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d5ecd7e 791c23b d5ecd7e 791c23b 5b85fd3 d5ecd7e 791c23b d5ecd7e 791c23b d5ecd7e 791c23b d5ecd7e 5b85fd3 d5ecd7e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 | import csv
import gzip
import pickle
import hashlib
import os
import shutil
import uuid
import multiprocessing as mp
from tqdm import tqdm
import numpy as np
import lmdb
import requests
from src.utils import utils
ctx = mp.get_context("spawn") # safer with LMDB/pickle
Process = ctx.Process
Queue = ctx.Queue
def _is_test(draft_id: str, pct=10):
h = int.from_bytes(hashlib.sha1(draft_id.encode()).digest()[:4], "little")
return h % 100 < pct
def lmdb_writer(db_path, q: Queue, commit_every=200_000, map_size=int(16e9)):
env = lmdb.open(db_path, map_size=map_size, subdir=True, lock=True,
sync=False, metasync=False, readahead=False, writemap=False) # writemap=False to avoid big prealloc on some FS
txn, n = env.begin(write=True), 0
while True:
item = q.get()
if item is None:
break
key, value = item
try:
txn.put(key, value)
except lmdb.MapFullError:
txn.abort()
env.set_mapsize(env.info()["map_size"] * 2)
txn = env.begin(write=True)
txn.put(key, value)
n += 1
if n % commit_every == 0:
txn.commit(); txn = env.begin(write=True)
txn.commit()
with env.begin(write=True) as t2:
t2.put(b"__len__", str(n).encode())
env.sync(); env.close()
def get_name(obstructed_name):
if 'pack_card_' in obstructed_name:
return obstructed_name.replace('pack_card_','')
elif 'pool_' in obstructed_name:
return obstructed_name.replace('pool_','')
else:
print(obstructed_name)
raise ValueError('Invalid name')
def encode_sample(sample):
return pickle.dumps(sample, protocol=pickle.HIGHEST_PROTOCOL)
def stream_csv(csv_path, train_q, test_q):
with open(csv_path, "r", newline="", buffering=16*1024*1024) as f:
reader = csv.reader(f)
header = next(reader)
pack_idx = [i for i,c in enumerate(header) if c.startswith("pack_card_")]
pool_idx = [i for i,c in enumerate(header) if c.startswith("pool_")]
pick_idx = header.index("pick")
wins_idx = header.index("event_match_wins")
loss_idx = header.index("event_match_losses")
try:
user_games = header.index("user_n_games_bucket")
except:
user_games = header.index("user_n_matches_bucket")
try:
user_wr = header.index("user_game_win_rate_bucket")
except:
try:
user_wr = header.index("user_match_win_rate_bucket")
except:
user_wr = None
draft_idx = 2 # your draft id column
pack_names = {i: header[i].replace("pack_card_","") for i in pack_idx}
pool_names = {i: header[i].replace("pool_","") for i in pool_idx}
for row in tqdm(reader):
try:
draft_id = row[draft_idx]
positive = row[pick_idx]
except:
print(f"Skipping row with missing draft_id or pick: {row}")
continue
negatives = []
for i in pack_idx:
s = row[i]
if s and s != "0":
cnt = int(s)
name = pack_names[i]
if name != positive:
negatives.extend([name]*cnt)
anchor = []
for i in pool_idx:
s = row[i]
if s and s != "0":
anchor.extend([pool_names[i]]*int(s))
wins = int(row[wins_idx])
losses = int(row[loss_idx])
u_g = int(row[user_games])
u_wr = float(row[user_wr]) if user_wr and row[user_wr] else 0.0
payload = encode_sample((positive, negatives, anchor, wins, losses, u_g, u_wr))
key = os.urandom(16)
(test_q if _is_test(draft_id) else train_q).put((key, payload))
_17LANDS_URL = (
"https://17lands-public.s3.amazonaws.com/analysis_data/draft_data/"
"draft_data_public.{set_tag}.{draft_format}.csv.gz"
)
def download_17lands(set_tag, raw_data_folder, draft_format="PremierDraft"):
url = _17LANDS_URL.format(set_tag=set_tag, draft_format=draft_format)
out_dir = os.path.join(raw_data_folder, set_tag)
os.makedirs(out_dir, exist_ok=True)
csv_path = os.path.join(out_dir, f"{set_tag}_{draft_format}.csv")
if os.path.exists(csv_path):
print(f"{csv_path} already exists, skipping download")
return
print(f"Downloading {url}")
response = requests.get(url, stream=True)
if response.status_code == 404:
raise FileNotFoundError(
f"17lands data not found for {set_tag}/{draft_format}.\n"
f"URL tried: {url}\n"
f"Check https://17lands.com/public/data for the correct set code and format."
)
response.raise_for_status()
gz_path = csv_path + ".gz"
total = int(response.headers.get("content-length", 0))
with open(gz_path, "wb") as f, tqdm(total=total, unit="B", unit_scale=True,
desc=f"Downloading {set_tag}") as bar:
for chunk in response.iter_content(chunk_size=1 << 17):
f.write(chunk)
bar.update(len(chunk))
print(f"Decompressing {gz_path}")
with gzip.open(gz_path, "rb") as f_in, open(csv_path, "wb") as f_out:
shutil.copyfileobj(f_in, f_out)
os.remove(gz_path)
print(f"Saved to {csv_path}")
def stream_csv_trajectories(csv_path, train_q, test_q):
"""Buffer all picks by draft_id, then write one trajectory per draft."""
drafts = {}
with open(csv_path, "r", newline="", buffering=16*1024*1024) as f:
reader = csv.reader(f)
header = next(reader)
pack_idx = [i for i,c in enumerate(header) if c.startswith("pack_card_")]
pick_idx = header.index("pick")
wins_idx = header.index("event_match_wins")
loss_idx = header.index("event_match_losses")
draft_idx = 2
try:
user_games = header.index("user_n_games_bucket")
except:
user_games = header.index("user_n_matches_bucket")
try:
user_wr = header.index("user_game_win_rate_bucket")
except:
try:
user_wr = header.index("user_match_win_rate_bucket")
except:
user_wr = None
if "expansion_pick_number" in header:
order_idx = header.index("expansion_pick_number")
pack_num_idx = None
pick_num_idx = None
else:
order_idx = None
pack_num_idx = header.index("pack_number")
pick_num_idx = header.index("pick_number")
maindeck_idx = header.index("pick_maindeck_rate") if "pick_maindeck_rate" in header else None
pack_names = {i: header[i].replace("pack_card_", "") for i in pack_idx}
for row in tqdm(reader):
try:
draft_id = row[draft_idx]
positive = row[pick_idx]
except:
continue
step = int(row[order_idx]) if order_idx is not None \
else int(row[pack_num_idx]) * 15 + int(row[pick_num_idx])
# pick first, then the rest of the pack
pack_cards = [positive]
for i in pack_idx:
s = row[i]
if s and s != "0":
name = pack_names[i]
if name != positive:
pack_cards.extend([name] * int(s))
wins = int(row[wins_idx])
losses = int(row[loss_idx])
u_g = int(row[user_games])
u_wr = float(row[user_wr]) if user_wr and row[user_wr] else 0.0
in_md = float(row[maindeck_idx]) if maindeck_idx is not None and row[maindeck_idx] else 0.0
if positive in ('Plains', 'Island', 'Swamp', 'Mountain', 'Forest'):
in_md = 0.0
if draft_id not in drafts:
drafts[draft_id] = {'steps': {}, 'wins': wins, 'losses': losses,
'u_g': u_g, 'u_wr': u_wr}
if step not in drafts[draft_id]['steps']:
drafts[draft_id]['steps'][step] = (pack_cards, in_md)
for draft_id, data in drafts.items():
sorted_steps = sorted(data['steps'].items())
sequence = [pack for _, (pack, _) in sorted_steps]
in_maindeck = [md for _, (_, md) in sorted_steps]
if not sequence:
continue
payload = encode_sample((sequence, in_maindeck, data['wins'], data['losses'],
data['u_g'], data['u_wr']))
(test_q if _is_test(draft_id) else train_q).put((draft_id.encode(), payload))
def all_preprocessing_for_set(set_tag, raw_data_folder, out_folder):
base = os.path.join(raw_data_folder, set_tag)
files = [os.path.join(base, f) for f in os.listdir(base) if f.endswith(".csv")]
out = os.path.join(out_folder, set_tag)
for name in ('train.lmdb', 'test.lmdb'):
p = os.path.join(out, name)
if os.path.exists(p):
shutil.rmtree(p)
os.makedirs(out, exist_ok=True)
train_q = Queue(maxsize=50_000)
test_q = Queue(maxsize=50_000)
train_writer = Process(target=lmdb_writer, args=(os.path.join(out, "train.lmdb"), train_q))
test_writer = Process(target=lmdb_writer, args=(os.path.join(out, "test.lmdb"), test_q))
train_writer.start(); test_writer.start()
for csv_path in files:
print(f"Processing {os.path.basename(csv_path)}")
stream_csv_trajectories(csv_path, train_q, test_q)
train_q.put(None); test_q.put(None)
train_writer.join(); test_writer.join()
if __name__ == "__main__":
config = utils.load_config('src/configs/config.yaml')
raw_data_folder = config['raw_data_folder']
out_folder = config['super_folder']
os.makedirs(out_folder, exist_ok=True)
for folder in os.listdir(raw_data_folder):
if folder not in os.listdir(out_folder) and folder != "cube":
print(f"Processing set {folder}")
all_preprocessing_for_set(folder, raw_data_folder, out_folder)
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