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"""Script to segment IMO shortlist md files using regex. |
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To run: |
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`python segment_script/segment.py` |
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To debug (or see covered use cases listed in fixtures/): |
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`pytest test_segment` |
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""" |
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from collections import defaultdict |
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import os |
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from pathlib import Path |
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import re |
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import pandas as pd |
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import json |
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section_re = re.compile(r"##\s+([A-Za-z]\w.*)") |
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problem_re = re.compile( |
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r"^(?:##\s*)?((?:[AGNC]\s*\d+))\.*\s*(.*?)(?:\((.*?)\))?$", re.MULTILINE |
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) |
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solution_re = re.compile( |
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r"^(?:##\s*)?(Solution(?: \d+)?\.)\s*(.*?)(?=(?:Solution|Comment|A\d+|G\d+|N\d+|C\d+|##|$))", |
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re.MULTILINE | re.DOTALL, |
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) |
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def add_content(section, label, text_class, text, problems, solutions): |
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text_str = " ".join(text).strip() |
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if text_class == "problem": |
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problems.append({"section": section, "label": label, "problem": text_str}) |
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elif text_class == "solution": |
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solutions.append({"label": label, "solution": text_str}) |
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def parse(file: Path): |
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content = file.read_text(encoding="utf-8") |
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problems, solutions = [], [] |
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current_section, current_label, current_class = None, None, None |
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current_lines = [] |
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for line in content.splitlines(): |
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if match := problem_re.match(line): |
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label, text, country = match.groups() |
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label = label.replace(" ", "") |
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add_content( |
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current_section, |
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current_label, |
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current_class, |
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current_lines, |
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problems, |
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solutions, |
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) |
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current_class = "problem" |
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current_label = label |
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current_lines = [text] |
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elif match := solution_re.match(line): |
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label, text = match.groups() |
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add_content( |
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current_section, |
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current_label, |
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current_class, |
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current_lines, |
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problems, |
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solutions, |
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) |
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current_class = "solution" |
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current_lines = [text] |
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elif match := section_re.match(line): |
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add_content( |
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current_section, |
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current_label, |
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current_class, |
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current_lines, |
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problems, |
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solutions, |
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) |
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current_class = "section" |
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(text,) = match.groups() |
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current_section = text |
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else: |
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current_lines.append(line) |
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add_content( |
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current_section, |
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current_label, |
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current_class, |
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current_lines, |
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problems, |
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solutions, |
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) |
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problems_df = pd.DataFrame(problems).drop_duplicates(subset=["label", "problem"]) |
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solutions_df = pd.DataFrame(solutions) |
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return problems_df, solutions_df |
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def join(problems_df, solutions_df): |
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pairs_df = problems_df.merge(solutions_df, on=["label"], how="left") |
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return pairs_df |
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def add_metadata(pairs_df, year, resource_path): |
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pairs_df.rename( |
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columns={"section": "problem_type", "label": "problem_label"}, inplace=True |
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) |
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pairs_df["year"] = year |
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pairs_df["tier"] = "T0" |
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pairs_df["exam"] = ["IMO-SL"] * len(pairs_df) |
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pairs_df["metadata"] = [{"resource_path": resource_path}] * len(pairs_df) |
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return pairs_df[ |
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[ |
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"year", |
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"tier", |
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"problem_label", |
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"problem_type", |
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"exam", |
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"problem", |
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"solution", |
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"metadata", |
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] |
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] |
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def write_pairs(file_path, pairs_df): |
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pairs_df = pairs_df.replace({pd.NA: None, pd.NaT: None, float("nan"): None}) |
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pairs_dict = pairs_df.to_dict(orient="records") |
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output_text = "" |
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for pair in pairs_dict: |
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output_text += json.dumps(pair, ensure_ascii=False) + "\n" |
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file_path.write_text(output_text, encoding="utf-8") |
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if __name__ == "__main__": |
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project_root = Path(__file__).parent.parent.parent |
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compet_base_path = Path(__file__).resolve().parent.parent |
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compet_md_path = compet_base_path / "md" |
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seg_output_path = compet_base_path / "segmented" |
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for md_file in compet_md_path.glob("**/*.md"): |
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if "compendium" not in md_file.name: |
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year = re.search(r"(\d{4})", md_file.name).group(1) |
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output_file = seg_output_path / md_file.relative_to( |
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compet_md_path |
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).with_suffix(".jsonl") |
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output_file.parent.mkdir(parents=True, exist_ok=True) |
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print(md_file) |
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problems, solutions = parse(md_file) |
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pairs_df = join(problems, solutions) |
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pairs_df = add_metadata( |
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pairs_df, year, output_file.relative_to(project_root).as_posix() |
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) |
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print(pairs_df) |
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write_pairs(output_file, pairs_df) |
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