126 lines
3.6 KiB
Python
126 lines
3.6 KiB
Python
import torch
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
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import seaborn as sns
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import os
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class AverageMeter:
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"""跟踪平均值和当前值"""
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def __init__(self):
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self.reset()
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def reset(self):
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self.val = 0
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self.avg = 0
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self.sum = 0
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self.count = 0
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def update(self, val, n=1):
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self.val = val
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self.sum += val * n
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self.count += n
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self.avg = self.sum / self.count
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class EarlyStopping:
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"""提前停止训练，避免过拟合"""
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def __init__(self, patience=7, delta=0, path='checkpoint.pt'):
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self.patience = patience
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self.delta = delta
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self.path = path
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self.counter = 0
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self.best_score = None
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self.early_stop = False
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def __call__(self, val_loss, model):
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score = -val_loss
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if self.best_score is None:
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self.best_score = score
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self.save_checkpoint(val_loss, model)
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elif score < self.best_score + self.delta:
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self.counter += 1
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print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
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if self.counter >= self.patience:
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self.early_stop = True
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else:
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self.best_score = score
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self.save_checkpoint(val_loss, model)
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self.counter = 0
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def save_checkpoint(self, val_loss, model):
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torch.save(model.state_dict(), self.path)
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print(f'验证损失降低 ({self.best_score:.6f} --> {-val_loss:.6f}). 保存模型...')
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def plot_training_curves(train_losses, val_losses, train_accs, val_accs, save_path=None):
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"""绘制训练和验证的损失与准确率曲线"""
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plt.figure(figsize=(12, 5))
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plt.subplot(1, 2, 1)
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plt.plot(train_losses, label='训练损失')
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plt.plot(val_losses, label='验证损失')
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plt.title('损失曲线')
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plt.xlabel('Epoch')
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plt.ylabel('损失')
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plt.legend()
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plt.grid(True)
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plt.subplot(1, 2, 2)
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plt.plot(train_accs, label='训练准确率')
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plt.plot(val_accs, label='验证准确率')
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plt.title('准确率曲线')
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plt.xlabel('Epoch')
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plt.ylabel('准确率')
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plt.legend()
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plt.grid(True)
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plt.tight_layout()
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if save_path:
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plt.savefig(save_path)
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plt.show()
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def plot_confusion_matrix(y_true, y_pred, class_names=None, save_path=None):
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"""绘制混淆矩阵"""
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cm = confusion_matrix(y_true, y_pred)
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plt.figure(figsize=(8, 6))
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sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
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xticklabels=class_names if class_names else "auto",
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yticklabels=class_names if class_names else "auto")
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plt.title('混淆矩阵')
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plt.xlabel('预测标签')
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plt.ylabel('真实标签')
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if save_path:
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plt.savefig(save_path)
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plt.show()
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def compute_metrics(y_true, y_pred, y_proba=None):
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"""计算各种评估指标"""
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accuracy = accuracy_score(y_true, y_pred)
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precision = precision_score(y_true, y_pred, average='binary', zero_division=0)
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recall = recall_score(y_true, y_pred, average='binary')
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f1 = f1_score(y_true, y_pred, average='binary')
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metrics = {
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'accuracy': accuracy,
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'precision': precision,
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'recall': recall,
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'f1': f1
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}
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return metrics
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def save_results(results, filename):
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"""保存结果到文本文件"""
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with open(filename, 'w') as f:
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for key, value in results.items():
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f.write(f"{key}: {value}\n") |