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import torch
from MyData import MyDataset
from torch.utils.data import DataLoader
from net import Model
from transformers import BertTokenizer
from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score
from sklearn.metrics import confusion_matrix, classification_report
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import os
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
token = BertTokenizer.from_pretrained(
r"\models\bert-base-chinese\models--bert-base-chinese\snapshots\c30a6ed22ab4564dc1e3b2ecbf6e766b0611a33f")
def collate_fn(data):
sents = [i[0] for i in data]
label = [i[1] for i in data]
data = token.batch_encode_plus(
batch_text_or_text_pairs=sents,
truncation=True,
max_length=512,
padding="max_length",
return_tensors="pt",
return_length=True
)
input_ids = data["input_ids"]
attention_mask = data["attention_mask"]
token_type_ids = data["token_type_ids"]
label = torch.LongTensor(label)
return input_ids, attention_mask, token_type_ids, label
def evaluate_model(model, test_loader, device):
"""
评估模型在测试集上的性能
:param model: 待评估模型
:param test_loader: 测试数据加载器
:param device: 计算设备
:return: 评估指标字典
""" model.eval()
all_preds, all_labels = [], []
for i, (input_ids, attention_mask, token_type_ids, labels) in enumerate(test_loader):
input_ids = input_ids.to(device)
attention_mask = attention_mask.to(device)
token_type_ids = token_type_ids.to(device)
labels = labels.to(device)
with torch.no_grad():
outputs = model(input_ids, attention_mask, token_type_ids)
preds = torch.argmax(outputs, dim=1)
all_preds.extend(preds.cpu().numpy())
all_labels.extend(labels.cpu().numpy())
metrics = {
'accuracy': accuracy_score(all_labels, all_preds),
'precision_macro': precision_score(all_labels, all_preds, average='macro'),
'recall_macro': recall_score(all_labels, all_preds, average='macro'),
'f1_macro': f1_score(all_labels, all_preds, average='macro'),
'precision_weighted': precision_score(all_labels, all_preds, average='weighted'),
'recall_weighted': recall_score(all_labels, all_preds, average='weighted'),
'f1_weighted': f1_score(all_labels, all_preds, average='weighted'),
'confusion_matrix': confusion_matrix(all_labels, all_preds),
'classification_report': classification_report(all_labels, all_preds, digits=4)
}
return metrics
def plot_confusion_matrix(cm, class_names, save_path=None):
"""
绘制并保存混淆矩阵
:param cm: 混淆矩阵
:param class_names: 类别名称列表
:param save_path: 保存路径（可选）
""" plt.figure(figsize=(10, 8))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
xticklabels=class_names, yticklabels=class_names)
plt.xlabel('预测标签')
plt.ylabel('真实标签')
plt.title('混淆矩阵')
if save_path:
plt.savefig(save_path, bbox_inches='tight')
print(f"混淆矩阵已保存至: {save_path}")
plt.show()
def save_metrics_to_file(metrics, save_path):
"""
将评估指标保存到文本文件
:param metrics: 评估指标字典
:param save_path: 保存路径
""" with open(save_path, 'w', encoding='utf-8') as f:
f.write("模型评估报告\n")
f.write("=" * 50 + "\n")
f.write(f"准确率 (Accuracy): {metrics['accuracy']:.4f}\n\n")
f.write("宏平均指标 (Macro-average):\n")
f.write(f" 精确率 (Precision): {metrics['precision_macro']:.4f}\n")
f.write(f" 召回率 (Recall): {metrics['recall_macro']:.4f}\n")
f.write(f" F1分数 (F1 Score): {metrics['f1_macro']:.4f}\n\n")
f.write("加权平均指标 (Weighted-average):\n")
f.write(f" 精确率 (Precision): {metrics['precision_weighted']:.4f}\n")
f.write(f" 召回率 (Recall): {metrics['recall_weighted']:.4f}\n")
f.write(f" F1分数 (F1 Score): {metrics['f1_weighted']:.4f}\n\n")
f.write("分类报告 (Classification Report):\n")
f.write(metrics['classification_report'])
f.write("\n\n混淆矩阵 (Confusion Matrix):\n")
np.savetxt(f, metrics['confusion_matrix'], fmt='%d')
print(f"评估报告已保存至: {save_path}")
if __name__ == '__main__':
test_dataset = MyDataset("test")
test_loader = DataLoader(
dataset=test_dataset,
batch_size=100,
shuffle=False,
drop_last=False,
collate_fn=collate_fn
)
print(f"使用设备: {DEVICE}")
model = Model().to(DEVICE)
model_path = "params/best_bert.pth"
if not os.path.exists(model_path):
raise FileNotFoundError(f"模型参数文件不存在: {model_path}")
model.load_state_dict(torch.load(model_path))
metrics = evaluate_model(model, test_loader, DEVICE)
print("\n" + "=" * 50)
print(f"准确率 (Accuracy): {metrics['accuracy']:.4f}")
print("\n宏平均指标 (Macro-average):")
print(f" 精确率 (Precision): {metrics['precision_macro']:.4f}")
print(f" 召回率 (Recall): {metrics['recall_macro']:.4f}")
print(f" F1分数 (F1 Score): {metrics['f1_macro']:.4f}")
print("\n加权平均指标 (Weighted-average):")
print(f" 精确率 (Precision): {metrics['precision_weighted']:.4f}")
print(f" 召回率 (Recall): {metrics['recall_weighted']:.4f}")
print(f" F1分数 (F1 Score): {metrics['f1_weighted']:.4f}")
print("\n分类报告 (Classification Report):")
print(metrics['classification_report'])
class_names = ["类别0", "类别1"]
plot_confusion_matrix(metrics['confusion_matrix'], class_names, "confusion_matrix.png")
save_metrics_to_file(metrics, "evaluation_report.txt")
print("评估完成!")
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