Datasets:
Tasks:
Tabular Regression
Sub-tasks:
tabular-single-column-regression
Languages:
English
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bdf42bf | 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 | #!/usr/bin/env python3
from __future__ import annotations
import argparse
import shutil
from pathlib import Path
import pandas as pd
DATASET_DIRS = ("charged", "neutral")
COLUMN_MAP = {
"density_g/cm3": "density_g_cm3",
"lcd_ang_H2": "lcd_ang_H2",
"pld_ang_H2": "pld_ang_H2",
"asa_m2/cm3_H2": "asa_m2_cm3_H2",
"asa_m2/g_H2": "asa_m2_g_H2",
"void_fraction_H2": "void_fraction_H2",
"av_ang3_H2": "av_ang3_H2",
"av_cm3/g_H2": "av_cm3_g_H2",
"void_fraction_probe-occupiable_H2": "void_fraction_probe-occupiable_H2",
"av_probe-occupiable_ang3_H2": "av_probe-occupiable_ang3_H2",
"av_probe-occupiable_cm3/g_H2": "av_probe-occupiable_cm3_g_H2",
}
TARGET_COLUMNS = [
"cif",
"density_g_cm3",
"lcd_ang_H2",
"pld_ang_H2",
"asa_m2_cm3_H2",
"asa_m2_g_H2",
"void_fraction_H2",
"av_ang3_H2",
"av_cm3_g_H2",
"void_fraction_probe-occupiable_H2",
"av_probe-occupiable_ang3_H2",
"av_probe-occupiable_cm3_g_H2",
]
def normalize_cif(series: pd.Series) -> pd.Series:
cif = series.astype(str).str.strip()
return cif.str.replace(r"\\.cif$", "", regex=True)
def load_properties_table(csv_path: Path) -> tuple[pd.DataFrame, set[str]]:
required = ["cif", *COLUMN_MAP.keys()]
df = pd.read_csv(csv_path)
missing = [c for c in required if c not in df.columns]
if missing:
raise ValueError(f"Missing required columns in CSV: {', '.join(missing)}")
df["cif"] = normalize_cif(df["cif"])
duplicated = df["cif"].duplicated(keep=False)
if duplicated.any():
dup_count = int(duplicated.sum())
print(
f"[WARN] Found {dup_count} duplicated cif rows in CSV. "
"Keeping the first occurrence for each cif."
)
props = df[["cif", *COLUMN_MAP.keys()]].copy()
props = props.rename(columns=COLUMN_MAP)
props = props.drop_duplicates(subset=["cif"], keep="first")
props = props[TARGET_COLUMNS]
return props, set(props["cif"])
def process_subset(subset_dir: Path, valid_cif: set[str], props: pd.DataFrame) -> dict[str, int]:
raw_dir = subset_dir / "raw"
raw_dir.mkdir(exist_ok=True)
moved = 0
deleted = 0
# Move/delete CIFs from subset root
for cif_path in subset_dir.glob("*.cif"):
if cif_path.stem in valid_cif:
destination = raw_dir / cif_path.name
if destination.exists():
cif_path.unlink()
else:
shutil.move(str(cif_path), str(destination))
moved += 1
else:
cif_path.unlink()
deleted += 1
# Clean invalid CIFs inside raw/
for cif_path in raw_dir.glob("*.cif"):
if cif_path.stem not in valid_cif:
cif_path.unlink()
deleted += 1
raw_cif = sorted(p.stem for p in raw_dir.glob("*.cif") if p.stem in valid_cif)
props_indexed = props.set_index("cif")
available = [c for c in raw_cif if c in props_indexed.index]
missing_in_csv = [c for c in raw_cif if c not in props_indexed.index]
if missing_in_csv:
print(
f"[WARN] {subset_dir.name}: {len(missing_in_csv)} files in raw/ "
"not found in CSV; skipped in id_prop.csv"
)
id_prop = props_indexed.loc[available].reset_index()
id_prop_path = subset_dir / "id_prop.csv"
id_prop.to_csv(id_prop_path, index=False)
return {
"moved": moved,
"deleted": deleted,
"raw_count": len(raw_cif),
"id_prop_rows": len(id_prop),
}
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description=(
"Prepare MOSAEC-DB full/charged and full/neutral datasets: "
"move valid .cif files into raw/ and create id_prop.csv"
)
)
parser.add_argument(
"--csv",
default="mosaec-db.csv",
help="Path to source CSV (default: mosaec-db.csv)",
)
parser.add_argument(
"--root",
default=".",
help="Root directory containing charged and neutral folders (default: current directory)",
)
return parser.parse_args()
def main() -> None:
args = parse_args()
root = Path(args.root).resolve()
csv_path = (root / args.csv).resolve() if not Path(args.csv).is_absolute() else Path(args.csv)
if not csv_path.exists():
raise FileNotFoundError(f"CSV file not found: {csv_path}")
props, valid_cif = load_properties_table(csv_path)
print(f"Loaded {len(valid_cif)} unique cif values from: {csv_path}")
print()
for subset_name in DATASET_DIRS:
subset_dir = root / subset_name
if not subset_dir.exists() or not subset_dir.is_dir():
print(f"[WARN] Skip {subset_name}: folder not found at {subset_dir}")
continue
stats = process_subset(subset_dir, valid_cif, props)
print(
f"{subset_name}: moved={stats['moved']}, deleted={stats['deleted']}, "
f"raw_files={stats['raw_count']}, id_prop_rows={stats['id_prop_rows']}"
)
if __name__ == "__main__":
main()
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