IMO/segment_script/segment.py

# -----------------------------------------------------------------------------
# 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)