174 lines
5.9 KiB
Python
174 lines
5.9 KiB
Python
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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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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