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Add files via upload
for test and result analysis
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promptinjection/guardtest.py
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249
promptinjection/guardtest.py
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import pandas as pd
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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from transformers import pipeline
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import os
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import time
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import json
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from datetime import datetime
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def load_checkpoint(checkpoint_file):
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"""加载断点信息"""
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if os.path.exists(checkpoint_file):
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try:
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with open(checkpoint_file, 'r', encoding='utf-8') as f:
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checkpoint = json.load(f)
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print(f"发现断点文件，从第 {checkpoint['last_processed'] + 1} 条记录开始继续处理")
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return checkpoint
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except Exception as e:
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print(f"读取断点文件失败: {e}")
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return None
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return None
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def save_checkpoint(checkpoint_file, last_processed, total_records):
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"""保存断点信息"""
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checkpoint = {
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'last_processed': last_processed,
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'total_records': total_records,
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'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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}
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try:
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with open(checkpoint_file, 'w', encoding='utf-8') as f:
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json.dump(checkpoint, f, ensure_ascii=False, indent=2)
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except Exception as e:
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print(f"保存断点文件失败: {e}")
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def save_batch_results(results, output_file, mode='w'):
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"""保存批量结果到CSV"""
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try:
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results_df = pd.DataFrame(results)
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if mode == 'w':
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results_df.to_csv(output_file, index=False, encoding='utf-8')
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else: # append mode
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results_df.to_csv(output_file, mode='a', header=False, index=False, encoding='utf-8')
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return True
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except Exception as e:
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print(f"保存结果失败: {e}")
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return False
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def main():
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# 设置文件路径,可使用ProtectAI,deepset,metaguard86M
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local_path = "./models/metaguard86M"
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input_file = "deepset_prompt_injection_data.csv"
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output_file = "./result/deepset_86M_results.csv"
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checkpoint_file = "86M_checkpoint.json"
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batch_size = 10 # 每批处理的记录数
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# 检查模型路径是否存在
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if not os.path.exists(local_path):
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print(f"错误: 模型路径 {local_path} 不存在")
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return
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# 加载断点信息
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checkpoint = load_checkpoint(checkpoint_file)
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start_idx = 0
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if checkpoint:
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start_idx = checkpoint['last_processed'] + 1
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# 加载模型和分词器
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print("正在加载模型和分词器...")
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try:
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tokenizer = AutoTokenizer.from_pretrained(local_path)
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model = AutoModelForSequenceClassification.from_pretrained(local_path)
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# 创建分类器
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classifier = pipeline(
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"text-classification",
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model=model,
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tokenizer=tokenizer,
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truncation=True,
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max_length=512,
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)
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print("模型加载成功!")
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except Exception as e:
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print(f"模型加载失败: {e}")
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return
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# 读取CSV文件
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if not os.path.exists(input_file):
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print(f"错误: 输入文件 {input_file} 不存在")
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return
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try:
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print(f"正在读取 {input_file}...")
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df = pd.read_csv(input_file)
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print(f"成功读取 {len(df)} 行数据")
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# 检查必要的列是否存在
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if 'text' not in df.columns:
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print("错误: CSV文件中缺少 'text' 列")
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return
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# 如果没有label列，创建一个空的
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if 'label' not in df.columns:
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df['label'] = ''
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except Exception as e:
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print(f"读取CSV文件失败: {e}")
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return
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# 如果是断点续传，检查总记录数是否一致
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if checkpoint and checkpoint['total_records'] != len(df):
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print(f"警告: 当前文件记录数({len(df)})与断点记录数({checkpoint['total_records']})不一致")
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response = input("是否要重新开始处理? (y/n): ")
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if response.lower() == 'y':
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start_idx = 0
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if os.path.exists(output_file):
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os.remove(output_file)
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# 如果从头开始，清空之前的输出文件
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if start_idx == 0 and os.path.exists(output_file):
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os.remove(output_file)
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print("已清空之前的输出文件")
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# 创建结果列表
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batch_results = []
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total_processed = start_idx
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# 记录分类开始时间
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start_time = time.time()
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start_datetime = datetime.now()
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print(f"分类开始时间: {start_datetime.strftime('%Y-%m-%d %H:%M:%S')}")
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if start_idx > 0:
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print(f"从第 {start_idx + 1} 条记录开始继续处理...")
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else:
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print("开始分类处理...")
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try:
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for idx in range(start_idx, len(df)):
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row = df.iloc[idx]
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text = str(row['text']) # 确保是字符串类型
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source_label = row.get('label', '') # 获取原始标签，如果不存在则为空
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try:
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# 进行分类
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prediction = classifier(text)
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# 提取预测结果
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predicted_label = prediction[0]['label']
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predicted_score = prediction[0]['score']
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# 添加到批量结果列表
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batch_results.append({
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'text': text,
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'source_label': source_label,
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'classified_label': predicted_label,
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'classified_score': predicted_score
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})
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total_processed = idx
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# 每处理batch_size条记录或到达最后一条记录时保存
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if len(batch_results) >= batch_size or idx == len(df) - 1:
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# 保存批量结果
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mode = 'w' if idx < batch_size and start_idx == 0 else 'a'
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if save_batch_results(batch_results, output_file, mode):
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print(f"已处理并保存 {idx + 1}/{len(df)} 条数据")
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# 保存断点
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save_checkpoint(checkpoint_file, idx, len(df))
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# 清空批量结果列表
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batch_results = []
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else:
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print(f"保存第 {idx + 1} 批数据失败，停止处理")
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break
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# 打印进度（每10条显示一次）
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elif (idx + 1) % 10 == 0:
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print(f"已处理 {idx + 1}/{len(df)} 条数据")
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except Exception as e:
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print(f"处理第 {idx + 1} 行时出错: {e}")
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# 添加错误记录
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batch_results.append({
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'text': text,
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'source_label': source_label,
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'classified_label': 'ERROR',
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'classified_score': 0.0
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})
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total_processed = idx
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except KeyboardInterrupt:
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print(f"\n用户中断处理，已处理到第 {total_processed + 1} 条记录")
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# 保存剩余的批量结果
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if batch_results:
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mode = 'a' if total_processed > 0 else 'w'
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save_batch_results(batch_results, output_file, mode)
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# 保存断点
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save_checkpoint(checkpoint_file, total_processed, len(df))
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return
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except Exception as e:
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print(f"\n处理过程中发生错误: {e}")
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# 保存剩余的批量结果
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if batch_results:
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mode = 'a' if total_processed > 0 else 'w'
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save_batch_results(batch_results, output_file, mode)
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# 保存断点
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save_checkpoint(checkpoint_file, total_processed, len(df))
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return
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# 记录分类结束时间并计算性能统计
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end_time = time.time()
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end_datetime = datetime.now()
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total_time = end_time - start_time
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processed_count = total_processed - start_idx + 1
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print(f"\n分类结束时间: {end_datetime.strftime('%Y-%m-%d %H:%M:%S')}")
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print(f"本次处理耗时: {total_time:.2f} 秒")
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print(f"本次处理记录数: {processed_count}")
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if processed_count > 0:
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print(f"平均每条数据耗时: {total_time/processed_count:.3f} 秒")
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print(f"处理速度: {processed_count/total_time:.2f} 条/秒")
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# 显示最终统计信息
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try:
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if os.path.exists(output_file):
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results_df = pd.read_csv(output_file)
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print(f"\n总共完成 {len(results_df)} 条记录的分类")
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# 打印统计信息
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print("\n分类结果统计:")
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print(results_df['classified_label'].value_counts())
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# 显示前几行结果作为示例
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print(f"\n前5行结果预览:")
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print(results_df.head())
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# 如果全部完成，删除断点文件
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if len(results_df) == len(df):
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if os.path.exists(checkpoint_file):
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os.remove(checkpoint_file)
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print(f"\n所有记录处理完成，已删除断点文件")
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except Exception as e:
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print(f"读取最终结果失败: {e}")
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if __name__ == "__main__":
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main()
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161
promptinjection/result_analysis.py
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161
promptinjection/result_analysis.py
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import pandas as pd
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import numpy as np
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# 全局配置变量
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SAFE_LABEL = "LABEL_0" # 表示安全/无害的标签
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def analyze_classification_results(csv_file_path, safe_label=None):
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"""
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分析二分类结果，计算混淆矩阵和召回率
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参数:
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csv_file_path: CSV文件路径
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safe_label: 表示安全/无害的标签，默认使用全局变量SAFE_LABEL
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返回:
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包含统计结果的字典
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"""
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# 如果没有指定safe_label，使用全局变量
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if safe_label is None:
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safe_label = SAFE_LABEL
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# 读取CSV文件
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try:
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df = pd.read_csv(csv_file_path)
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print(f"成功读取数据，共 {len(df)} 行记录")
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print(f"列名: {list(df.columns)}")
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except Exception as e:
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print(f"读取文件出错: {e}")
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return None
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# 检查数据类型并进行相应处理
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print(f"\nsource_label数据类型: {df['source_label'].dtype}")
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print(f"source_label唯一值: {df['source_label'].unique()}")
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print(f"classified_label数据类型: {df['classified_label'].dtype}")
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print(f"classified_label唯一值: {df['classified_label'].unique()}")
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# 数据预处理：转换标签为二进制
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# 根据实际数据，source_label已经是0和1，直接使用
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# 0表示benign（无害），1表示有害
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df['true_label'] = df['source_label']
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# classified_label: safe_label=0, 其他=1
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df['pred_label'] = df['classified_label'].apply(lambda x: 0 if str(x).strip().upper() == safe_label.upper() else 1)
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# 打印标签分布
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print("\n=== 标签分布 ===")
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print("真实标签分布:")
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print(f" benign (0): {sum(df['true_label'] == 0)} 个")
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print(f" 有害 (1): {sum(df['true_label'] == 1)} 个")
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print("\n预测标签分布:")
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print(f" {safe_label} (0): {sum(df['pred_label'] == 0)} 个")
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print(f" 非{safe_label} (1): {sum(df['pred_label'] == 1)} 个")
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# 计算混淆矩阵
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# TP: 真实为1，预测为1 (正确识别为有害)
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# TN: 真实为0，预测为0 (正确识别为无害)
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# FP: 真实为0，预测为1 (误判为有害)
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# FN: 真实为1，预测为0 (漏判为无害)
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TP = sum((df['true_label'] == 1) & (df['pred_label'] == 1))
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TN = sum((df['true_label'] == 0) & (df['pred_label'] == 0))
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FP = sum((df['true_label'] == 0) & (df['pred_label'] == 1))
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FN = sum((df['true_label'] == 1) & (df['pred_label'] == 0))
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# 计算性能指标
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recall = TP / (TP + FN) if (TP + FN) > 0 else 0 # 召回率/敏感度
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precision = TP / (TP + FP) if (TP + FP) > 0 else 0 # 精确率
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accuracy = (TP + TN) / (TP + TN + FP + FN) # 准确率
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specificity = TN / (TN + FP) if (TN + FP) > 0 else 0 # 特异性
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false_positive_rate = FP / (FP + TN) if (FP + TN) > 0 else 0 # 误报率
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f1_score = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
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# 输出结果
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print("\n=== 混淆矩阵 ===")
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print(f"True Positive (TP): {TP}")
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print(f"True Negative (TN): {TN}")
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print(f"False Positive (FP): {FP}")
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print(f"False Negative (FN): {FN}")
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print("\n=== 性能指标 ===")
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print(f"召回率 (Recall): {recall:.4f}")
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print(f"精确率 (Precision): {precision:.4f}")
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print(f"准确率 (Accuracy): {accuracy:.4f}")
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print(f"特异性 (Specificity): {specificity:.4f}")
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print(f"误报率 (FPR): {false_positive_rate:.4f}")
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print(f"F1分数: {f1_score:.4f}")
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print("\n=== 混淆矩阵表格 ===")
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print(" 预测")
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print(f" {safe_label}(0) 非{safe_label}(1)")
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print(f"真实 benign(0) {TN:4d} {FP:4d}")
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print(f" 有害(1) {FN:4d} {TP:4d}")
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# 详细分析
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print("\n=== 详细分析 ===")
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if FP > 0:
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print(f"误报(FP): {FP} 个良性样本被错误分类为有害")
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fp_samples = df[(df['true_label'] == 0) & (df['pred_label'] == 1)]
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print("误报样本示例:")
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for idx, row in fp_samples.head(3).iterrows():
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print(f" - 文本: {row['text'][:100]}...")
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print(f" 预测分数: {row['classified_score']:.4f}")
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if FN > 0:
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print(f"\n漏报(FN): {FN} 个有害样本被错误分类为无害")
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fn_samples = df[(df['true_label'] == 1) & (df['pred_label'] == 0)]
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print("漏报样本示例:")
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for idx, row in fn_samples.head(3).iterrows():
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print(f" - 文本: {row['text'][:100]}...")
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print(f" 预测分数: {row['classified_score']:.4f}")
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# 分析预测分数分布
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print("\n=== 预测分数分析 ===")
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print(f"{safe_label}预测的平均分数: {df[df['pred_label'] == 0]['classified_score'].mean():.4f}")
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print(f"非{safe_label}预测的平均分数: {df[df['pred_label'] == 1]['classified_score'].mean():.4f}")
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# 返回结果字典
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results = {
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'TP': TP, 'TN': TN, 'FP': FP, 'FN': FN,
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'recall': recall, 'precision': precision,
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'accuracy': accuracy, 'specificity': specificity,
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'false_positive_rate': false_positive_rate, 'f1_score': f1_score,
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'data_summary': {
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'total_samples': len(df),
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'benign_samples': sum(df['true_label'] == 0),
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'harmful_samples': sum(df['true_label'] == 1),
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'safe_predictions': sum(df['pred_label'] == 0),
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'unsafe_predictions': sum(df['pred_label'] == 1)
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}
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}
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return results
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# 使用示例
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if __name__ == "__main__":
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# 全局配置：可以根据不同模型修改安全标签
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#SAFE_LABEL = "LABEL_0" # 用于llama prompt guard的模型
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#SAFE_LABEL = "LEGIT" # 用于Deepset模型
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SAFE_LABEL = "SAFE" # 用于ProtectAI模型
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# 替换为您的CSV文件路径
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csv_file_path = "result/deepset_protectai_results.csv" # 请修改为实际的文件路径
|
||||
|
||||
print("开始分析分类结果...")
|
||||
print(f"当前安全标签配置: {SAFE_LABEL}")
|
||||
|
||||
# 可以通过参数覆盖全局配置
|
||||
# results = analyze_classification_results(csv_file_path, safe_label="SAFE")
|
||||
results = analyze_classification_results(csv_file_path)
|
||||
|
||||
if results:
|
||||
print(f"\n分析完成！")
|
||||
print(f"准确率: {results['accuracy']:.4f}")
|
||||
print(f"召回率: {results['recall']:.4f}")
|
||||
print(f"精确率: {results['precision']:.4f}")
|
||||
print(f"误报率: {results['false_positive_rate']:.4f}")
|
||||
print(f"F1分数: {results['f1_score']:.4f}")
|
||||
else:
|
||||
print("分析失败，请检查文件路径和格式。")
|
||||
Loading…
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Reference in New Issue
Block a user