| |
| """Rebuild finance topic views, workbook, notes, and bibliography locally. |
| |
| The CSV and structured JSONL are the curated inputs. This command never changes |
| the inclusion decisions or reclassifies existing papers. |
| """ |
| import csv |
| import os |
| import json |
| import re |
| from collections import Counter |
| from pathlib import Path |
|
|
| import openpyxl |
| from openpyxl.styles import Font, PatternFill |
|
|
| from build_indexes import escape, link, table, write_page |
|
|
| REPO = Path(__file__).resolve().parents[1] |
| TRACK = os.environ.get('AI4FINANCE_TRACK', 'main') |
| assert TRACK in {'main', 'findings'} |
| ROOT = REPO / TRACK |
|
|
|
|
| def write_csv(path, rows, columns): |
| path.parent.mkdir(parents=True, exist_ok=True) |
| with path.open('w', encoding='utf-8-sig', newline='') as stream: |
| writer = csv.DictWriter(stream, fieldnames=columns, extrasaction='ignore') |
| writer.writeheader() |
| writer.writerows(rows) |
|
|
|
|
| def sheet(book, name, rows, columns): |
| page = book.create_sheet(name) |
| page.append(columns) |
| for row in rows: |
| page.append([row.get(key, '') for key in columns]) |
| page.freeze_panes = 'A2' |
| page.auto_filter.ref = page.dimensions |
| for cell in page[1]: |
| cell.font = Font(color='FFFFFF', bold=True) |
| cell.fill = PatternFill('solid', fgColor='264653') |
| for i, key in enumerate(columns, 1): |
| page.column_dimensions[openpyxl.utils.get_column_letter(i)].width = ( |
| 65 if key in {'title', 'finance_topics', 'finance_subtopics', 'finance_methods', |
| 'finance_primary_topic', 'topic', 'definition'} else 24) |
| for row in page.iter_rows(min_row=2): |
| for i, cell in enumerate(row): |
| if isinstance(cell.value, str): |
| cell.data_type = 's' |
| if columns[i] in {'paper_url', 'pdf_url', 'local_path'} and cell.value: |
| cell.hyperlink = cell.value |
| cell.font = Font(color='0563C1', underline='single') |
|
|
|
|
| def main(): |
| with (ROOT / 'finance_catalog.csv').open(encoding='utf-8-sig', newline='') as stream: |
| reader = csv.DictReader(stream) |
| columns = reader.fieldnames |
| rows = list(reader) |
| records = [json.loads(line) for line in (ROOT / 'finance_catalog.jsonl').read_text().splitlines()] |
| schema = json.loads((ROOT / 'checks/finance_taxonomy/schema.json').read_text()) |
| assignments = json.loads((ROOT / 'checks/finance_taxonomy/assignments.json').read_text()) |
| assert {r['id'] for r in rows} == {r['id'] for r in records} == set(assignments) |
| excel_columns = [c for c in columns if c not in {'abstract', 'metadata_sources', 'oral_evidence', 'award_evidence'}] |
| book = openpyxl.Workbook() |
| book.remove(book.active) |
| sheet(book, 'Confirmed Finance', rows, excel_columns) |
| topics = [] |
| for topic in schema['topics']: |
| subset = [row for row in rows if topic['id'] in assignments[row['id']]['topic_ids']] |
| core = sum(row['finance_scope'] == 'core' for row in subset) |
| topics.append({'topic_id': topic['id'], 'topic': topic['label'], 'papers': len(subset), |
| 'core': core, 'application': len(subset)-core, |
| 'primary': sum(assignments[row['id']]['primary_topic_id'] == topic['id'] for row in rows), |
| 'definition': topic['description']}) |
| write_csv(ROOT / f"indexes/finance_topics/{topic['id']}.csv", subset, columns) |
| sheet(book, topic['sheet'], subset, excel_columns) |
| page = ROOT / f"indexes/finance_topics/{topic['id']}.md" |
| subtopics = [s for s in schema['subtopics'] if s['parent_id'] == topic['id']] |
| sub_counts = ['| Subtopic | Papers |', '|---|---:|'] |
| for sub in subtopics: |
| count = sum(sub['id'] in assignments[row['id']]['subtopic_ids'] for row in subset) |
| sub_counts.append(f"| {escape(sub['label'])} | {count} |") |
| lines = [f"# {topic['label']}", '', topic['description'], '', |
| f'{len(subset):,} {TRACK.title()} long papers. Topic memberships can overlap.', '', |
| '[All topics](../ai_for_finance.md) · [Classification definitions](../finance_taxonomy.md) · ' |
| f"[CSV]({topic['id']}.csv) · [Excel](../../finance_catalog.xlsx)", '', |
| '\n'.join(sub_counts), '', |
| '| Year | Venue | Paper | Scope | Primary topic | Subtopics | Methods | Resources |', |
| '|---|---|---|---|---|---|---|---|'] |
| for row in sorted(subset, key=lambda r: (-int(r['year']), r['venue'], r['title'].casefold())): |
| note = link(f"indexes/finance_notes/{row['id']}.md", page) |
| resource = f"[PDF]({link(row['local_path'], page)})" if row['local_path'] else f"[Online]({row['paper_url']})" |
| lines.append(f"| {row['year']} | {row['venue']} | [{escape(row['title'])}]({note}) | " |
| f"{row['finance_scope']} | {escape(row['finance_primary_topic'])} | " |
| f"{escape(row['finance_subtopics'])} | {escape(row['finance_methods'])} | {resource} |") |
| page.write_text('\n'.join(lines) + '\n') |
| sheet(book, 'Topics', topics, list(topics[0])) |
| for category, field, title in [('subtopics', 'subtopic_ids', 'Subtopics'), ('methods', 'method_ids', 'Methods')]: |
| stats = [{'id': item['id'], 'label': item['label'], |
| 'papers': sum(item['id'] in a[field] for a in assignments.values())} for item in schema[category]] |
| sheet(book, title, stats, list(stats[0])) |
| coverage = [] |
| for venue in sorted({row['venue'] for row in rows}): |
| for year in sorted({row['year'] for row in rows if row['venue'] == venue}): |
| subset = [row for row in rows if row['venue'] == venue and row['year'] == year] |
| coverage.append({'venue': venue, 'year': year, 'papers': len(subset), |
| 'core': sum(r['finance_scope'] == 'core' for r in subset), |
| 'application': sum(r['finance_scope'] == 'application' for r in subset), |
| 'local_pdfs': sum(r['download_status'] == 'downloaded' for r in subset)}) |
| sheet(book, 'By Venue and Year', coverage, list(coverage[0])) |
| missing = [row for row in rows if row['download_status'] != 'downloaded'] |
| sheet(book, 'Missing PDFs', missing, excel_columns) |
| book.save(ROOT / 'finance_catalog.xlsx') |
| write_csv(ROOT / 'checks/missing_pdfs.csv', missing, |
| ['id', 'title', 'venue', 'year', 'track', 'finance_scope', 'paper_url', 'pdf_url', 'download_status']) |
|
|
| toc = ['# AI for Finance', '', f'{len(rows):,} {TRACK.title()} long papers, organized into 23 overlapping finance topics.', '', |
| '[Home](../README.md) · [Excel](../finance_catalog.xlsx) · [CSV](../finance_catalog.csv) · ' |
| '[BibTeX](../finance_references.bib) · [Classification definitions](finance_taxonomy.md) · ' |
| '[Selection evidence](../checks/README.md)', '', |
| '| Topic | Papers | Core | Application | Primary | Data |', '|---|---:|---:|---:|---:|---|'] |
| for topic in topics: |
| ident = topic['topic_id'] |
| toc.append(f"| [{escape(topic['topic'])}](finance_topics/{ident}.md) | {topic['papers']} | " |
| f"{topic['core']} | {topic['application']} | {topic['primary']} | [CSV](finance_topics/{ident}.csv) |") |
| toc.extend(['', 'Each paper has one primary finance topic. Technical methods are separate labels.', '']) |
| (ROOT / 'indexes/ai_for_finance.md').write_text('\n'.join(toc)) |
| write_page('indexes/financial_time_series.md', 'Financial time series', |
| [r for r in rows if 'time_series' in assignments[r['id']]['topic_ids']]) |
|
|
| bib = [] |
| for record in records: |
| key = re.sub('[^A-Za-z0-9_-]', '_', record['id']) |
| title = record['title'].replace('&', r'\&').replace('%', r'\%').replace('#', r'\#') |
| entry = f'@inproceedings{{{key},\n title = {{{{{title}}}}},\n' |
| entry += ' author = {' + ' and '.join(record['authors']) + '},\n' |
| entry += ' booktitle = {' + record.get('booktitle', record['venue'] + ' main conference') + '},\n' |
| entry += f" year = {{{record['year']}}},\n url = {{{record['paper_url']}}}" |
| if record.get('doi'): |
| entry += f",\n doi = {{{record['doi']}}}" |
| bib.append(entry + '\n}\n') |
| (ROOT / 'finance_references.bib').write_text('\n'.join(bib)) |
| print(f'Rebuilt topic pages, CSVs, workbook, and bibliography for {len(rows):,} papers.') |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
