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Commit f14e50e2 authored by 赵小蒙's avatar 赵小蒙
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remove some code

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tools/benchmark.py deleted 100644 → 0
View file @ fb27361e
import zipfile
import os
import shutil
import json
import markdown_calculate
code_path = os.environ.get('GITHUB_WORKSPACE')
#数据集存放路径
pdf_dev_path = "/share/quyuan/mineru/data/"
#magicpdf最终结果
pdf_res_path = "/share/quyuan/mineru/data/mineru"
file_types = ["academic_literature", "atlas", "courseware", "colorful_textbook", "historical_documents", "notes", "ordinary_books", "ordinary_exam_paper", "ordinary_textbook", "research_report", "special_exam_paper"]
def test_cli():
#magicpdf模型输出结果
magicpdf_path = os.path.join(pdf_dev_path, "output")
rm_cmd = "rm -rf %s" % (pdf_res_path)
os.system(rm_cmd)
os.makedirs(pdf_res_path)
cmd = 'cd %s && export PYTHONPATH=. && find %s -type f -name "*.pdf" | xargs -I{} python magic_pdf/cli/magicpdf.py pdf-command --pdf {}' % (code_path, magicpdf_path)
os.system(cmd)
for root, dirs, files in os.walk(pdf_res_path):
for magic_file in files:
for file_type in file_types:
target_dir = os.path.join(pdf_dev_path, "ci", file_type, "magicpdf")
if magic_file.endswith(".md") and magic_file.startswith(file_type):
source_file = os.path.join(root, magic_file)
target_file = os.path.join(pdf_dev_path, "ci", file_type, "magicpdf", magic_file)
if not os.path.exists(target_dir):
os.makedirs(target_dir)
shutil.copy(source_file, target_file)
def calculate_score():
data_path = os.path.join(pdf_dev_path, "ci")
cmd = "cd %s && export PYTHONPATH=. && python tools/clean_photo.py --tool_name annotations --download_dir %s" % (code_path, data_path)
os.system(cmd)
cmd = "cd %s && export PYTHONPATH=. && python tools/clean_photo.py --tool_name magicpdf --download_dir %s" % (code_path, data_path)
os.system(cmd)
score = markdown_calculate.Scoring(os.path.join(data_path, "result.json"))
score.calculate_similarity_total("magicpdf", file_types, data_path)
res = score.summary_scores()
return res
def extrat_zip(zip_file_path, extract_to_path):
if zipfile.is_zipfile(zip_file_path):
with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
zip_ref.extractall(extract_to_path)
print(f'Files extracted to {extract_to_path}')
else:
print(f'{zip_file_path} is not a zip file')
def ci_ben():
fr = open(os.path.join(pdf_dev_path, "ci", "result.json"), "r")
lines = fr.readlines()
last_line = lines[-1].strip()
last_score = json.loads(last_line)
print ("last_score:", last_score)
last_simscore = last_score["average_sim_score"]
last_editdistance = last_score["average_edit_distance"]
last_bleu = last_score["average_bleu_score"]
extrat_zip(os.path.join(pdf_dev_path, 'output.zip'), os.path.join(pdf_dev_path))
test_cli()
now_score = calculate_score()
print ("now_score:", now_score)
now_simscore = now_score["average_sim_score"]
now_editdistance = now_score["average_edit_distance"]
now_bleu = now_score["average_bleu_score"]
assert last_simscore <= now_simscore
assert last_editdistance <= now_editdistance
assert last_bleu <= now_bleu
if __name__ == "__main__":
ci_ben()
tools/clean_photo.py deleted 100644 → 0
View file @ fb27361e
import pypandoc
import re
import htmltabletomd
import os
import argparse
import zipfile
parser = argparse.ArgumentParser(description="get tool type")
parser.add_argument(
"--tool_name",
type=str,
required=True,
help="input tool name",
)
parser.add_argument(
"--download_dir",
type=str,
required=True,
help="input download dir",
)
args = parser.parse_args()
def clean_markdown_images(content):
pattern = re.compile(r'!\[[^\]]*\]\([^)]*\)', re.IGNORECASE)
cleaned_content = pattern.sub('', content)
return cleaned_content
def clean_ocrmath_photo(content):
pattern = re.compile(r'\\includegraphics\[.*?\]\{.*?\}', re.IGNORECASE)
cleaned_content = pattern.sub('', content)
return cleaned_content
def convert_html_table_to_md(html_table):
lines = html_table.strip().split('\n')
md_table = ''
if lines and '<tr>' in lines[0]:
in_thead = True
for line in lines:
if '<th>' in line:
cells = re.findall(r'<th>(.*?)</th>', line)
md_table += '| ' + ' | '.join(cells) + ' |\n'
in_thead = False
elif '<td>' in line and not in_thead:
cells = re.findall(r'<td>(.*?)</td>', line)
md_table += '| ' + ' | '.join(cells) + ' |\n'
md_table = md_table.rstrip() + '\n'
return md_table
def convert_latext_to_md(content):
tables = re.findall(r'\\begin\{tabular\}(.*?)\\end\{tabular\}', content, re.DOTALL)
placeholders = []
for table in tables:
placeholder = f"<!-- TABLE_PLACEHOLDER_{len(placeholders)} -->"
replace_str = f"\\begin{{tabular}}{table}cl\\end{{tabular}}"
content = content.replace(replace_str, placeholder)
try:
pypandoc.convert_text(replace_str, format="latex", to="md", outputfile="output.md", encoding="utf-8")
except:
markdown_string = replace_str
else:
markdown_string = open('output.md', 'r', encoding='utf-8').read()
placeholders.append((placeholder, markdown_string))
new_content = content
for placeholder, md_table in placeholders:
new_content = new_content.replace(placeholder, md_table)
# 写入文件
return new_content
def convert_htmltale_to_md(content):
tables = re.findall(r'<table>(.*?)</table>', content, re.DOTALL)
placeholders = []
for table in tables:
placeholder = f"<!-- TABLE_PLACEHOLDER_{len(placeholders)} -->"
content = content.replace(f"<table>{table}</table>", placeholder)
try:
convert_table = htmltabletomd.convert_table(table)
except:
convert_table = table
placeholders.append((placeholder,convert_table))
new_content = content
for placeholder, md_table in placeholders:
new_content = new_content.replace(placeholder, md_table)
# 写入文件
return new_content
def clean_data(prod_type, download_dir):
file_type = ["academic_literature", "atlas", "courseware", "colorful_textbook", "historical_documents", "notes", "ordinary_books", "ordinary_exam_paper", "ordinary_textbook", "research_report", "special_exam_paper"]
for filetype in file_type:
tgt_dir = os.path.join(download_dir, filetype, prod_type, "cleaned")
if not os.path.exists(tgt_dir):
os.makedirs(tgt_dir)
source_dir = os.path.join(download_dir, filetype, prod_type)
filenames = os.listdir(source_dir)
for filename in filenames:
if filename.endswith('.md'):
input_file = os.path.join(source_dir, filename)
output_file = os.path.join(tgt_dir, "cleaned_" + filename)
with open(input_file, 'r', encoding='utf-8') as fr:
content = fr.read()
new_content = convert_htmltale_to_md(content)
new_content = clean_markdown_images(new_content)
new_content = clean_ocrmath_photo(new_content)
new_content = convert_latext_to_md(new_content)
with open(output_file, 'w', encoding='utf-8') as fw:
fw.write(new_content)
if __name__ == '__main__':
tool_type = args.tool_name
download_dir = args.download_dir
clean_data(tool_type, download_dir)
tools/markdown_calculate.py deleted 100644 → 0
View file @ fb27361e
import os
from Levenshtein import distance
from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction, corpus_bleu
from nltk.tokenize import word_tokenize
import json
import re
import scoring
import argparse
import nltk
nltk.download('punkt')
# 初始化列表来存储编辑距离和BLEU分数
class Scoring:
def __init__(self, result_path):
self.edit_distances = []
self.bleu_scores = []
self.sim_scores = []
self.filenames = []
self.score_dict = {}
self.anntion_cnt = 0
self.fw = open(result_path, "w+")
def simple_bleu_score(self, candidate, reference):
candidate_tokens = word_tokenize(candidate)
reference_tokens = word_tokenize(reference)
return sentence_bleu([reference_tokens], candidate_tokens, smoothing_function=SmoothingFunction().method1)
def preprocess_string(self, s):
sub_enter = re.sub(r'\n+', '\n', s)
return re.sub(r' ', ' ', sub_enter)
def calculate_similarity(self, annotion, actual, tool_type):
class_dict = {}
edit_distances = []
bleu_scores = []
sim_scores = list()
total_file = 0
for filename in os.listdir(annotion):
if filename.endswith('.md') and not filename.startswith('.'): # 忽略隐藏文件
total_file = total_file + 1
# 读取A目录中的文件
with open(os.path.join(annotion, filename), 'r', encoding='utf-8') as file_a:
content_a = file_a.read()
self.anntion_cnt = self.anntion_cnt + 1
filepath_b = os.path.join(actual, filename)
if os.path.exists(filepath_b):
with open(filepath_b, 'r', encoding='utf-8') as file_b:
content_b = file_b.read()
self.filenames.append(filename)
# 计算编辑距离
edit_dist = distance(self.preprocess_string(content_b),self.preprocess_string(content_a)) / max(len(content_a), len(content_b))
self.edit_distances.append(edit_dist)
edit_distances.append(edit_dist)
#计算BLUE分数
bleu_score = self.simple_bleu_score(content_b, content_a)
bleu_scores.append(bleu_score)
self.bleu_scores.append(bleu_score)
#计算marker分数
score = scoring.score_text(content_b, content_a)
sim_scores.append(score)
self.sim_scores.append(score)
class_dict[filename] = {"edit_dist": edit_dist, "bleu_score": bleu_score, "sim_score": score}
self.score_dict[filename] = {"edit_dist": edit_dist, "bleu_score": bleu_score, "sim_score": score}
else:
print(f"File {filename} not found in actual directory.")
# 计算每类平均值
class_average_edit_distance = sum(edit_distances) / len(edit_distances) if edit_distances else 0
class_average_bleu_score = sum(bleu_scores) / len(bleu_scores) if bleu_scores else 0
class_average_sim_score = sum(sim_scores) / len(sim_scores) if sim_scores else 0
self.fw.write(json.dumps(class_dict, ensure_ascii=False) + "\n")
ratio = len(class_dict)/total_file
self.fw.write(f"{tool_type} extract ratio: {ratio}" + "\n")
self.fw.write(f"{tool_type} Average Levenshtein Distance: {class_average_edit_distance}" + "\n")
self.fw.write(f"{tool_type} Average BLEU Score: {class_average_bleu_score}" + "\n")
self.fw.write(f"{tool_type} Average Sim Score: {class_average_sim_score}" + "\n")
print (f"{tool_type} extract ratio: {ratio}")
print (f"{tool_type} Average Levenshtein Distance: {class_average_edit_distance}")
print (f"{tool_type} Average BLEU Score: {class_average_bleu_score}")
print (f"{tool_type} Average Sim Score: {class_average_sim_score}")
return self.score_dict
def summary_scores(self):
# 计算整体平均值
over_all_dict = dict()
average_edit_distance = sum(self.edit_distances) / len(self.edit_distances) if self.edit_distances else 0
average_bleu_score = sum(self.bleu_scores) / len(self.bleu_scores) if self.bleu_scores else 0
average_sim_score = sum(self.sim_scores) / len(self.sim_scores) if self.sim_scores else 0
over_all_dict["average_edit_distance"] = average_edit_distance
over_all_dict["average_bleu_score"] = average_bleu_score
over_all_dict["average_sim_score"] = average_sim_score
self.fw.write(json.dumps(over_all_dict, ensure_ascii=False) + "\n")
return over_all_dict
def calculate_similarity_total(self, tool_type, file_types, download_dir):
for file_type in file_types:
annotion = os.path.join(download_dir, file_type, "annotations", "cleaned")
actual = os.path.join(download_dir, file_type, tool_type, "cleaned")
self.calculate_similarity(annotion, actual, file_type)
tools/scoring.py deleted 100644 → 0
View file @ fb27361e
import math
from rapidfuzz import fuzz
import re
import regex
from statistics import mean
CHUNK_MIN_CHARS = 25
def chunk_text(text, chunk_len=500):
chunks = [text[i:i+chunk_len] for i in range(0, len(text), chunk_len)]
chunks = [c for c in chunks if c.strip() and len(c) > CHUNK_MIN_CHARS]
return chunks
def overlap_score(hypothesis_chunks, reference_chunks):
if len(reference_chunks) > 0:
length_modifier = len(hypothesis_chunks) / len(reference_chunks)
else:
length_modifier = 0
search_distance = max(len(reference_chunks) // 5, 10)
chunk_scores = []
for i, hyp_chunk in enumerate(hypothesis_chunks):
max_score = 0
total_len = 0
i_offset = int(i * length_modifier)
chunk_range = range(max(0, i_offset-search_distance), min(len(reference_chunks), i_offset+search_distance))
for j in chunk_range:
ref_chunk = reference_chunks[j]
score = fuzz.ratio(hyp_chunk, ref_chunk, score_cutoff=30) / 100
if score > max_score:
max_score = score
total_len = len(ref_chunk)
chunk_scores.append(max_score)
return chunk_scores
def score_text(hypothesis, reference):
# Returns a 0-1 alignment score
hypothesis_chunks = chunk_text(hypothesis)
reference_chunks = chunk_text(reference)
chunk_scores = overlap_score(hypothesis_chunks, reference_chunks)
if len(chunk_scores) > 0:
mean_score = mean(chunk_scores)
return mean_score
else:
return 0
#return mean(chunk_scores)
\ No newline at end of file
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