33 lines
1.2 KiB
Python
Executable File
33 lines
1.2 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
"""Prepare vocab and normalization stats for HAI 21.03."""
|
|
|
|
import json
|
|
from typing import Optional
|
|
|
|
from data_utils import compute_cont_stats, build_vocab, load_split
|
|
|
|
DATA_PATH = "/home/anay/Dev/diffusion/dataset/hai/hai-21.03/train1.csv.gz"
|
|
SPLIT_PATH = "/home/anay/Dev/diffusion/mask-ddpm/example/feature_split.json"
|
|
OUT_STATS = "/home/anay/Dev/diffusion/mask-ddpm/example/results/cont_stats.json"
|
|
OUT_VOCAB = "/home/anay/Dev/diffusion/mask-ddpm/example/results/disc_vocab.json"
|
|
|
|
|
|
def main(max_rows: Optional[int] = None):
|
|
split = load_split(SPLIT_PATH)
|
|
cont_cols = split["continuous"]
|
|
disc_cols = split["discrete"]
|
|
|
|
mean, std = compute_cont_stats(DATA_PATH, cont_cols, max_rows=max_rows)
|
|
vocab = build_vocab(DATA_PATH, disc_cols, max_rows=max_rows)
|
|
|
|
with open(OUT_STATS, "w", encoding="ascii") as f:
|
|
json.dump({"mean": mean, "std": std, "max_rows": max_rows}, f, indent=2)
|
|
|
|
with open(OUT_VOCAB, "w", encoding="ascii") as f:
|
|
json.dump({"vocab": vocab, "max_rows": max_rows}, f, indent=2)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# Default: sample 50000 rows for speed. Set to None for full scan.
|
|
main(max_rows=50000)
|