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"""Script to segment IMO shortlist md files using regex. It takes as input |
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the file en-compendium.md in en-shortlist and outputs the segmentation |
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(problem/solution pairs) in en-shortlist-seg |
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To run: |
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`python segment_compendium.py` |
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To debug (or see covered use cases by regex): |
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`pytest test_segment_compendium` |
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""" |
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import json |
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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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base = "en-shortlist" |
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seg_base = "en-shortlist-seg" |
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basename = "en-compendium" |
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level1_re = re.compile(r"^##\s+(Problems|Solutions|Notation and Abbreviations)$") |
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year_re = re.compile(r"^[^=]*,\s+(\d{4})\s*$") |
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problem_section_re = re.compile(r"^###\s+(\d+\.\d+\.\d+)\s+(.+)$") |
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solution_section_re = re.compile(r"^###\s+(\d+\.\d+)\s+([\w\s]+)\s+(\d{4})$") |
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problem_or_solution_re = re.compile(r"^(?:\[.*?\])?\s*(\d+)\s*\.\s*(.+)$") |
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def add_content(current_dict): |
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required_keys = ["year", "category", "section_label", "label", "lines"] |
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if not all(current_dict[key] for key in required_keys): |
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return |
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text_str = " ".join(current_dict["lines"]).strip() |
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entry = { |
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"year": current_dict["year"], |
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"category": current_dict["category"], |
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"section": current_dict["section_label"], |
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"label": current_dict["label"], |
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} |
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if current_dict["class"] == "problem": |
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entry["problem"] = text_str |
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current_dict["problems"].append(entry) |
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elif current_dict["class"] == "solution": |
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entry["solution"] = text_str |
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current_dict["solutions"].append(entry) |
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def get_category(s: str): |
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cat = None |
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if "contest" in s.lower(): |
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cat = "contest" |
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elif "shortlisted" in s.lower(): |
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cat = "shortlisted" |
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elif "longlisted" in s.lower(): |
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cat = "longlisted" |
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return cat |
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def get_matching_section_label(s: str): |
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""" |
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extracts the section number to be used a a join key to pair a problem and solution |
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for problems: 3.44.1 -> 44 |
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for solutions: 4.20 -> 20 |
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""" |
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return s.split(".")[1] |
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def parse(file): |
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with open(file, "r", encoding="utf-8") as file: |
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content = file.read() |
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current = { |
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"year": None, |
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"category": None, |
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"section_label": None, |
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"label": None, |
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"class": None, |
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"lines": [], |
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"problems": [], |
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"solutions": [], |
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} |
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for line in content.splitlines(): |
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if match := level1_re.match(line): |
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add_content(current) |
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(title,) = match.groups() |
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current["class"] = { |
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"Problems": "problem", |
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"Solutions": "solution", |
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}.get(title, "other") |
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current["lines"] = [] |
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elif match := year_re.match(line): |
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add_content(current) |
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current["year"] = match.group(1) |
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current["lines"] = [] |
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elif match := problem_section_re.match(line): |
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add_content(current) |
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number, title = match.groups() |
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current["section_label"] = get_matching_section_label(number) |
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current["category"] = get_category(title) |
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current["lines"] = [] |
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elif match := solution_section_re.match(line): |
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add_content(current) |
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number, title, year = match.groups() |
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current["section_label"] = get_matching_section_label(number) |
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current["category"] = get_category(title) |
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current["year"] = year |
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current["lines"] = [] |
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elif match := problem_or_solution_re.match(line): |
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add_content(current) |
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current["label"] = match.group(1) |
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current["lines"] = [line] |
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else: |
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if current["lines"]: |
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current["lines"].append(line) |
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problems_df = pd.DataFrame(current["problems"]) |
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solutions_df = pd.DataFrame(current["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( |
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solutions_df, on=["year", "category", "section", "label"], how="outer" |
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) |
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return pairs_df |
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def add_metadata(pairs_df, resource_path): |
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problem_type_mapping = { |
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"A": "Algebra", |
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"C": "Combinatorics", |
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"G": "Geometry", |
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"N": "Number Theory", |
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} |
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pairs_df["problem_type"] = pairs_df["problem"].str.extract(r"^\d+\.\s*([ACGN])\d*")[ |
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0 |
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] |
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pairs_df["problem_type"] = pairs_df["problem_type"].map(problem_type_mapping) |
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pairs_df["tier"] = "T0" |
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pairs_df["exam"] = "IMO" |
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pairs_df["metadata"] = [{"resource_path": resource_path}] * len(pairs_df) |
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pairs_df.rename( |
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columns={"category": "problem_phase", "label": "problem_label"}, |
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inplace=True, |
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) |
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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" in md_file.name: |
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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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problems, solutions = parse(md_file) |
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pairs_df = join(problems, solutions) |
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pairs_df = pairs_df[pairs_df.notnull().all(axis=1)] |
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pairs_df = add_metadata( |
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pairs_df, output_file.relative_to(project_root).as_posix() |
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) |
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write_pairs(output_file, pairs_df) |
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