File size: 3,969 Bytes
97ecad4 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | #!/usr/bin/env python3
"""Export Sudoku Extreme R0 train/test CSVs into this ``data/`` folder.
Run from anywhere::
python data/export_sudoku_r0_csv.py
Protocol
--------
1. Read ``data/sudoku_extreme_raw/train.csv``
2. Keep rows with ``rating == 0``
3. Shuffle with seed 42
4. Take first 505_000 rows
5. Write:
- first 500_000 -> ``data/sudoku_r0_train.csv``
- remaining 5_000 -> ``data/sudoku_r0_test.csv``
Columns match simple Sudoku: ``quizzes,solutions``.
Note: the 5k test is the remainder of the 505k train-pool sample, not the
independent Extreme ``test.csv`` R0 pool.
"""
from __future__ import annotations
import csv
import random
from pathlib import Path
# This file lives in <repo>/data
DATA_DIR = Path(__file__).resolve().parent
RAW_TRAIN_CSV = DATA_DIR / "sudoku_extreme_raw" / "train.csv"
OUT_TRAIN_CSV = DATA_DIR / "sudoku_r0_train.csv"
OUT_TEST_CSV = DATA_DIR / "sudoku_r0_test.csv"
SEED = 42
POOL_SIZE = 505_000
TRAIN_SIZE = 500_000 # remainder of POOL_SIZE becomes test
csv.field_size_limit(10**7)
def load_r0(train_csv: Path) -> list[tuple[str, str]]:
if not train_csv.is_file():
raise FileNotFoundError(
f"Missing Extreme train.csv: {train_csv}\n"
"Put official Extreme train.csv under data/sudoku_extreme_raw/."
)
out: list[tuple[str, str]] = []
with train_csv.open(newline="", encoding="utf-8") as f:
reader = csv.DictReader(f)
for row in reader:
q = str(row["question"]).replace(".", "0")
a = str(row["answer"])
if len(q) != 81 or len(a) != 81:
continue
if float(row["rating"]) == 0:
out.append((q, a))
return out
def write_csv(path: Path, pairs: list[tuple[str, str]]) -> None:
with path.open("w", encoding="utf-8", newline="") as f:
w = csv.writer(f)
w.writerow(["quizzes", "solutions"])
w.writerows(pairs)
# ---------------------------------------------------------------------------
# Pull Sudoku Extreme raw CSVs (once) into data/sudoku_extreme_raw/, then run
# this script. From the repo root:
#
# mkdir -p data/sudoku_extreme_raw
# curl -L -o data/sudoku_extreme_raw/train.csv \
# https://huggingface.co/zeyuzy/my_datasets/resolve/main/train.csv
# curl -L -o data/sudoku_extreme_raw/test.csv \
# https://huggingface.co/zeyuzy/my_datasets/resolve/main/test.csv
#
# Windows PowerShell (same two downloads):
#
# New-Item -ItemType Directory -Force -Path data/sudoku_extreme_raw | Out-Null
# curl.exe -L -o data/sudoku_extreme_raw/train.csv `
# https://huggingface.co/zeyuzy/my_datasets/resolve/main/train.csv
# curl.exe -L -o data/sudoku_extreme_raw/test.csv `
# https://huggingface.co/zeyuzy/my_datasets/resolve/main/test.csv
# ---------------------------------------------------------------------------
def main() -> None:
print(f"source: {RAW_TRAIN_CSV}")
print(f"output: {OUT_TRAIN_CSV}")
print(f" {OUT_TEST_CSV}")
print(f"seed={SEED} pool={POOL_SIZE} train={TRAIN_SIZE} test={POOL_SIZE - TRAIN_SIZE}")
print("loading Extreme train.csv, filter rating == 0 ...")
pool = load_r0(RAW_TRAIN_CSV)
print(f" R0 pool size: {len(pool)}")
if len(pool) < POOL_SIZE:
raise ValueError(f"R0 pool has only {len(pool)} rows, need {POOL_SIZE}")
rng = random.Random(SEED)
rng.shuffle(pool)
selected = pool[:POOL_SIZE]
train_pairs = selected[:TRAIN_SIZE]
test_pairs = selected[TRAIN_SIZE:]
train_q = {q for q, _ in train_pairs}
test_q = {q for q, _ in test_pairs}
overlap = len(train_q & test_q)
if overlap:
raise RuntimeError(f"train/test overlap: {overlap}")
write_csv(OUT_TRAIN_CSV, train_pairs)
write_csv(OUT_TEST_CSV, test_pairs)
print(f"wrote {OUT_TRAIN_CSV} ({len(train_pairs)} rows)")
print(f"wrote {OUT_TEST_CSV} ({len(test_pairs)} rows)")
print("DONE")
if __name__ == "__main__":
main()
|