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8e574de ae74a72 8e574de ae74a72 8e574de ae74a72 8e574de ae74a72 8e574de ae74a72 8e574de ae74a72 8e574de ae74a72 8e574de ae74a72 8e574de ae74a72 8e574de | 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 | #!/usr/bin/env python3
from __future__ import annotations
import os
import argparse
import pandas as pd
OUT_SPLITS = ['train', 'validation', 'test']
def ensure_dir(p: str):
os.makedirs(p, exist_ok=True)
def write_subset(out_dir: str, name: str, df: pd.DataFrame):
outp = os.path.join(out_dir, name)
ensure_dir(outp)
for split in OUT_SPLITS:
fp = os.path.join(outp, f"{split}.parquet")
d_split = df[df["split"] == split].copy()
d_split.to_parquet(fp, index=False)
print(f"{name}/{split}: {len(d_split):,} -> {fp}")
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--input", default="../data/fireprotdb_with_cluster_splits.parquet",
help="Parquet with MMseqs2 cluster_id and split columns already assigned")
ap.add_argument("--out_dir", default="../data/subsets",
help="Output dir for HF subset parquets")
ap.add_argument("--drop_columns", default="",
help="Comma-separated columns to drop in outputs (optional)")
ap.add_argument("--require_sequence", action="store_true",
help="If set, drop rows without a UniProt sequence before creating subsets")
ap.add_argument("--strict_split_check", action="store_true",
help="If set, error if any protein/cluster appears in multiple splits")
args = ap.parse_args()
df = pd.read_parquet(args.input)
# Basic required columns
if "split" not in df.columns:
raise ValueError("Input parquet must contain a 'split' column (train/validation/test).")
if "mutation" not in df.columns:
raise ValueError("Input parquet must contain a 'mutation' column.")
# Normalize split values
df["split"] = df["split"].astype("string").str.strip().str.lower()
bad = sorted(set(df["split"].dropna().unique()) - set(OUT_SPLITS))
if bad:
raise ValueError(f"Unexpected split values: {bad}. Expected {OUT_SPLITS}.")
if args.require_sequence and "sequence" in df.columns:
before = len(df)
df = df[df["sequence"].notna()].copy()
print(f"Dropped rows without sequence: {before - len(df):,}")
# Optional leakage check: ensure each cluster/protein lives in exactly one split
# Prefer cluster_id if available, else fall back to uniprotkb/sequence_id
if args.strict_split_check:
if "cluster_id" in df.columns:
key = "cluster_id"
elif "uniprotkb" in df.columns:
key = "uniprotkb"
else:
key = "sequence_id"
counts = df.groupby(key, dropna=False)["split"].nunique()
leaked = counts[counts > 1]
if len(leaked):
example = leaked.head(10)
raise ValueError(
f"Leakage detected: {len(leaked):,} {key}s appear in multiple splits.\n"
f"Examples:\n{example}"
)
print(f"Split leakage check passed using key={key}.")
# Base filter: parsed mutation only (matches your fireprotdb.py intent)
has_mut = df["mutation"].notna()
df_mut = df[has_mut].copy()
# Build subsets (you said you only want these)
dg = df_mut[df_mut["dg"].notna()].copy()
ddg = df_mut[df_mut["ddg"].notna()].copy()
tm = df_mut[df_mut["tm"].notna()].copy()
dtm = df_mut[df_mut["dtm"].notna()].copy()
fitness = df_mut[df_mut["fitness"].notna()].copy()
binary = df_mut[df_mut["stabilizing"].notna()].copy()
# Optional: drop columns to slim outputs
if args.drop_columns.strip():
drop_cols = [c.strip() for c in args.drop_columns.split(",") if c.strip()]
keep_drop = [c for c in drop_cols if c in df.columns]
if keep_drop:
dg = dg.drop(columns=keep_drop, errors="ignore")
ddg = ddg.drop(columns=keep_drop, errors="ignore")
tm = tm.drop(columns=keep_drop, errors="ignore")
dtm = dtm.drop(columns=keep_drop, errors="ignore")
fitness = fitness.drop(columns=keep_drop, errors="ignore")
binary = binary.drop(columns=keep_drop, errors="ignore")
write_subset(args.out_dir, "mutation_dg", dg)
write_subset(args.out_dir, "mutation_ddg", ddg)
write_subset(args.out_dir, "mutation_tm", tm)
write_subset(args.out_dir, "mutation_dtm", dtm)
write_subset(args.out_dir, "mutation_fitness", fitness)
write_subset(args.out_dir, "mutation_binary", binary)
if __name__ == "__main__":
main()
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