File size: 2,628 Bytes
ee5764c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import gzip
import json
import random
from tqdm.auto import tqdm
from concurrent.futures import ProcessPoolExecutor

MONTHS = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"]

def determine_format(s):
    for format in ["Bullet", "Blitz", "Rapid", "Classical", "Correspondence"]:
        if format in s:
            return format
    else:
        return ""
    
subsample_ratios = {
 1700.0: 0.03773727310464546,
 1800.0: 0.037873049537948796,
 1600.0: 0.03882816595158128,
 1900.0: 0.04081466062609689,
 1500.0: 0.04225024822020829,
 1400.0: 0.04841208365608056,
 2000.0: 0.050853060082890485,
 1300.0: 0.05542161997395184,
 1200.0: 0.0683480281593876,
 2100.0: 0.07245851749873197,
 1100.0: 0.08971023593792052,
 2200.0: 0.1211827435773146,
 1000.0: 0.12567550584391102,
 900.0: 0.22724690376093626,
 2300.0: 0.24479804161566707,
 800.0: 0.47438330170777987,
 2400.0: 0.5417118093174431,
 700.0: 1.0,
 2500.0: 1.0,
 600.0: 1.0,
 2600.0: 1.0,
 2700.0: 1.0,
 2800.0: 1.0,
 2900.0: 1.0
}
    
def checks(game: dict) -> bool:
    format = determine_format(game["event"])
    white_elo = int(game["white-elo"])
    black_elo = int(game["black-elo"])
    elo_diff = abs(white_elo - black_elo)
    elo_mean = (white_elo + black_elo) / 2
    subsample_ratio = subsample_ratios.get(
        (elo_mean) // 100 * 100,
        1.0
    )
    n_plys = len(game["moves-uci"].split())

    return format in ("Blitz", "Rapid") and \
        elo_diff <= 100 and \
        random.random() < subsample_ratio and \
        n_plys >= 4
    

        

def filter_month(month: str):

    train = gzip.open(f"/data/datasets/models/hf_cache/tmp-lichess/lichess-2022/br-subsample/train-2022-{month}.jsonl.gz", "wb")
    val = gzip.open(f"/data/datasets/models/hf_cache/tmp-lichess/lichess-2022/br-subsample/val-2022-{month}.jsonl.gz", "wb")
    test = gzip.open(f"/data/datasets/models/hf_cache/tmp-lichess/lichess-2022/br-subsample/test-2022-{month}.jsonl.gz", "wb")

    with gzip.open(f"/data/datasets/models/hf_cache/tmp-lichess/lichess-2022/all/2022-{month}.jsonl.gz", "rb") as f:
        for i, line in tqdm(enumerate(f), desc=month):
            d = json.loads(line)
            if checks(d):
                if random.random() < 1e-4:
                    val.write((json.dumps(d) + '\n').encode())
                elif random.random() < 2e-4:
                    test.write((json.dumps(d) + '\n').encode())
                else:
                    train.write((json.dumps(d) + '\n').encode())

    return 0


ppe = ProcessPoolExecutor(12)
for _ in ppe.map(filter_month, MONTHS):
    ...

ppe.shutdown()
print("Done!")