glowz
87f2756fdf
- Implemented a new script `val_test.py` to analyze classification results from a JSONL file. - Extracted true labels and predicted responses, handling invalid entries gracefully. - Generated a classification report with accuracy metrics and detailed statistics for each category. - Added functionality to export results to CSV and save analysis reports. - Included visualization of confusion matrix and category accuracy distribution. - Ensured dynamic handling of categories based on the input data.
303 lines
16 KiB
Python
303 lines
16 KiB
Python
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import json
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import os
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import argparse
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import random
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def extract_title_author_and_abstract(content_text):
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"""
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content_text: 格式示例"Based on the title 'The Quantum Primordial Black Holes, Dimensionless Small Parameter, Inflationary Cosmology and Non-Gaussianity', authors 'Alexander Shalyt-Margolin', and abstract 'In the present work consideration is given to the primordial black holes ({\\bf pbhs}) in the Schwarzschild-de Sitter Metric with small mass (ultralight) in the preinflationary epoch. Within the scope of natural assumptions, it has been shown that the quantum-gravitational corrections ({\\bf qgcs}) to the characteristics of such black holes can contribute to all the cosmological parameters, shifting them compared with the semiclassical consideration. These contributions are determined by a series expansion in terms of a small parameter dependent on the hole mass (radius). For this pattern different cases have been considered (stationary, black hole evaporation...). It has been demonstrated that involvement of ({\\bf qgcs}) leads to a higher probability for the occurrence of such {\\bf pbhs}. Besides, high-energy deformations of Friedmann Equations created on the basis of these corrections have been derived for different patterns. In the last section of this work it is introduced a study into the contributions generated by the above-mentioned {\\bf qgcs} in inflationary cosmological perturbations. Besides, it has been shown that non-Gaussianity of these perturbations is higher as compared to the semi-classical pattern.', please determine the scientific category of this paper. Additional info: 35 pages, Latex ,
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A. quant-ph\nB. physics.chem-ph\nC. physics.atom-ph\nD. cond-mat.soft\nE. cs.RO\nF. cs.CL\nG. cs.SE\nH. cs.IR\nI. hep-th\nJ. hep-ph\nK. physics.optics\nL. cs.AI\nM. cs.CV\nN. nucl-th\nO. astro-ph\nP. math.PR\nQ. cs.OS\nR. eess.SP\nS. math.OC\nT. math.DS\nU. math.DG\nV. math.MP\nW. cs.MM\nX. stat.ME\nY. math.CO\nZ. cs.NE", "assistant": "I"}]}
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"""
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#content_text.split("',")
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parts = content_text.split("',")
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title = parts[0].split("'")[1].strip()
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authors = parts[1].split("'")[1].strip()
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abstract = parts[2].split("'")[1].strip()
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# # for part in parts:
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# # print(part)
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# print(title)
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# print("----------------------------------------------------------------------------------------------------------")
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# print(authors)
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# print("----------------------------------------------------------------------------------------------------------")
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# print(abstract)
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# print("----------------------------------------------------------------------------------------------------------")
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return {"title": title, "authors": authors, "abstract": abstract}
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def convert_to_alpaca_format(input_file, output_file):
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"""
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将 Swift 格式的数据转换为 Alpaca 格式
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输入格式:
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{
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"system": "你是个优秀的论文分类师",
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"conversation": [
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{
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"human": "Based on the title...",
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"assistant": "D"
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}
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]
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}
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"""
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print(f"转换数据: {input_file} -> {output_file}")
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converted_data = []
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with open(input_file, "r", encoding="utf-8") as f:
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for line in f:
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try:
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data = json.loads(line.strip())
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# 检查数据结构
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if "system" not in data or "conversation" not in data:
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print(f"警告: 数据缺少必要字段: {data}")
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continue
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# 从 system 提取指令
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instruction = data.get("system", "")
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if not instruction:
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instruction = "根据论文的标题、作者和摘要，确定该论文的科学类别。"
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# 处理对话
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for turn in data["conversation"]:
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if "human" in turn and "assistant" in turn:
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# 创建新的 Alpaca 格式数据
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new_data = {
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"messages": [
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{
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"role": "assistant",
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"content": "This is a paper titled " + turn["human"]
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}]}
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converted_data.append(new_data)
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except json.JSONDecodeError:
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print(f"警告: 无法解析JSON行: {line}")
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except Exception as e:
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print(f"处理行时发生错误: {str(e)}")
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# 写入输出文件
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with open(output_file, "w", encoding="utf-8") as f:
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for item in converted_data:
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f.write(json.dumps(item, ensure_ascii=False) + "\n")
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print(f"转换完成! 共转换 {len(converted_data)} 条数据")
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def convert_onedata2multi_type(input_file, output_file, num_templates):
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"""
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读取input_file，将Swift格式的1条数据按20种问题模板格式转换为20条数据，
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并保存为output_file
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参数:
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input_file: 输入文件路径
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output_file: 输出文件路径
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"""
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print(f"开始转换数据...每条数据生成{num_templates}条变体")
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print(f"开始转换数据: {input_file} -> {output_file}")
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category_text=" A. quant-ph\nB. physics.chem-ph\nC. physics.atom-ph\nD. cond-mat.soft\nE. cs.RO\nF. cs.CL\nG. cs.SE\nH. cs.IR\nI. hep-th\nJ. hep-ph\nK. physics.optics\nL. cs.AI\nM. cs.CV\nN. nucl-th\nO. astro-ph\nP. math.PR\nQ. cs.OS\nR. eess.SP\nS. math.OC\nT. math.DS\nU. math.DG\nV. math.MP\nW. cs.MM\nX. stat.ME\nY. math.CO\nZ. cs.NE\n"
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# 定义20种问题模板
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question_templates = [
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# 直接提问式
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"{category_text}What is the scientific category for a paper titled '{title}', authored by {authors}, with abstract '{abstract}'?",
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# 命令式
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"Classify this paper into its scientific category based on title '{title}', authors '{authors}', and abstract '{abstract}'.{category_text}",
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# 描述性引导
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"{category_text}Given a research paper with title '{title}', authors {authors}, and abstract '{abstract}', identify the appropriate discipline.",
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# 正式请求
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"Please assign the scientific category for the paper: title '{title}', authors '{authors}', abstract '{abstract}'.{category_text}",
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# 摘要优先
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"Using the abstract '{abstract}', title '{title}', and authors '{authors}', determine the paper's category.{category_text}",
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# 作者强调
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"{category_text}From authors '{authors}', title '{title}', and abstract '{abstract}', what category does this paper fall into?",
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# 问题链式
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"Here's a paper: title '{title}', authors {authors}, abstract '{abstract}'. What is its scientific category?{category_text}",
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# 简洁版
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"Category for: title '{title}', authors '{authors}', abstract '{abstract}'?{category_text}",
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# 上下文嵌入
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"Considering the title '{title}', the authors '{authors}', and the abstract content '{abstract}', please specify the paper's field.{category_text}",
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# 非正式口语
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"Hey, what category is this paper? Title '{title}', by {authors}, abstract '{abstract}'.{category_text}",
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# 元素罗列
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"{category_text}Title: '{title}'. Authors: '{authors}'. Abstract: '{abstract}'. Now, what's the scientific category?",
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# 假设场景
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"If a paper has title '{title}', authors '{authors}', and abstract '{abstract}', which scientific category best fits it?{category_text}",
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# 强调关键信息
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"Based solely on the title '{title}', authors list '{authors}', and abstract text '{abstract}', categorize this paper.{category_text}",
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# 间接询问
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"For the paper '{title}' by {authors}, with abstract '{abstract}', could you indicate its scientific discipline?{category_text}",
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# 完整句子整合
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"Determine the category of the research paper entitled '{title}', written by {authors}, and summarized as '{abstract}'.{category_text}",
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# 问题聚焦摘要
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"The abstract '{abstract}' describes a paper titled '{title}' by authors '{authors}'. What category is it?{category_text}",
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# 标题驱动
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"{category_text}Starting from the title '{title}', and considering authors '{authors}' and abstract '{abstract}', what is the paper's category?",
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# 多部分查询
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"Part 1: Title is '{title}'. Part 2: Authors are '{authors}'. Part 3: Abstract is '{abstract}'. Based on this, classify the paper.{category_text}",
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# 比较式
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"Given the details: title '{title}', authors '{authors}', abstract '{abstract}', how would you categorize this paper scientifically?{category_text}",
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# 行动导向
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"Using the provided title '{title}', authors '{authors}', and abstract '{abstract}', output the scientific category for this paper.{category_text}"
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]
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multi_type_data = []
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with open(input_file, "r", encoding="utf-8") as f:
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for line in f:
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try:
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data = json.loads(line.strip())
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# 检查新格式的数据结构
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if "messages" in data and isinstance(data["messages"], list) and len(data["messages"]) >= 3:
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# 提取系统指令
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system_instruction = ""
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human_content = ""
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assistant_content = ""
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for msg in data["messages"]:
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if msg["role"] == "system":
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system_instruction = msg["content"]
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elif msg["role"] == "user":
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human_content = msg["content"]
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elif msg["role"] == "assistant":
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assistant_content = msg["content"]
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# 提取标题、作者和摘要
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extracted = extract_title_author_and_abstract(human_content)
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title = extracted.get("title", "")
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authors = extracted.get("authors", "")
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abstract = extracted.get("abstract", "")
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n = min(num_templates, len(question_templates))
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selected_templates = random.sample(question_templates, n)
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# 为每个问题模板创建新数据
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for template in selected_templates:
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formatted_question = template.format(
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title=title,
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authors=authors,
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abstract=abstract,
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category_text=category_text
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)
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# 创建新的数据条目（保持新格式）
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new_data = {
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"messages": [
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{"role": "system", "content": system_instruction},
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{"role": "user", "content": formatted_question},
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{"role": "assistant", "content": assistant_content}
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]
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}
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multi_type_data.append(new_data)
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# 检查旧格式的数据结构
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elif "system" in data and "conversation" in data and data["conversation"]:
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system_instruction = data.get("system", "根据论文的标题、作者和摘要，确定该论文的科学类别。")
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for turn in data["conversation"]:
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if "human" in turn and "assistant" in turn:
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extracted = extract_title_author_and_abstract(turn["human"])
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title = extracted.get("title", "")
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authors = extracted.get("authors", "")
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abstract = extracted.get("abstract", "")
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n = min(num_templates, len(question_templates))
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selected_templates = random.sample(question_templates, n)
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for template in selected_templates:
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formatted_question = template.format(
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title=title,
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authors=authors,
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abstract=abstract,
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category_text=category_text
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)
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new_data = {
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"system": system_instruction,
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"conversation": [
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{
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"human": formatted_question,
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"assistant": turn["assistant"]
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}
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]
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}
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multi_type_data.append(new_data)
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else:
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print(f"警告: 数据格式不识别: {data}")
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continue
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except json.JSONDecodeError:
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print(f"警告: 无法解析JSON行: {line}")
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except Exception as e:
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print(f"处理行时发生错误: {str(e)}")
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# 写入输出文件
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with open(output_file, "w", encoding="utf-8") as f:
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for item in multi_type_data:
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f.write(json.dumps(item, ensure_ascii=False) + "\n")
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print(f"转换完成! 共转换 {len(multi_type_data)} 条数据")
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if __name__ == "__main__":
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content_text="Based on the title 'The Quantum Primordial Black Holes, Dimensionless Small Parameter, Inflationary Cosmology and Non-Gaussianity', authors 'Alexander Shalyt-Margolin', and abstract 'In the present work consideration is given to the primordial black holes ({\\bf pbhs}) in the Schwarzschild-de Sitter Metric with small mass (ultralight) in the preinflationary epoch. Within the scope of natural assumptions, it has been shown that the quantum-gravitational corrections ({\\bf qgcs}) to the characteristics of such black holes can contribute to all the cosmological parameters, shifting them compared with the semiclassical consideration. These contributions are determined by a series expansion in terms of a small parameter dependent on the hole mass (radius). For this pattern different cases have been considered (stationary, black hole evaporation...). It has been demonstrated that involvement of ({\\bf qgcs}) leads to a higher probability for the occurrence of such {\\bf pbhs}. Besides, high-energy deformations of Friedmann Equations created on the basis of these corrections have been derived for different patterns. In the last section of this work it is introduced a study into the contributions generated by the above-mentioned {\\bf qgcs} in inflationary cosmological perturbations. Besides, it has been shown that non-Gaussianity of these perturbations is higher as compared to the semi-classical pattern.', please determine the scientific category of this paper. Additional info: 35 pages, Latex , A. quant-ph\nB. physics.chem-ph\nC. physics.atom-ph\nD. cond-mat.soft\nE. cs.RO\nF. cs.CL\nG. cs.SE\nH. cs.IR\nI. hep-th\nJ. hep-ph\nK. physics.optics\nL. cs.AI\nM. cs.CV\nN. nucl-th\nO. astro-ph\nP. math.PR\nQ. cs.OS\nR. eess.SP\nS. math.OC\nT. math.DS\nU. math.DG\nV. math.MP\nW. cs.MM\nX. stat.ME\nY. math.CO\nZ. cs.NE"
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extract_title_author_and_abstract(content_text)
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input_file = "G:\\11\\data-prepare\\arxiv-metadata-oai-snapshot--swift-26-500.jsonl"
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output_file = "G:\\11\\data-prepare\\arxiv-metadata-oai-snapshot--swift-26-500-m.jsonl" # 输出文件路径
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# input_file = "G:\\11\\data-prepare\\newformat_sft_test_data--swift-sft-26.jsonl"
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# output_file = "G:\\11\\data-prepare\\newformat_sft_test_data--swift-sft-26-m4.jsonl" # 输出文件路径
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convert_onedata2multi_type(input_file, output_file, num_templates=1)
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