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ad9fbbf | 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 | #!/usr/bin/env python3
"""Run MVGNN-PPIS five-fold ensemble inference."""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
import pandas as pd
import torch
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from model import MVGNN, Seed_everything, model_test # noqa: E402
def project_path(value: str | Path) -> Path:
"""Resolve package-relative paths while preserving explicit absolute paths."""
path = Path(value).expanduser()
return path if path.is_absolute() else PROJECT_ROOT / path
def load_config(path: Path) -> dict:
with path.open("r", encoding="utf-8") as handle:
return json.load(handle)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Predict protein-protein interaction sites with MVGNN-PPIS."
)
parser.add_argument(
"--config",
default="conf/config.json",
help="Package-relative JSON configuration file.",
)
parser.add_argument("--dataset", help="Override the evaluation CSV path.")
parser.add_argument("--feature-path", help="Override the precomputed feature directory.")
parser.add_argument("--weight-path", help="Override the checkpoint directory.")
parser.add_argument("--output-path", help="Override the prediction output directory.")
parser.add_argument("--num-workers", type=int, help="DataLoader worker count.")
parser.add_argument("--seed", type=int, help="Random seed.")
parser.add_argument(
"--device",
help="PyTorch device, for example 'cuda', 'cuda:0', or 'cpu'.",
)
return parser.parse_args()
def required_feature_files(feature_dir: Path, protein_ids: list[str]) -> list[Path]:
suffixes = ("X", "node_feature", "mask", "label", "adj")
return [
feature_dir / f"{protein_id}_{suffix}.tensor"
for protein_id in protein_ids
for suffix in suffixes
]
def main() -> None:
args = parse_args()
config = load_config(project_path(args.config))
paths = config["paths"]
dataset_path = project_path(args.dataset or paths["dataset"])
feature_dir = project_path(args.feature_path or paths["feature_dir"])
weight_dir = project_path(args.weight_path or paths["weight_dir"])
output_dir = project_path(args.output_path or paths["output_dir"])
runtime = config["runtime"]
seed = args.seed if args.seed is not None else runtime["seed"]
num_workers = (
args.num_workers if args.num_workers is not None else runtime["num_workers"]
)
requested_device = args.device or runtime.get("device", "auto")
if requested_device == "auto":
requested_device = "cuda" if torch.cuda.is_available() else "cpu"
device = torch.device(requested_device)
if device.type == "cuda" and not torch.cuda.is_available():
raise RuntimeError("A CUDA/HIP device was requested but torch.cuda.is_available() is False.")
test_df = pd.read_csv(
dataset_path,
dtype={"ID": str, "sequence": str, "label": str},
keep_default_na=False,
)
required_columns = {"ID", "sequence", "label"}
missing_columns = required_columns.difference(test_df.columns)
if missing_columns:
raise ValueError(
f"Dataset {dataset_path} is missing columns: {sorted(missing_columns)}"
)
test_df["ID"] = test_df["ID"].str.strip()
protein_ids = test_df["ID"].str.strip().drop_duplicates().tolist()
missing_features = [
path for path in required_feature_files(feature_dir, protein_ids) if not path.is_file()
]
if missing_features:
preview = "\n".join(f" - {path}" for path in missing_features[:10])
raise FileNotFoundError(
f"Missing {len(missing_features)} precomputed feature files.\n{preview}"
)
folds = config["model"]["folds"]
missing_weights = [
weight_dir / f"fold{fold}.ckpt"
for fold in range(folds)
if not (weight_dir / f"fold{fold}.ckpt").is_file()
]
if missing_weights:
formatted = "\n".join(f" - {path}" for path in missing_weights)
raise FileNotFoundError(f"Missing model checkpoints:\n{formatted}")
all_protein_data = {}
for protein_id in protein_ids:
all_protein_data[protein_id] = tuple(
torch.load(
feature_dir / f"{protein_id}_{suffix}.tensor",
map_location="cpu",
weights_only=True,
)
for suffix in ("X", "node_feature", "mask", "label", "adj")
)
Seed_everything(seed)
model_config = dict(config["model"])
model_config.update(
{
"seed": seed,
"id_name": "ID",
"remark": "PRO binding site prediction",
}
)
print(f"Project root: {PROJECT_ROOT}")
print(f"Dataset: {dataset_path}")
print(f"Proteins: {len(test_df)}")
print(f"Device: {device}")
print(f"Checkpoints: {folds}")
model_test(
test_df,
all_protein_data,
MVGNN,
model_config,
weight_dir=weight_dir,
output_dir=output_dir,
logit=True,
device=device,
num_workers=num_workers,
)
if __name__ == "__main__":
main()
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