File size: 3,450 Bytes
1f3039b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | #!/usr/bin/env python3
"""Download the first 2M rows of Amazebay-catalog and save locally as a HF dataset.
Usage:
uv run python scripts/make_mini_catalog.py [--max-rows 2000000] [--out data/amazebay-2M]
This streams the dataset so it never loads the full 1.97M+ dataset into memory.
The output is saved in Arrow/Parquet format and can be loaded with:
ds = load_dataset("data/amazebay-2M")
"""
from __future__ import annotations
import argparse
import time
from pathlib import Path
from datasets import Dataset, load_dataset
def main() -> None:
parser = argparse.ArgumentParser(description="Create a mini Amazebay catalog")
parser.add_argument(
"--max-rows", type=int, default=2_000_000,
help="Number of rows to take (default: 2,000,000)",
)
parser.add_argument(
"--out", type=str, default="data/amazebay-2M",
help="Output directory for the saved dataset",
)
parser.add_argument(
"--source", type=str, default="thebajajra/Amazebay-catalog",
help="HuggingFace dataset name",
)
parser.add_argument(
"--batch-size", type=int, default=10_000,
help="Rows to accumulate before printing progress",
)
args = parser.parse_args()
out_path = Path(args.out)
print(f"Streaming {args.source} → first {args.max_rows:,} rows → {out_path}")
t0 = time.time()
ds_stream = load_dataset(args.source, split="train", streaming=True)
# Collect rows in batches for progress reporting
rows: list[dict] = []
for i, row in enumerate(ds_stream):
if i >= args.max_rows:
break
rows.append(row)
if (i + 1) % args.batch_size == 0:
elapsed = time.time() - t0
rate = (i + 1) / elapsed
eta = (args.max_rows - i - 1) / rate if rate > 0 else 0
print(
f" {i + 1:>10,} / {args.max_rows:,} rows "
f"({100 * (i + 1) / args.max_rows:5.1f}%) "
f"{rate:,.0f} rows/s ETA {eta / 60:.1f}min"
)
elapsed = time.time() - t0
print(f"\nCollected {len(rows):,} rows in {elapsed:.1f}s")
# Convert to HF Dataset and save
print("Converting to Arrow dataset...")
ds = Dataset.from_list(rows)
print(f" columns: {ds.column_names}")
print(f" num_rows: {ds.num_rows:,}")
print(f"Saving to {out_path} ...")
out_path.mkdir(parents=True, exist_ok=True)
ds.save_to_disk(str(out_path))
# Also save as parquet for easy inspection
parquet_path = out_path / "catalog.parquet"
ds.to_parquet(str(parquet_path))
print(f"Also saved as parquet: {parquet_path}")
# Quick stats
print("\n--- Quick Stats ---")
cats = {}
valid_prices = 0
for row in rows:
cat = row.get("main_category", "unknown")
cats[cat] = cats.get(cat, 0) + 1
p = row.get("price")
if p and str(p) != "None":
valid_prices += 1
print(f"Total rows: {len(rows):,}")
print(f"Valid prices: {valid_prices:,} ({100 * valid_prices / len(rows):.1f}%)")
print(f"Categories ({len(cats)}):")
for cat, cnt in sorted(cats.items(), key=lambda x: -x[1]):
cat_name = str(cat) if cat is not None else "(None)"
print(f" {cat_name:45s} {cnt:>10,} ({100 * cnt / len(rows):5.1f}%)")
total_sec = time.time() - t0
print(f"\nDone in {total_sec / 60:.1f} min total.")
if __name__ == "__main__":
main()
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