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''' Script to segment IMO shortlist md files using regex. It takes as input |
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files in en-shortlist and outputs en-shortlist-seg |
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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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import re |
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import pandas as pd |
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import json |
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base = 'md' |
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seg_base = 'segmented' |
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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*(.*?)(?:\((.*?)\))?$', |
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re.MULTILINE |
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) |
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solution_re = re.compile( |
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r'^(?:##\s*)?(Solution(?: \d+)?\.)\s*(.*?)(?=(?:Solution|Comment|A\d+|G\d+|N\d+|C\d+|##|$))', |
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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): |
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with open(file, 'r') as file: |
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content = file.read() |
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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(current_section, current_label, current_class, current_lines, problems, solutions) |
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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(current_section, current_label, current_class, current_lines, problems, solutions) |
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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(current_section, current_label, current_class, current_lines, problems, solutions) |
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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(current_section, current_label, current_class, current_lines, problems, solutions) |
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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): |
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pairs_df.rename(columns={"section": "problem_type", "label": "problem_label"}, inplace=True) |
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pairs_df['tier'] = 0 |
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return pairs_df |
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def write_pairs(filename, pairs_df): |
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pairs_df.to_json(filename, orient="records", lines=True) |
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os.makedirs(seg_base, exist_ok=True) |
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for name in os.listdir(base): |
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if "compendium" not in name: |
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print(name) |
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problems, solutions = parse(os.path.join(base, name)) |
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pairs_df = join(problems, solutions) |
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pairs_df = add_metadata(pairs_df) |
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print(pairs_df) |
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basename = os.path.splitext(name)[0] |
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print(f"{seg_base}/{basename}.jsonl") |
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write_pairs(f"{seg_base}/{basename}.jsonl", pairs_df) |
