File size: 3,011 Bytes
5f87c14 | 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 | # Completed Dataset Results
## Artifacts
- Dataset: `data/ccrl-high-variance-1p5m.vpd`
- Training dataset: `data/ccrl-high-variance-1p5m.parquet/`
- Parquet manifest: `data/ccrl-high-variance-1p5m.parquet/_manifest.json`
- Variance report: `data/variance-report.json`
- Source archive: `source/ccrl-pgn.tar.bz2`
- Extractor and reader: `vpd.py`
- Format specification: `FORMAT.md`
## Provenance
The source is Leela Chess Zero's published CCRL standard dataset: 2.5 million
CCRL 40/40 and 40/4 engine games with an 80/20 train/test split. Extraction
used 910,462 source games and completed with zero PGN parse errors.
The final VPD1 database contains 1,509,201 unique normalized FEN positions and
is 438,558,720 bytes.
The Parquet derivative contains the same 1,509,201 rows in six Hive-style
`source_split`/`phase` partitions. It is 52 MiB on disk, uses Zstandard level 6
compression, and has 26 row groups capped at 65,536 rows.
## Variance result
Overall result: **HIGH VARIANCE — PASS**
Every declared threshold in `data/variance-report.json` passed.
| Dimension | Result |
| --- | ---: |
| Opening | 234,201 (15.52%) |
| Middlegame | 900,000 (59.63%) |
| Endgame | 375,000 (24.85%) |
| Normalized phase entropy | 0.858704 |
| White wins | 570,602 (37.81%) |
| Draws | 481,726 (31.92%) |
| Black wins | 456,873 (30.27%) |
| White to move | 758,019 (50.23%) |
| Black to move | 751,182 (49.77%) |
| Positions in check | 103,519 (6.86%) |
| Positions with castling rights | 289,814 (19.20%) |
| Positions without castling rights | 1,219,387 (80.80%) |
| Material imbalance of at least 3 | 234,018 (15.51%) |
Legal move counts span 0–87, with p10 12, median 33, p90 44, and standard
deviation 12.064. Piece counts span 2–32, with p10 10, median 21, p90 30, and
standard deviation 7.5146.
## Validation
- SQLite `PRAGMA integrity_check`: `ok`
- Random FEN validation: 1,000/1,000 valid
- Indexed random retrieval: 1,000 queries in 4.404 ms
- Mean in-process lookup latency: 0.004404 ms per board
- Parquet row-count validation: 1,509,201/1,509,201
- Parquet partition-count validation: 6/6 exact matches
- Parquet FEN validation sample: 6,000/6,000 valid
- Parquet file checksums: 6/6 match `_manifest.json`
- Parquet compression audit: all 26 row groups use Zstandard
- Source archive SHA-256:
`5f4d7ec86a99ba56fd3e46b1eb35f3ae109890bc77fecc3e605a6174ea717e76`
- Dataset SHA-256:
`2f3af2973d1e4cb2d7e0d54e7e7a0f12def44d982915df0c9cab1735f6b138cc`
- Report SHA-256:
`759699d7bc704fd265524a6df320c1a0c925f902e29a60b83366d987d3ab507e`
## Retrieve one board
```bash
cd /Users/pawit/Documents/vexilon/tmp/vex-position-dataset
PYTHONDONTWRITEBYTECODE=1 .venv/bin/python vpd.py sample \
data/ccrl-high-variance-1p5m.vpd
```
Example output:
```json
{"id":257499,"fen":"8/4p3/p6p/1p3k1K/1n3P2/1P4P1/r3RN2/8 w - - 0 1","phase":"endgame","result":"1-0"}
```
The source `result` is provenance metadata. It is not the depth-zero static WDL
label; lc0 can add that label in a subsequent pass.
|