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"""Build a training-oriented source mix from existing Polish DynaWord parquets.
The raw corpus is provenance-first. This script creates a training view with:
- temperature/sqrt sampling by source,
- a hard cap for legal/parliamentary sources,
- an optional high-quality final-phase manifest,
- optional sampled parquet materialization.
"""
from __future__ import annotations
import argparse
import json
import math
import random
from pathlib import Path
import pyarrow as pa
import pyarrow.compute as pc
import pyarrow.parquet as pq
def load_config(path: Path) -> dict:
return json.loads(path.read_text(encoding="utf-8"))
def source_inventory(data_root: Path) -> dict[str, dict]:
inventory = {}
for parquet_path in sorted((data_root / "data").glob("*/*.parquet")):
source = parquet_path.parent.name
stats_path = parquet_path.with_name(f"{source}.stats.json")
docs = None
tokens = None
if stats_path.exists():
stats = json.loads(stats_path.read_text(encoding="utf-8"))
docs = int(stats.get("kept", 0))
tokens = int(stats.get("tokens", 0))
if not docs or not tokens:
pf = pq.ParquetFile(parquet_path)
docs = pf.metadata.num_rows
tokens = 0
for rg in range(pf.num_row_groups):
tbl = pf.read_row_group(rg, columns=["token_count"])
tokens += int(pc.sum(tbl["token_count"]).as_py())
inventory[source] = {
"path": str(parquet_path),
"docs": docs,
"tokens": tokens,
}
return inventory
def normalize(weights: dict[str, float], total: float = 1.0) -> dict[str, float]:
denom = sum(weights.values())
if denom <= 0:
return {k: 0.0 for k in weights}
return {k: v / denom * total for k, v in weights.items()}
def compute_mix(inventory: dict[str, dict], config: dict) -> dict[str, dict]:
alpha = float(config.get("temperature_alpha", 0.5))
multipliers = config.get("source_multipliers", {})
legal_sources = set(config.get("legal_sources", []))
legal_cap = float(config.get("legal_cap_share", 0.15))
base_weights = {}
for source, meta in inventory.items():
multiplier = float(multipliers.get(source, 1.0))
base_weights[source] = math.pow(meta["tokens"], alpha) * multiplier
legal_weights = {s: w for s, w in base_weights.items() if s in legal_sources}
other_weights = {s: w for s, w in base_weights.items() if s not in legal_sources}
raw_share = normalize(base_weights)
raw_legal_share = sum(raw_share.get(s, 0.0) for s in legal_sources)
if raw_legal_share > legal_cap and other_weights:
legal_share = legal_cap
else:
legal_share = raw_legal_share
other_share = max(0.0, 1.0 - legal_share)
final_shares = {}
final_shares.update(normalize(legal_weights, legal_share))
final_shares.update(normalize(other_weights, other_share))
mix = {}
total_tokens = sum(meta["tokens"] for meta in inventory.values())
for source, meta in inventory.items():
raw_corpus_share = meta["tokens"] / total_tokens if total_tokens else 0.0
target_share = final_shares.get(source, 0.0)
mix[source] = {
**meta,
"raw_corpus_share": raw_corpus_share,
"target_share": target_share,
"sampling_multiplier": target_share / raw_corpus_share if raw_corpus_share else 0.0,
"is_legal_capped": source in legal_sources,
}
return dict(sorted(mix.items()))
def add_token_targets(mix: dict[str, dict], token_budget: int | None) -> None:
for meta in mix.values():
target_tokens = int(round(meta["target_share"] * token_budget)) if token_budget else 0
meta["target_tokens"] = target_tokens
meta["sampling_probability"] = min(1.0, target_tokens / meta["tokens"]) if token_budget else 0.0
def write_report(mix: dict[str, dict], config: dict, out_path: Path, token_budget: int | None) -> None:
legal_sources = set(config.get("legal_sources", []))
raw_legal = sum(v["raw_corpus_share"] for k, v in mix.items() if k in legal_sources)
target_legal = sum(v["target_share"] for k, v in mix.items() if k in legal_sources)
lines = [
"# Polish DynaWord training mix",
"",
f"- config: `{config.get('name', 'unnamed')}`",
f"- temperature alpha: `{config.get('temperature_alpha', 0.5)}`",
f"- legal/parliamentary raw share: `{raw_legal * 100:.2f}%`",
f"- legal/parliamentary target share: `{target_legal * 100:.2f}%`",
]
if token_budget:
lines.append(f"- token budget: `{token_budget:,}`")
lines.extend([
"",
"| source | raw tokens | raw share | target share | sampling multiplier | target tokens |",
"|---|---:|---:|---:|---:|---:|",
])
for source, meta in mix.items():
lines.append(
f"| `{source}` | {meta['tokens']:,} | {meta['raw_corpus_share'] * 100:.2f}% | "
f"{meta['target_share'] * 100:.2f}% | {meta['sampling_multiplier']:.3f} | "
f"{meta['target_tokens']:,} |"
)
final_phase_sources = config.get("final_phase_sources", [])
final_phase_share = float(config.get("final_phase_share", 0.10))
lines.extend([
"",
"## Final training phase",
"",
f"Reserve the last `{final_phase_share * 100:.0f}%` of training tokens for higher-quality sources:",
"",
])
for source in final_phase_sources:
lines.append(f"- `{source}`")
missing = config.get("target_missing_sources", [])
if missing:
lines.extend(["", "## Missing source classes for v0.3+", ""])
for item in missing:
lines.append(f"- {item}")
out_path.parent.mkdir(parents=True, exist_ok=True)
out_path.write_text("\n".join(lines) + "\n", encoding="utf-8")
def write_manifest(mix: dict[str, dict], out_path: Path) -> None:
payload = {
"sources": [
{
"source": source,
"path": meta["path"],
"tokens": meta["tokens"],
"raw_corpus_share": meta["raw_corpus_share"],
"target_share": meta["target_share"],
"target_tokens": meta["target_tokens"],
"sampling_probability": meta["sampling_probability"],
}
for source, meta in mix.items()
]
}
out_path.parent.mkdir(parents=True, exist_ok=True)
out_path.write_text(json.dumps(payload, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
def sample_source(source: str, meta: dict, seed: int) -> pa.Table:
rng = random.Random(f"{seed}:{source}")
target = meta["target_tokens"]
if target <= 0:
return pa.table({})
pf = pq.ParquetFile(meta["path"])
batches = []
sampled_tokens = 0
probability = meta["sampling_probability"]
for rg in range(pf.num_row_groups):
tbl = pf.read_row_group(rg)
keep = [rng.random() < probability for _ in range(tbl.num_rows)]
if not any(keep):
continue
sampled = tbl.filter(pa.array(keep))
batches.append(sampled)
sampled_tokens += int(pc.sum(sampled["token_count"]).as_py())
if sampled_tokens >= target:
break
return pa.concat_tables(batches, promote_options="default") if batches else pa.table({})
def write_sampled_parquet(mix: dict[str, dict], out_path: Path, seed: int) -> None:
out_path.parent.mkdir(parents=True, exist_ok=True)
writer = None
try:
for source, meta in mix.items():
tbl = sample_source(source, meta, seed)
if tbl.num_rows == 0:
continue
if writer is None:
writer = pq.ParquetWriter(out_path, tbl.schema, compression="zstd")
writer.write_table(tbl)
print(f"{source}: wrote {tbl.num_rows:,} docs")
finally:
if writer is not None:
writer.close()
def parse_args() -> argparse.Namespace:
ap = argparse.ArgumentParser()
ap.add_argument("--data-root", type=Path, default=Path("."))
ap.add_argument("--config", type=Path, default=Path("configs/training_mix_v0_3.json"))
ap.add_argument("--token-budget", type=int, default=None)
ap.add_argument("--out-report", type=Path, default=Path("artifacts/training_mix_v0_3.md"))
ap.add_argument("--out-manifest", type=Path, default=Path("artifacts/training_mix_v0_3.json"))
ap.add_argument("--write-parquet", type=Path, default=None)
ap.add_argument("--seed", type=int, default=13)
return ap.parse_args()
def main() -> None:
args = parse_args()
config = load_config(args.config)
inventory = source_inventory(args.data_root)
mix = compute_mix(inventory, config)
add_token_targets(mix, args.token_budget)
write_report(mix, config, args.out_report, args.token_budget)
write_manifest(mix, args.out_manifest)
if args.write_parquet:
if not args.token_budget:
raise SystemExit("--write-parquet requires --token-budget")
write_sampled_parquet(mix, args.write_parquet, args.seed)
print(f"wrote: {args.out_report}")
print(f"wrote: {args.out_manifest}")
if __name__ == "__main__":
main()
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