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# -----------------------------------------------------------------------------
# Author: Marina
# Date: 2024-11-15
# -----------------------------------------------------------------------------
''' Script to segment IMO shortlist md files using regex. It takes as input
files in en-shortlist and outputs en-shortlist-seg
To run:
`python segment_script/segment.py`
To debug (or see covered use cases listed in fixtures/):
`pytest test_segment`
'''
from collections import defaultdict
import os
import re
import pandas as pd
import json
base = 'md'
seg_base = 'segmented'
section_re = re.compile(r'##\s+([A-Za-z]\w.*)')
problem_re = re.compile(
r'^(?:##\s*)?((?:[AGNC]\s*\d+))\.*\s*(.*?)(?:\((.*?)\))?$',
re.MULTILINE
)
solution_re = re.compile(
r'^(?:##\s*)?(Solution(?: \d+)?\.)\s*(.*?)(?=(?:Solution|Comment|A\d+|G\d+|N\d+|C\d+|##|$))',
re.MULTILINE | re.DOTALL
)
def add_content(section, label, text_class, text, problems, solutions):
text_str = " ".join(text).strip()
if text_class == "problem":
# print(f"ADD PROBLEM {section} {label} ")
problems.append({"section": section, "label": label, "problem": text_str})
elif text_class == "solution":
# print(f"ADD SOLUTION {section} {label}")
solutions.append({"label": label, "solution": text_str})
def parse(file):
with open(file, 'r') as file:
content = file.read()
problems, solutions = [], []
current_section, current_label, current_class = None, None, None
current_lines = []
for line in content.splitlines():
if match := problem_re.match(line):
label, text, country = match.groups()
label = label.replace(" ", "") # clean the label
add_content(current_section, current_label, current_class, current_lines, problems, solutions)
current_class = "problem"
current_label = label
current_lines = [text]
elif match := solution_re.match(line):
label, text = match.groups()
add_content(current_section, current_label, current_class, current_lines, problems, solutions)
current_class = "solution"
current_lines = [text]
elif match := section_re.match(line):
add_content(current_section, current_label, current_class, current_lines, problems, solutions)
current_class = "section"
text, = match.groups()
current_section = text
else:
current_lines.append(line)
add_content(current_section, current_label, current_class, current_lines, problems, solutions)
problems_df = pd.DataFrame(problems).drop_duplicates(subset=["label", "problem"])
solutions_df = pd.DataFrame(solutions)
return problems_df, solutions_df
def join(problems_df, solutions_df):
pairs_df = problems_df.merge(solutions_df, on=["label"], how="left")
return pairs_df
def add_metadata(pairs_df):
pairs_df.rename(columns={"section": "problem_type", "label": "problem_label"}, inplace=True)
pairs_df['tier'] = 0 # according to omnimath
return pairs_df
def write_pairs(filename, pairs_df):
pairs_df.to_json(filename, orient="records", lines=True)
os.makedirs(seg_base, exist_ok=True)
for name in os.listdir(base):
if "compendium" not in name: # en-compendium is segmented in segment_compendium.py
print(name)
problems, solutions = parse(os.path.join(base, name))
pairs_df = join(problems, solutions)
pairs_df = add_metadata(pairs_df)
print(pairs_df)
basename = os.path.splitext(name)[0]
print(f"{seg_base}/{basename}.jsonl")
write_pairs(f"{seg_base}/{basename}.jsonl", pairs_df) |