添加多个类别关键词，优化数据处理逻辑，支持从arXiv提取和筛选论文数据
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91
01-pre-multi.py
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91
01-pre-multi.py
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import json
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# 要保留的类别关键词
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# target_categories = {
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# "astro-ph", "cond-mat.mes-hall", "cond-mat.mtrl-sci",
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# "cs.CL", "cs.CV", "cs.LG",
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# "gr-qc", "hep-ph", "hep-th", "quant-ph"
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# }
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target_categories = {
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'quant-ph',
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'physics.chem-ph',
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'physics.atom-ph',
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'cond-mat.soft',
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'cs.RO',
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'cs.CL',
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'cs.SE',
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'cs.IR',
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'hep-th',
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'hep-ph',
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'physics.optics',
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'cs.AI',
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'cs.CV',
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'nucl-th',
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'astro-ph',
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'math.PR',
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'cs.OS',
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'eess.SP',
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'math.OC',
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'math.DS',
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'math.DG',
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'math.MP',
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'cs.MM',
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'stat.ME',
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'math.CO',
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'cs.NE'
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}
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input_path = "arxiv-metadata-oai-snapshot.json"#原数据路径
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output_path = "arxiv-metadata-oai-snapshot-multi.json" # 使用 JSON Lines 格式输出路径
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count = 0
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with open(input_path, 'r') as infile, open(output_path, 'w') as outfile:
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for line in infile:
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try:
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record = json.loads(line)
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record_cats = record.get("categories", "").split()
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# 获取更新日期和摘要
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update_date = record.get("update_date", "")
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abstract = record.get("abstract", "")
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# 多类别的记录
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if len(record_cats) > 1:
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# 检查是否record_cats只有一个类别在目标类别中
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# 检查record_cats中是否只有一个类别在目标类别中
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target_count = sum(1 for cat in record_cats if cat in target_categories)
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has_single_target_category = target_count == 1
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if not has_single_target_category:
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continue
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# 检查是否包含无需过滤的类别
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no_filter_categories = {'cs.OS'}
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has_no_filter_category = any(cat in no_filter_categories for cat in record_cats)
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# 如果包含无需过滤的类别，直接写入
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if has_no_filter_category:
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outfile.write(json.dumps(record) + '\n')
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count += 1
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else:
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# 其他需要满足过滤条件
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if len(abstract) >= 300 and len(abstract) <= 1024:
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if update_date and int(update_date[:4]) >= 2016:
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outfile.write(json.dumps(record) + '\n')
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count += 1
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except json.JSONDecodeError:
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continue # 忽略格式错误的行
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print(f"筛选完成，共保存了 {count} 条记录到 {output_path}")
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31
01-pre.py
31
01-pre.py
@@ -40,7 +40,7 @@ target_categories = {
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input_path = "arxiv-metadata-oai-snapshot.json"#原数据路径
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output_path = "arxiv-metadata-oai-snapshot--26.json" # 使用 JSON Lines 格式输出路径
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output_path = "arxiv-metadata-oai-snapshot-single.json" # 使用 JSON Lines 格式输出路径
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count = 0
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@@ -49,11 +49,34 @@ with open(input_path, 'r') as infile, open(output_path, 'w') as outfile:
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try:
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record = json.loads(line)
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record_cats = record.get("categories", "").split()
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# 获取更新日期和摘要
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update_date = record.get("update_date", "")
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abstract = record.get("abstract", "")
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# 只保留一个类别的记录
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if len(record_cats) > 1:
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continue
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if record_cats:
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last_cat = record_cats[-1]
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last_cat = record_cats[0]
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if last_cat in target_categories:
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outfile.write(json.dumps(record) + '\n')
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count += 1
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# 定义无需过滤条件的类别
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no_filter_categories = {'cs.OS', 'cs.MM', 'cs.NE', 'math.MP'}
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# 如果属于无需过滤的类别，直接写入
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if last_cat in no_filter_categories:
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outfile.write(json.dumps(record) + '\n')
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count += 1
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else:
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# 其他类别需要满足过滤条件
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if len(abstract) >= 300 and len(abstract) <= 1024:
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if update_date and int(update_date[:4]) >= 2016:
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outfile.write(json.dumps(record) + '\n')
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count += 1
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except json.JSONDecodeError:
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continue # 忽略格式错误的行
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@@ -1,93 +1,190 @@
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import json
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import random
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categorys = [
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'quant-ph',
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'physics.chem-ph',
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'physics.atom-ph',
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'cond-mat.soft',
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'cs.RO',
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'cs.CL',
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'cs.SE',
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'cs.IR',
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'hep-th',
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'hep-ph',
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'physics.optics',
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'cs.AI',
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'cs.CV',
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'nucl-th',
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'astro-ph',
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'math.PR',
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'cs.OS' ,
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'eess.SP',
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'math.OC',
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'math.DS',
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'math.DG',
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'math.MP',
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'cs.MM',
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'stat.ME',
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'math.CO',
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'cs.NE'
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]
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input_path = "arxiv-metadata-oai-snapshot--26.json"
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output_path = "arxiv-metadata-oai-snapshot--26-500.json"
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sample_size = 4000 # 你可以改成 10000 等其他数字
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def extract_category_mapping():
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"""定义类别到选项的映射"""
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category_to_option = {
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'quant-ph': 'A',
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'physics.chem-ph': 'B',
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'physics.atom-ph': 'C',
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'cond-mat.soft': 'D',
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'cs.RO': 'E',
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'cs.CL': 'F',
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'cs.SE': 'G',
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'cs.IR': 'H',
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'hep-th': 'I',
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'hep-ph': 'J',
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'physics.optics': 'K',
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'cs.AI': 'L',
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'cs.CV': 'M',
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'nucl-th': 'N',
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'astro-ph': 'O',
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'math.PR': 'P',
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'cs.OS': 'Q',
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'eess.SP': 'R',
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'math.OC': 'S',
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'math.DS': 'T',
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'math.DG': 'U',
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'math.MP': 'V',
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'cs.MM': 'W',
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'stat.ME': 'X',
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'math.CO': 'Y',
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'cs.NE': 'Z'
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}
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return category_to_option
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def get_category_options_text():
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"""生成选项文本"""
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options = [
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"A. quant-ph", "B. physics.chem-ph", "C. physics.atom-ph", "D. cond-mat.soft",
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"E. cs.RO", "F. cs.CL", "G. cs.SE", "H. cs.IR", "I. hep-th", "J. hep-ph",
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"K. physics.optics", "L. cs.AI", "M. cs.CV", "N. nucl-th", "O. astro-ph",
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"P. math.PR", "Q. cs.OS", "R. eess.SP", "S. math.OC", "T. math.DS",
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"U. math.DG", "V. math.MP", "W. cs.MM", "X. stat.ME", "Y. math.CO", "Z. cs.NE"
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]
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return "\n".join(options)
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def process_paper(paper_data, verbose=False):
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"""处理单篇论文数据"""
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category_mapping = extract_category_mapping()
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# 提取基本信息
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paper_id = paper_data.get('id', '')
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title = paper_data.get('title', '').replace('\n', ' ').strip()
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authors = paper_data.get('authors', '')
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abstract = paper_data.get('abstract', '').replace('\n', ' ').strip()
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categories = paper_data.get('categories', '')
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# 检查是否包含多个类别（用空格分隔）
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category_list = categories.split()
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if len(category_list) > 1:
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# 如果有多个类别，category_list中第1个满足category_to_option的类别作为目标类别
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target_category = next((category for category in category_list if category in categorys), None)
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# 先将所有数据加载到内存中（30万条可以接受）
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else:
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target_category = category_list[0] if category_list else ''
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# 检查类别是否在我们的目标列表中
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# if target_category not in category_mapping:
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# if verbose:
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# print(f"跳过非目标类别论文 {paper_id}: {target_category}")
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# return None
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# 获取对应的选项字母
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correct_option = category_mapping[target_category]
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# 构建human问题
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options_text = get_category_options_text()
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human_content = f"Based on the title '{title}', authors '{authors}', and abstract '{abstract}', please determine the scientific category of this paper.\n\n{options_text}"
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# 构建JSONL条目
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jsonl_entry = {
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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": human_content,
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"assistant": correct_option
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}
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]
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}
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if verbose:
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print(f"处理论文 {paper_id}: {target_category} -> {correct_option}")
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return jsonl_entry
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# input_path = "arxiv-metadata-oai-snapshot-single.json"
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# output_path_1 = "arxiv-metadata-oai-snapshot-single-batch1.json"
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# output_path_2 = "arxiv-metadata-oai-snapshot-single-batch2.json"
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# batch1_size_per_category = 400
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# batch2_size_per_category = 600
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input_path = "arxiv-metadata-oai-snapshot-multi.json"
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output_path_1 = "arxiv-metadata-oai-snapshot-multi-batch1.json"
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output_path_2 = "arxiv-metadata-oai-snapshot-multi-batch2.json"
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batch1_size_per_category = 400
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batch2_size_per_category = 400
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# 先将所有数据加载到内存中
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with open(input_path, 'r') as infile:
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data = [json.loads(line) for line in infile]
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print(f"原始数据量：{len(data)} 条")
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## 按类别筛选数据，不是随机
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## 每个类别指定抽取的比例
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# category_proportions = {
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# 'astro-ph': 0.1336,
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# 'cond-mat.mes-hall': 0.0486,
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# 'cond-mat.mtrl-sci': 0.0587,
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# 'cs.CL': 0.085,
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# 'cs.CV': 0.0931,
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# 'cs.LG': 0.0992,
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# 'gr-qc': 0.1174,
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# 'hep-ph': 0.1194,
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# 'hep-th': 0.085,
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# 'quant-ph': 0.1599
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# }
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category_proportions = {
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'quant-ph': 0.1,
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'physics.chem-ph': 0.1,
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'physics.atom-ph': 0.1,
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'cond-mat.soft': 0.1,
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'cs.RO': 0.1,
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'cs.CL': 0.1,
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'cs.SE': 0.1,
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'cs.IR': 0.1,
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'hep-th': 0.1,
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'hep-ph': 0.1,
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'physics.optics': 0.1,
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'cs.AI': 0.1,
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'cs.CV': 0.1,
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'nucl-th': 0.1,
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'astro-ph': 0.1,
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'math.PR': 0.1,
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'cs.OS': 0.1,
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'eess.SP': 0.1,
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'math.OC': 0.1,
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'math.DS': 0.1,
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'math.DG': 0.1,
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'math.MP': 0.1,
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'cs.MM': 0.1,
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'stat.ME': 0.1,
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'math.CO': 0.1,
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'cs.NE': 0.1
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}
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# 存储两个批次的数据
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batch1_data = []
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batch2_data = []
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## print 每个类别的筛选比例和数量
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print("每个类别的筛选比例和数量:")
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for category, proportion in category_proportions.items():
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count = sample_size * proportion
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print(f"类别 {category}: 抽取比例 {proportion}, 数量 {count}")
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# 按每个类别的数量筛选数据
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filtered_data = []
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for category, proportion in category_proportions.items():
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count = int(sample_size * proportion)
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# 按类别处理数据
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for category in categorys:
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# 筛选出当前类别的数据
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category_data = [item for item in data if item.get('categories', '').strip() == category]
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# 如果当前类别的数据量小于需要抽取的数量，则全部取出
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if len(category_data) < count:
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filtered_data.extend(category_data)
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else:
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# 随机抽样指定数量的数据
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sampled_data = random.sample(category_data, count)
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filtered_data.extend(sampled_data)
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print(f"类别 {category}: 抽取数量 {count}")
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category_data = [item for item in data if category in item.get('categories', '').strip().split()]
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print(f"类别 {category}: 总共 {len(category_data)} 条")
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# 打乱数据顺序
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random.shuffle(category_data)
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# 确定第一批和第二批的数量
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total_count = len(category_data)
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batch1_count = min(batch1_size_per_category, total_count)
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batch2_count = min(batch2_size_per_category, total_count - batch1_count)
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# 分配数据到两个批次
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batch1_data.extend(category_data[:batch1_count])
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batch2_data.extend(category_data[batch1_count:batch1_count + batch2_count])
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print(f"类别 {category}: 第一批 {batch1_count} 条, 第二批 {batch2_count} 条")
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# 保存第一批数据
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with open(output_path_1, 'w', encoding='utf-8') as outfile:
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for record in batch1_data:
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swft_js = process_paper(record, verbose=False)
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outfile.write(json.dumps(swft_js, ensure_ascii=False) + '\n')
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# 保存第二批数据
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with open(output_path_2, 'w', encoding='utf-8') as outfile:
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for record in batch2_data:
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swft_js = process_paper(record, verbose=False)
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outfile.write(json.dumps(swft_js, ensure_ascii=False) + '\n')
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# 保存结果
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with open(output_path, 'w') as outfile:
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for record in filtered_data:
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outfile.write(json.dumps(record) + '\n')
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print(f"已按比例抽取 {sample_size} 条数据保存到 {output_path}")
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print(f"第一批数据: {len(batch1_data)} 条，已保存到 {output_path_1}")
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print(f"第二批数据: {len(batch2_data)} 条，已保存到 {output_path_2}")
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@@ -124,6 +124,12 @@ QUESTION_TEMPLATES = [
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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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QUESTION_TEMPLATES = [
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"Based on the title '{title}', authors '{authors}', and abstract '{abstract}', please determine the scientific category of this paper.\n\n{category_text}"
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]
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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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@@ -548,7 +554,7 @@ if __name__ == "__main__":
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output_file_pre = r"G:\\11\data-prepare\\arxiv_papers-multi_type-pre.json"
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paper_datas=get_paper_data_from_crawl_jason(input_file)
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convert_onedata2multi_type_sft(paper_datas, output_file_sft, num_templates=1)
|
||||
convert_onedata2multi_type_pre(paper_datas, output_file_pre, num_templates=1)
|
||||
#convert_onedata2multi_type_pre(paper_datas, output_file_pre, num_templates=1)
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -50,5 +50,5 @@ def get_Composition_ratio(input_file):
|
||||
if __name__ == "__main__":
|
||||
# input_file = "sftdata.jsonl"
|
||||
input_file = "output-26.jsonl"
|
||||
input_file = "arxiv-metadata-oai-snapshot--swift-26.json"
|
||||
input_file = "G:\\11\\data-prepare\\arxiv-metadata-oai-snapshot-multi-batch1.json"
|
||||
get_Composition_ratio(input_file)
|
||||
|
||||
@@ -11,7 +11,7 @@ from sklearn.metrics import (
|
||||
)
|
||||
|
||||
# 配置参数
|
||||
RESULT_FILE = "G:\\11\\data-prepare\\20250720-195839.jsonl" # 替换为你的结果文件路径
|
||||
RESULT_FILE = "G:\\11\\data-prepare\\20250727-084808.jsonl" # 替换为你的结果文件路径
|
||||
OUTPUT_DIR = "G:\\11\\data-prepare\\analysis_results" # 分析结果输出目录
|
||||
EXPORT_CSV = True # 是否导出CSV格式的详细结果
|
||||
PLOT_CONFUSION_MATRIX = True # 是否绘制混淆矩阵
|
||||
|
||||
Reference in New Issue
Block a user