File size: 6,041 Bytes
245bd92 | 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 | from __future__ import annotations
import argparse
import hashlib
import json
from dataclasses import asdict, dataclass
from pathlib import Path
import numpy as np
import pandas as pd
COLUMNS = ("protein_sequence", "compound_smiles", "label")
AMINO_ACIDS = set("ACDEFGHIKLMNPQRSTUVWY")
@dataclass(frozen=True)
class DatasetProfile:
rows: int
unique_proteins: int
unique_compounds: int
unique_pairs: int
protein_length_min: int
protein_length_max: int
protein_length_mean: float
smiles_length_min: int
smiles_length_max: int
smiles_length_mean: float
label_min: float
label_max: float
label_mean: float
label_std: float
duplicate_pairs: int
def load_dataset(path: str | Path, nrows: int | None = None) -> pd.DataFrame:
frame = pd.read_csv(path, nrows=nrows)
missing_columns = set(COLUMNS) - set(frame.columns)
if missing_columns:
raise ValueError(f"Missing required columns: {sorted(missing_columns)}")
frame = frame.loc[:, COLUMNS].copy()
if frame.isna().any().any():
counts = frame.isna().sum()
raise ValueError(f"Missing values found: {counts[counts > 0].to_dict()}")
frame["protein_sequence"] = frame["protein_sequence"].astype(str).str.strip().str.upper()
frame["compound_smiles"] = frame["compound_smiles"].astype(str).str.strip()
frame["label"] = pd.to_numeric(frame["label"], errors="raise").astype(np.float32)
invalid = frame["protein_sequence"].map(lambda sequence: bool(set(sequence) - AMINO_ACIDS))
if invalid.any():
raise ValueError(f"{int(invalid.sum())} protein sequences contain unsupported residues")
return frame
def profile_dataset(frame: pd.DataFrame) -> DatasetProfile:
protein_lengths = frame["protein_sequence"].str.len()
smiles_lengths = frame["compound_smiles"].str.len()
unique_pairs = frame.drop_duplicates(["protein_sequence", "compound_smiles"]).shape[0]
return DatasetProfile(
rows=len(frame),
unique_proteins=frame["protein_sequence"].nunique(),
unique_compounds=frame["compound_smiles"].nunique(),
unique_pairs=unique_pairs,
protein_length_min=int(protein_lengths.min()),
protein_length_max=int(protein_lengths.max()),
protein_length_mean=float(protein_lengths.mean()),
smiles_length_min=int(smiles_lengths.min()),
smiles_length_max=int(smiles_lengths.max()),
smiles_length_mean=float(smiles_lengths.mean()),
label_min=float(frame["label"].min()),
label_max=float(frame["label"].max()),
label_mean=float(frame["label"].mean()),
label_std=float(frame["label"].std()),
duplicate_pairs=len(frame) - unique_pairs,
)
def _stable_fraction(value: str, seed: int) -> float:
digest = hashlib.sha256(f"{seed}:{value}".encode("utf-8")).digest()
return int.from_bytes(digest[:8], "big") / 2**64
def assign_splits(
frame: pd.DataFrame,
strategy: str = "cold_protein",
train_fraction: float = 0.8,
validation_fraction: float = 0.1,
seed: int = 42,
) -> pd.Series:
if train_fraction <= 0 or validation_fraction <= 0:
raise ValueError("Train and validation fractions must be positive")
if train_fraction + validation_fraction >= 1:
raise ValueError("Train plus validation fraction must be below 1")
if strategy == "cold_protein":
keys = frame["protein_sequence"]
elif strategy == "cold_compound":
keys = frame["compound_smiles"]
elif strategy == "pair":
keys = frame["protein_sequence"] + "|" + frame["compound_smiles"]
elif strategy == "random":
keys = pd.Series(np.arange(len(frame)).astype(str), index=frame.index)
else:
raise ValueError("strategy must be one of: cold_protein, cold_compound, pair, random")
fractions = keys.map(lambda value: _stable_fraction(str(value), seed))
splits = np.where(
fractions < train_fraction,
"train",
np.where(fractions < train_fraction + validation_fraction, "validation", "test"),
)
return pd.Series(splits, index=frame.index, name="split")
def make_sample(
source: str | Path,
destination: str | Path,
rows: int = 512,
seed: int = 42,
) -> pd.DataFrame:
frame = load_dataset(source)
if rows >= len(frame):
sample = frame
else:
bins = pd.qcut(frame["label"], q=min(10, rows), duplicates="drop")
sample = (
frame.assign(_bin=bins)
.groupby("_bin", observed=True, group_keys=False)
.apply(
lambda group: group.sample(
n=max(1, round(rows * len(group) / len(frame))),
random_state=seed,
),
include_groups=False,
)
.head(rows)
.loc[:, COLUMNS]
)
if len(sample) < rows:
remainder = frame.drop(index=sample.index).sample(
rows - len(sample),
random_state=seed,
)
sample = pd.concat([sample, remainder], ignore_index=True)
destination = Path(destination)
destination.parent.mkdir(parents=True, exist_ok=True)
sample.to_csv(destination, index=False)
return sample
def main() -> None:
parser = argparse.ArgumentParser(
description="Validate, profile, or sample the affinity dataset"
)
parser.add_argument("--data", default="data/train.csv")
parser.add_argument("--output", default="")
parser.add_argument("--sample-rows", type=int, default=0)
parser.add_argument("--seed", type=int, default=42)
args = parser.parse_args()
if args.sample_rows:
if not args.output:
parser.error("--output is required with --sample-rows")
frame = make_sample(args.data, args.output, args.sample_rows, args.seed)
else:
frame = load_dataset(args.data)
profile = asdict(profile_dataset(frame))
print(json.dumps(profile, indent=2))
if __name__ == "__main__":
main()
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