin 0 -> 1089 bytes 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encoder.fit(y) + + # 分割数据集为训练集和测试集 + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state) + # 标准化特征 + scaler = StandardScaler() + X_train = scaler.fit_transform(X_train) + X_test = scaler.transform(X_test) + + # 编码标签 + y_train = encoder.transform(y_train.values.reshape(-1, 1)) + y_test = encoder.transform(y_test.values.reshape(-1, 1)) + + return X_train, X_test, y_train, y_test, encoder diff --git a/Qfunctions/loaData.py b/Qfunctions/loaData.py new file mode 100644 index 0000000..5d7272e --- /dev/null +++ b/Qfunctions/loaData.py @@ -0,0 +1,97 @@ +import os +import pandas as pd + +STATIC_PATH = './Static' + +# 从文件夹中读取所有xlsx文件，每个文件对应一个label +# labelNames为label的名字，如果不提供则默认为文件名 +def load_data(folder, labelNames, isDir): + # 检查folder参数 + if folder is None: + raise ValueError("The 'folder' parameter is required.") + + # 检查labelNames参数 + if labelNames is None: + raise ValueError("The 'labelNames' parameter is required if 'folder' does not contain labels.") + + folder = os.path.join(STATIC_PATH, folder) + + # 看看有没有元数据文件夹 + if not os.path.isdir(folder): + raise ValueError(f"The folder '{folder}' does not exist.") + +# fileNames = [f for f in os.listdir(folder) if f.endswith('.xlsx')] + +# # 获取数据的最大行数 +# max_row_length = get_max_row_len(folder, fileNames) + +# all_features = [] + +# for i, fileName in enumerate(fileNames): + +# features = load_xlsx(folder + '/' + fileName, labelNames[i], max_row_length, 'zero') +# all_features.append(features) + +# data = pd.concat(all_features, ignore_index = True) + + data = None + if not isDir: + data = load_from_file(folder=folder, labelNames=labelNames) + else: + data = load_from_folder(folder=folder, labelNames=labelNames) + print(data) + return data + +def load_from_folder(folder, labelNames): + pass + +def load_from_file(folder, labelNames): + fileNames = [labelName + ".xlsx" for labelName in labelNames] + # 获取数据的最大行数 + max_row_length = get_max_row_len(folder, fileNames) + all_features = [] + for i, fileName in enumerate(fileNames): + features = load_xlsx(folder + '/' + fileName, labelNames[i], max_row_length, 'zero') + all_features.append(features) + return pd.concat(all_features, ignore_index = True) + + +def load_xlsx(fileName, labelName, max_row_length = 1000, fill_rule = None): + df = pd.read_excel(fileName) + + # 提取偶数列 + features = df.iloc[0:, 1::2] + features.dropna(inplace=True) + features.reset_index(drop=True, inplace=True) + + features = features.T + + # 补全每一行到指定长度 + features = features.apply(lambda row: fill_to_len(row, max_row_length, fill_rule), axis=1) + + features['label'] = labelName + features.columns = [f'feature{i+1}' for i in range(max_row_length)] + ['label'] + + return features + +def fill_to_len(row, length = 1000, rule = None): + fill_value = 0 + + if rule == 'min': + fill_value = row.min() + elif rule == 'mean': + fill_value = row.mean() + elif rule == 'zero': + fill_value = 0 + fill_values = pd.Series([fill_value] * (length - len(row))) + + return pd.concat([row, fill_values], ignore_index=True) + +def get_max_row_len(folder, filenames): + max_len = 0 + for filename in filenames: + df = pd.read_excel(os.path.join(folder, filename)) + max_len = max(max_len, df.shape[0]) + return max_len + +__all__ = ['load_data'] diff --git a/Qfunctions/saveToxlsx.py b/Qfunctions/saveToxlsx.py new file mode 100644 index 0000000..9f8e5b4 --- /dev/null +++ b/Qfunctions/saveToxlsx.py @@ -0,0 +1,13 @@ +import os + +def save_to_xlsx(project_name, file_name, data): + os.makedirs(f'Result/{project_name}', exist_ok=True) + data.to_excel(f'Result/{project_name}/{file_name}.xlsx', index=True) + print("Save successed to " + f'Result/{project_name}/{file_name}.xlsx') + + + # for filename,data in save_maps.items(): + # data.to_excel(f'Result/{project_name}/{filename}.xlsx', index=True) + # print("Save successed to " + f'Result/{project_name}/{filename}.xlsx') + # print('Save to xlsx done!') + return diff --git a/Qfunctions/test.py b/Qfunctions/test.py new file mode 100644 index 0000000..d4c26dd --- /dev/null +++ b/Qfunctions/test.py @@ -0,0 +1,17 @@ +import time +from sklearn.neural_network import MLPClassifier +from sklearn.metrics import classification_report, accuracy_score + +def MLP(X_train, X_test, y_train, y_test): + start_time = time.time() + # 训练 MLP 分类器 + mlp = MLPClassifier(hidden_layer_sizes=(100,), max_iter=300, random_state=42) + mlp.fit(X_train, y_train) + y_pred = mlp.predict(X_test) + end_time = time.time() + # 打印训练时间 + print("Training Time:", end_time - start_time, "seconds") + # 评估模型 + print("Accuracy:", accuracy_score(y_test, y_pred)) + print("Classification Report:") + print(classification_report(y_test, y_pred)) \ No newline at end of file diff --git a/Qtorch/Models/QSVM.py b/Qtorch/Models/QSVM.py new file mode 100644 index 0000000..b0c5b35 --- /dev/null +++ b/Qtorch/Models/QSVM.py @@ -0,0 +1,14 @@ +from Qtorch.Models.Qnn import Qnn +from abc import ABC, abstractmethod + +class QSVM(Qnn, ABC): + def __init__(self, data, labels=None, test_size=0.2, random_state=None): + super().__init__(data, labels, test_size, random_state) + self.result.update({ + "pca_2d" : None, + "pca_3d" : None + }) + + @abstractmethod + def train_model(self, train_loader, test_loader, epochs): + return super().train_model(train_loader, test_loader, epochs) diff --git a/Qtorch/Models/QSVM_BRF.py b/Qtorch/Models/QSVM_BRF.py new file mode 100644 index 0000000..9e42baf --- /dev/null +++ b/Qtorch/Models/QSVM_BRF.py @@ -0,0 +1,74 @@ +import torch +import torch.nn as nn +import torch.optim as optim + +from Qtorch.Models.QSVM import QSVM as svm + +class QSVM_BRF(svm): + def __init__(self, data, labels=None, test_size=0.2, random_state=None, + gamma=1.0, C=100, batch_size = 64, learning_rate=0.01): + super().__init__(data, labels, test_size, random_state) + self.gamma, self.C, self.n_features = gamma, C, data.shape[0] - 1 + self.support_vectors = torch.cat([batch[0] for batch in self.train_loader]) + self.alpha = nn.Parameter(torch.zeros(self.support_vectors.shape[0])) + self.b = nn.Parameter(torch.zeros(1)) + self.batch_size = batch_size + self.learning_rate = learning_rate + self.optimizer = optim.SGD(self.parameters(), lr=self.learning_rate) + + def rbf_kernel(self, X, Y): + X_norm = (X**2).sum(1).view(-1, 1) + Y_norm = (Y**2).sum(1).view(1, -1) + dist = X_norm + Y_norm - 2.0 * torch.mm(X, Y.t()) + return torch.exp(-self.gamma * dist) + + def forward(self, X): + K = self.rbf_kernel(X, self.support_vectors) + return torch.mm(K, self.alpha.unsqueeze(1)).squeeze() + self.b + + def hinge_loss(self, outputs, targets): + return torch.mean(torch.clamp(1 - outputs * targets, min=0)) + + def regularization(self): + return 0.5 * (self.alpha ** 2).sum() + + def train_model(self, epoch_times=100, learning_rate=0.01): + + losses, train_accs, test_accs = [], [], [] + + for epoch in range(epoch_times): + self.train() + epoch_loss, correct_train, total_train = 0, 0, 0 + + for batch_X, batch_y in self.train_loader: + + self.optimizer.zero_grad() + outputs = self(batch_X) + loss = self.hinge_loss(outputs, batch_y) + self.C * self.regularization() + loss.backward() + self.optimizer.step() + + epoch_loss += loss.item() + predicted = torch.sign(outputs) + correct_train += (predicted == batch_y).sum().item() + total_train += batch_y.size(0) + + train_acc = correct_train / total_train + test_acc = self.evaluate() + + losses.append(epoch_loss / len(self.train_loader)) + train_accs.append(train_acc) + test_accs.append(test_acc) + print(f'Epoch [{epoch+1}/{epoch_times}], Loss: {losses[-1]:.4f}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}') + + def evaluate(self): + self.eval() + correct = 0 + total = 0 + with torch.no_grad(): + for batch_X, batch_y in self.test_loader: + outputs = self(batch_X) + predicted = torch.sign(outputs) + correct += (predicted == batch_y).sum().item() + total += batch_y.size(0) + return correct / total diff --git a/Qtorch/Models/Qmlp.py b/Qtorch/Models/Qmlp.py new file mode 100644 index 0000000..841bb90 --- /dev/null +++ b/Qtorch/Models/Qmlp.py @@ -0,0 +1,180 @@ +import torch +import torch.nn as nn +from torch.utils.data import DataLoader, TensorDataset +from sklearn.preprocessing import LabelEncoder +from sklearn.metrics import confusion_matrix +import pandas as pd + +LABEL_ENCODER = LabelEncoder() +DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +class Qmlp(nn.Module): + + epoch_data = { + 'epoch': [], + 'train_loss': [], + 'train_accuracy': [], + 'test_accuracy': [] + } + + labels = None + + def __init__(self, X_train, y_train, X_test, y_test, + hidden_layers, + labels=None, + dropout_rate=0.3 + ): + super(Qmlp, self).__init__() + + self.X_train, self.y_train, self.X_test, self.y_test = X_train, y_train, X_test, y_test + + self.labels = labels + + input_size = X_train.shape[1] + # input_size = 5 + print(input_size) + num_classes = len(set(y_train)) + + self.layers = nn.ModuleList() + + # Input layer to first hidden layer + self.layers.append(nn.Linear(input_size, hidden_layers[0])) + self.layers.append(nn.BatchNorm1d(hidden_layers[0])) + self.layers.append(nn.ReLU()) + self.layers.append(nn.Dropout(dropout_rate)) + + # Create hidden layers + for i in range(1, len(hidden_layers)): + self.layers.append(nn.Linear(hidden_layers[i-1], hidden_layers[i])) + self.layers.append(nn.BatchNorm1d(hidden_layers[i])) + self.layers.append(nn.ReLU()) + self.layers.append(nn.Dropout(dropout_rate)) + + # Output layer + self.layers.append(nn.Linear(hidden_layers[-1], num_classes)) + self.__init_weights() + + def forward(self, x): + for layer in self.layers: + x = layer(x) + 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) + y_train_tensor = torch.tensor(self.y_train, dtype=torch.long) + + X_test_tensor = torch.tensor(self.X_test, dtype=torch.float32) + self.y_test = LABEL_ENCODER.transform(self.y_test) + y_test_tensor = torch.tensor(self.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) + + return train_loader, test_loader + + def __train_model(self, train_loader, test_loader, epochs_times=100): + + model = self.to(DEVICE) + + criterion = nn.CrossEntropyLoss() + optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-5) + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=10) + best_test_accuracy = 0 + patience = 100 + counter = 0 + accuracy_threshold = 0.99 # 99% 的准确率阈值 + + for epoch in range(epochs_times): + + model.train() + running_loss = 0.0 + correct_train = 0 + total_train = 0 + + for inputs, labels in train_loader: + inputs, labels = inputs.to(DEVICE), labels.to(DEVICE) + + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + + running_loss += loss.item() + _, predicted = torch.max(outputs.data, 1) + total_train += labels.size(0) + correct_train += (predicted == labels).sum().item() + train_accuracy = correct_train / total_train + train_loss = running_loss / len(train_loader) + + model.eval() + correct_test = 0 + total_test = 0 + all_labels = [] + all_predicted = [] + all_prob = [] + with torch.no_grad(): + for inputs, labels in test_loader: + inputs, labels = inputs.to(DEVICE), labels.to(DEVICE) + outputs = model(inputs) + prob = torch.nn.functional.softmax(outputs, dim=1) + _, predicted = torch.max(outputs.data, 1) + total_test += labels.size(0) + correct_test += (predicted == labels).sum().item() + all_labels.extend(labels.cpu().numpy()) + all_predicted.extend(predicted.cpu().numpy()) + all_prob.extend(prob.cpu().numpy()) + + test_accuracy = correct_test / total_test + print(f'Epoch [{epoch+1}/{epochs_times}], Loss: {train_loss:.4f}, Train Accuracy: {train_accuracy * 100:.2f}%, Test Accuracy: {test_accuracy*100:.2f}%') + + self.epoch_data['epoch'].append(epoch+1) + self.epoch_data['train_loss'].append(train_loss) + self.epoch_data['train_accuracy'].append(train_accuracy) + self.epoch_data['test_accuracy'].append(test_accuracy) + + scheduler.step(train_loss) + + if test_accuracy > best_test_accuracy: + best_test_accuracy = test_accuracy + counter = 0 + else: + counter += 1 + + if counter >= patience and best_test_accuracy >= accuracy_threshold: + 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 + + def get_cm(self): + return pd.DataFrame(self.cm, columns=self.labels, index=self.labels) + + def get_epoch_data(self): + 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): + nn.init.xavier_uniform_(m.weight) + nn.init.zeros_(m.bias) \ No newline at end of file diff --git a/Qtorch/Models/Qnn.py b/Qtorch/Models/Qnn.py new file mode 100644 index 0000000..b35b439 --- /dev/null +++ b/Qtorch/Models/Qnn.py @@ -0,0 +1,105 @@ +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, data, labels=None, test_size=0.2, random_state=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 + 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' : { + '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 + + @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) + return cm(predicted.cpu(), true_label.cpu()) + + 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 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+output_file="python_files_output.md" + +# 清空或创建输出文件 +> "$output_file" + +# 递归函数来处理目录 +process_directory() { + local dir="$1" + local depth="$2" + + # 添加目录作为标题 + echo "$(printf '%0.s#' $(seq 1 $depth)) ${dir##*/}" >> "$output_file" + echo "" >> "$output_file" + + # 遍历当前目录中的所有.py文件 + for file in "$dir"/*.py; do + # 检查文件是否存在（以防止没有.py文件的情况） + if [ -f "$file" ]; then + # 将文件名作为子标题写入md文件 + echo "$(printf '%0.s#' $(seq 1 $((depth + 1)))) ${file##*/}" >> "$output_file" + echo "" >> "$output_file" + + # 添加代码块开始标记 + echo '```python' >> "$output_file" + + # 将Python文件内容添加到md文件 + cat "$file" >> "$output_file" + + # 添加代码块结束标记 + echo '```' >> "$output_file" + echo "" >> "$output_file" + fi + done + + # 递归处理子目录 + for subdir in "$dir"/*/; do + if [ -d "$subdir" ]; then + process_directory "$subdir" $((depth + 1)) + fi + done +} + +# 从当前目录开始处理 +process_directory "." 1 + +echo "Markdown文件已生成: $output_file" diff --git a/main.py b/main.py new file mode 100644 index 0000000..bcfdbb2 --- /dev/null +++ b/main.py @@ -0,0 +1,189 @@ +# 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= 0.5 + ) + + 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=[32, 32, 32], + # 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'") \ No newline at end of file diff --git a/remake/.vscode/launch.json b/remake/.vscode/launch.json new file mode 100644 index 0000000..14b7790 --- /dev/null +++ b/remake/.vscode/launch.json @@ -0,0 +1,15 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python Debugger: Current File", + "type": "debugpy", + "request": "launch", + "program": "main.py", + "console": "integratedTerminal" + } + ] +} \ No newline at end of file diff --git a/remake/Qtorch/Functions/__init__.py b/remake/Qtorch/Functions/__init__.py new file mode 100644 index 0000000..e2eb142 --- /dev/null +++ b/remake/Qtorch/Functions/__init__.py @@ -0,0 +1,5 @@ +from .dataLoader import dLoader +from .dataSplitter import dsplit +from .resSaver import save_to_xlsx + +__all__ = ['dLoader', 'dsplit', 'save_to_xlsx'] \ No newline at end of file diff --git a/remake/Qtorch/Functions/__pycache__/__init__.cpython-312.pyc b/remake/Qtorch/Functions/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3412e26aec5c2efe49b931ede117299d5170ec21 GIT binary patch literal 331 zcmXw!zfQw25XS8|f2z<-JOGF-3+4eTCd7o0P`50WRhuTlj)U!@X5E;k*N7s-s#+7314ecNV?Ps($TGIQzu4{TBWv3*! zZPPBPm73p4dgF~<)bw)IE_`EJM;U9{#xr)>_4~<88?jRI+!hcPKI0BSz6r*7gN`=n Pc!N$pbNZOs1DO8+7cE!Y literal 0 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