Refactor data saving and visualization functions; add metrics tracking in Qnn model
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@ -62,14 +62,13 @@ def load_xlsx(fileName, labelName, max_row_length = 1000, fill_rule = None):
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# 提取偶数列
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features = df.iloc[0:, 1::2]
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# 复制 features DataFrame
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features_copy = features.copy()
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# features_copy = features.copy()
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# 使用 pd.concat 来追加副本到原始 DataFrame
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features = pd.concat([features, features_copy], ignore_index=True, axis=1)
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# features = pd.concat([features, features_copy], ignore_index=True, axis=1)
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# 计算变化率
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# first_value = features.iloc[0, :] # 获取第一行的数据
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# features_pct_change = (features - first_value) / first_value
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# features = features_pct_change
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features.dropna(inplace=True)
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@ -77,11 +76,15 @@ def load_xlsx(fileName, labelName, max_row_length = 1000, fill_rule = None):
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features = features.T
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# 补全每一行到指定长度
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# 补全每一行到指定长度
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features = features.apply(lambda row: fill_to_len(row, max_row_length, fill_rule), axis=1)
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# 获取实际的列数
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actual_columns = features.shape[1]
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features['label'] = labelName
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features.columns = [f'feature{i+1}' for i in range(max_row_length)] + ['label']
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# 使用实际的列数来创建列名
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features.columns = [f'feature{i+1}' for i in range(actual_columns)] + ['label']
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return features
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@ -1,7 +1,173 @@
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import os
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import pandas as pd
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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import numpy as np
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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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folder_path = f'Result/{project_name}'
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os.makedirs(folder_path, exist_ok=True)
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data.to_excel(f'{folder_path}/{file_name}.xlsx', index=True)
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print("Save successed to " + f'{folder_path}/{file_name}.xlsx')
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save_to_pic(project_name=project_name, file_name=file_name)
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return
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def save_to_pic(project_name, file_name):
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os.makedirs(f'Result/{project_name}', exist_ok=True)
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if file_name == 'pca_2d':
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draw_pca_2d(f'Result/{project_name}/{file_name}.xlsx')
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print("Save successed to " + f'Result/{project_name}/{file_name}.png')
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elif file_name == 'pca_3d':
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draw_pca_3d(f'Result/{project_name}/{file_name}.xlsx')
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print("Save successed to " + f'Result/{project_name}/{file_name}.png')
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elif file_name == 'acc_and_loss':
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draw_epoch_data(f'Result/{project_name}/{file_name}.xlsx')
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draw_last_epoch_bar_chart(f'Result/{project_name}/{file_name}.xlsx')
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print("Save successed to line graph and bar graph")
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elif file_name == 'cm':
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draw_and_save_cm(f'Result/{project_name}/{file_name}.xlsx')
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print("Save successed cm")
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elif file_name == 'cmn':
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draw_and_save_cm(f'Result/{project_name}/{file_name}.xlsx')
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print("Save successed cmn")
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else:
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print("unknow picture type")
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def draw_pca_2d(file_path):
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df = pd.read_excel(file_path)
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plt.figure(figsize=(8, 6))
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plt.scatter(df['PC1'], df['PC2'], c=df['labels'], cmap='viridis', edgecolor='k', alpha=0.6)
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plt.xlabel('PC1')
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plt.ylabel('PC2')
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plt.title('2D PCA')
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plt.colorbar(label='Labels')
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plt.savefig(file_path.replace('.xlsx', '.png'))
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plt.close()
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def draw_pca_3d(file_path):
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df = pd.read_excel(file_path)
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fig = plt.figure(figsize=(8, 6))
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ax = fig.add_subplot(111, projection='3d')
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scatter = ax.scatter(df['PC1'], df['PC2'], df['PC3'], c=df['labels'], cmap='viridis', edgecolor='k', alpha=0.6)
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ax.set_xlabel('PC1')
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ax.set_ylabel('PC2')
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ax.set_zlabel('PC3')
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ax.set_title('3D PCA')
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fig.colorbar(scatter, ax=ax, label='Labels')
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plt.savefig(file_path.replace('.xlsx', '.png'))
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def draw_epoch_data(file_path):
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df = pd.read_excel(file_path)
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epochs = df['epoch']
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train_loss = df['train_loss']
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train_accuracy = df['train_accuracy'] * 100
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test_accuracy = df['test_accuracy'] * 100
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f1_score = df['f1_score']
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precision = df['precision']
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recall = df['recall']
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fig, axs = plt.subplots(2, 3, figsize=(18, 12))
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# 折线图：训练损失
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axs[0, 0].plot(epochs, train_loss, 'b-', label='Train Loss')
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axs[0, 0].set_xlabel('Epoch')
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axs[0, 0].set_ylabel('Loss')
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axs[0, 0].set_title('Training Loss over Epochs')
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axs[0, 0].legend()
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# 折线图：训练准确率和测试准确率
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axs[0, 1].plot(epochs, train_accuracy, 'g-', label='Train Accuracy')
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axs[0, 1].plot(epochs, test_accuracy, 'r-', label='Test Accuracy')
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axs[0, 1].set_xlabel('Epoch')
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axs[0, 1].set_ylabel('Accuracy (%)')
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axs[0, 1].set_title('Train and Test Accuracy over Epochs')
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axs[0, 1].legend()
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# 折线图：F1 Score
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axs[0, 2].plot(epochs, f1_score, 'm-', label='F1 Score')
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axs[0, 2].set_xlabel('Epoch')
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axs[0, 2].set_ylabel('F1 Score')
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axs[0, 2].set_title('F1 Score over Epochs')
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axs[0, 2].legend()
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# 折线图：Precision
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axs[1, 0].plot(epochs, precision, 'c-', label='Precision')
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axs[1, 0].set_xlabel('Epoch')
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axs[1, 0].set_ylabel('Precision')
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axs[1, 0].set_title('Precision over Epochs')
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axs[1, 0].legend()
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# 折线图：Recall
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axs[1, 1].plot(epochs, recall, 'y-', label='Recall')
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axs[1, 1].set_xlabel('Epoch')
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axs[1, 1].set_ylabel('Recall')
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axs[1, 1].set_title('Recall over Epochs')
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axs[1, 1].legend()
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# 空白或额外的图表空间（如果需要）
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axs[1, 2].axis('off')
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plt.tight_layout()
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plt.savefig(file_path.replace('.xlsx', '_epoch.png'))
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plt.close()
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def draw_last_epoch_bar_chart(file_path):
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df = pd.read_excel(file_path)
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last_epoch_data = df.iloc[-1]
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metrics = ['train_loss', 'train_accuracy', 'test_accuracy', 'f1_score', 'precision', 'recall']
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values = [last_epoch_data[metric] for metric in metrics]
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labels = ['Train Loss', 'Train Accuracy', 'Test Accuracy', 'F1 Score', 'Precision', 'Recall']
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# 调整数值格式
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values[1] *= 100 # Train Accuracy
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values[2] *= 100 # Test Accuracy
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plt.figure(figsize=(10, 6))
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plt.bar(labels, values, color=['blue', 'green', 'red', 'magenta', 'cyan', 'yellow'])
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plt.xlabel('Metrics')
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plt.ylabel('Values')
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plt.title('Last Epoch Metrics')
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plt.ylim(bottom=0)
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# 添加数值标签
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for i, value in enumerate(values):
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plt.text(i, value + 0.01, f'{value:.2f}', ha='center')
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plt.tight_layout()
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plt.savefig(file_path.replace('.xlsx', '_last_epoch_bar.png'))
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plt.close()
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def draw_and_save_cm(file_path):
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# 读取 Excel 文件
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df_cm = pd.read_excel(file_path)
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# 获取标签（假设 DataFrame 的列为类别标签）
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labels = df_cm.columns[1:].tolist()
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# 获取混淆矩阵和归一化混淆矩阵的数值
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cm = df_cm.values[:, 1:]
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# 创建一个图像和子图
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fig, axs = plt.subplots(1, 2, figsize=(12, 6))
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# 绘制普通混淆矩阵
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axs[0].imshow(cm, interpolation='nearest', cmap='Blues')
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axs[0].set_title('Confusion Matrix')
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axs[0].set_xlabel('Predicted')
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axs[0].set_ylabel('True')
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axs[0].set_xticks(np.arange(len(labels)))
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axs[0].set_yticks(np.arange(len(labels)))
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axs[0].set_xticklabels(labels)
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axs[0].set_yticklabels(labels)
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# 添加数值标签
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for i in range(len(labels)):
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for j in range(len(labels)):
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axs[0].text(j, i, f'{cm[i, j]}', ha='center', va='center')
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# 调整布局并保存图像
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plt.tight_layout()
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plt.savefig(file_path.replace('.xlsx', '.png'))
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plt.close()
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@ -2,7 +2,7 @@ import torch
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import torch.nn as nn
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import pandas as pd
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from sklearn.decomposition import PCA
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from sklearn.metrics import confusion_matrix
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from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score
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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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@ -27,20 +27,22 @@ class Qnn(nn.Module):
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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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'test_accuracy': [],
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'precision': [],
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'recall': [],
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'f1_score': []
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}
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self.pca_2d, self.pca_3d = None, None
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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).unsqueeze(1)
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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).unsqueeze(1)
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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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@ -57,7 +59,7 @@ class Qnn(nn.Module):
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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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optimizer = torch.optim.Adam(model.parameters(), lr=0.00001, 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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@ -106,12 +108,24 @@ class Qnn(nn.Module):
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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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f1 = f1_score(all_labels, all_predicted, average='macro')
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precision = precision_score(all_labels, all_predicted, average='macro')
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recall = recall_score(all_labels, all_predicted, average='macro')
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if (epoch + 1) % 10 == 0:
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print('===============================================')
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print(f'Epoch [{epoch + 1} / {epochs_times}]:')
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print(f'Train Accuracy: {train_accuracy * 100:.2f}%, Test Accuracy: {test_accuracy*100:.2f}%, Loss: {train_loss:.4f}')
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print(f'Precision: {precision:.4f}, Recall: {recall:.4f}, F1 Score:{f1:.4f}, ')
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print('===============================================')
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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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self.epoch_data['precision'].append(precision)
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self.epoch_data['recall'].append(recall)
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self.epoch_data['f1_score'].append(f1)
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scheduler.step(train_loss)
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@ -125,7 +139,9 @@ class Qnn(nn.Module):
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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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self.cm = confusion_matrix(all_labels, all_predicted)
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self.cmn = confusion_matrix(all_labels, all_predicted, normalize='true')
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print(self.cm)
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return
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@ -147,11 +163,14 @@ class Qnn(nn.Module):
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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_cmn(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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28
main.py
28
main.py
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@ -1,40 +1,42 @@
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from Qtorch.Models.Qcnn import QCNN
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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
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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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# 输入元数据文件夹名称
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projet_name = '20241228 Write'
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projet_name = '20250623 FHH-write'
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# 请在[]内输入每一个分类的名称
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label_names = ['I', 'L', 'O', 'V', 'E', 'F', 'J', 'U', 'T']
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label_names = ['5', '2', '0', 'M', 'J', 'U']
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print(label_names)
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data = load_data(projet_name, label_names, isDir=False, fileClass='xlsx')
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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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# 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],
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# dropout_rate=0
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# )
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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 = [16],
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dropout_rate=0
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)
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model = QCNN(
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X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test,
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dropout_rate=0
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)
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# model = QCNN(
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# X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test,
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# dropout_rate=0
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# )
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pca_2d, pca_3d = model.get_PCA()
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model.fit(300)
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cm = model.get_cm()
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cmn = model.get_cmn()
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epoch_data = model.get_epoch_data()
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save_to_xlsx(project_name=projet_name, file_name="pca_2d", data=pca_2d)
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save_to_xlsx(project_name=projet_name, file_name="pca_3d", data=pca_3d)
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save_to_xlsx(project_name=projet_name, file_name="cm", data=cm )
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save_to_xlsx(project_name=projet_name, file_name="cm", data=cm)
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save_to_xlsx(project_name=projet_name, file_name="cmn", data=cmn)
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save_to_xlsx(project_name=projet_name, file_name="acc_and_loss", data=epoch_data)
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print("Done")
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