| """Deterministic source/task-balanced training pilot; no evaluation examples.""" |
| import argparse,json |
| from pathlib import Path |
| import duckdb,pyarrow as pa |
| from assemble import allocate |
|
|
| def main(): |
| p=argparse.ArgumentParser();p.add_argument('--root',required=True);p.add_argument('--output',required=True);p.add_argument('--rows',type=int,default=1000);a=p.parse_args() |
| db=duckdb.connect();db.execute("SET memory_limit='16GB'") |
| pattern=str(Path(a.root)/'selected'/'part-*.parquet') |
| db.execute("CREATE TABLE pool AS SELECT *,provenance.repository AS source FROM read_parquet(?) WHERE split='train' AND array_length(options) BETWEEN 2 AND 20",[pattern]) |
| available=dict(db.execute('SELECT source,count(*) FROM pool GROUP BY source').fetchall()) |
| quotas=allocate(available,a.rows,{k:1 for k in available});caps=[] |
| for source,quota in quotas.items(): |
| strata=dict(db.execute('SELECT task,count(*) FROM pool WHERE source=? GROUP BY task',[source]).fetchall()) |
| for task,cap in allocate(strata,quota,{k:1 for k in strata}).items():caps.append({'source':source,'task':task,'cap':cap}) |
| db.register('caps',pa.Table.from_pylist(caps)) |
| db.execute('CREATE TABLE picked AS SELECT p.* FROM pool p JOIN caps c USING(source,task) QUALIFY row_number() OVER(PARTITION BY source,task ORDER BY sha256(id))<=c.cap') |
| |
| table=db.execute('SELECT * EXCLUDE(source) FROM picked QUALIFY true ORDER BY row_number() OVER(PARTITION BY source ORDER BY sha256(id)),source').fetch_arrow_table() |
| with open(a.output,'w') as f: |
| for row in table.to_pylist():f.write(json.dumps(row,ensure_ascii=False)+'\n') |
| print(json.dumps({'rows':len(table),'sources':quotas,'split':'train','max_options':20}),flush=True) |
|
|
| if __name__=='__main__':main() |
|
|
