File size: 3,493 Bytes
f28d994 | 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 | """Create a notebook-style local validation split.
The official example notebook creates validation data by:
1. sampling 90% of train author-paper edges as training edges;
2. using the remaining 10% known positives as validation positives;
3. sampling the same number of random negatives not present in all known refs.
This script materializes that split so later experiments use identical data.
"""
from __future__ import annotations
import argparse
from pathlib import Path
import numpy as np
import pandas as pd
def read_txt(path: Path) -> list[list[int]]:
rows: list[list[int]] = []
with path.open("r") as f:
for line in f:
rows.append(list(map(int, line.strip().split())))
return rows
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--package-root", type=Path, default=Path(__file__).resolve().parents[1])
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--train-frac", type=float, default=0.9)
args = parser.parse_args()
root = args.package_root
data_dir = root / "data_and_docs"
split_dir = root / "splits" / f"notebook_seed{args.seed}"
split_dir.mkdir(parents=True, exist_ok=True)
refs = read_txt(data_dir / "bipartite_train_ann.txt")
coauthor = read_txt(data_dir / "author_file_ann.txt")
citation = read_txt(data_dir / "paper_file_ann.txt")
test_refs = read_txt(data_dir / "bipartite_test_ann.txt")
ref_edges = pd.DataFrame(refs, columns=["source", "target"])
ref_edges = ref_edges.set_index("r-" + ref_edges.index.astype(str))
coauthor_edges = pd.DataFrame(coauthor, columns=["source", "target"])
citation_edges = pd.DataFrame(citation, columns=["source", "target"])
test_arr = np.array(test_refs, dtype=np.int64)
node_tmp = pd.concat([citation_edges["source"], citation_edges["target"], ref_edges["target"]])
paper_ids = pd.unique(node_tmp).astype(np.int64)
node_tmp = pd.concat([ref_edges["source"], coauthor_edges["source"], coauthor_edges["target"]])
author_ids = pd.unique(node_tmp).astype(np.int64)
train_refs = ref_edges.sample(frac=args.train_frac, random_state=args.seed, axis=0)
val_pos = ref_edges[~ref_edges.index.isin(train_refs.index)].copy()
val_pos.loc[:, "label"] = 1
existing_ref_set = set(map(tuple, ref_edges[["source", "target"]].to_numpy().tolist()))
neg_pairs: list[tuple[int, int]] = []
rng = np.random.default_rng(args.seed)
while len(neg_pairs) < len(val_pos):
src = int(rng.choice(author_ids))
dst = int(rng.choice(paper_ids))
if (src, dst) not in existing_ref_set:
neg_pairs.append((src, dst))
val_neg = pd.DataFrame(neg_pairs, columns=["source", "target"])
val_neg.loc[:, "label"] = 0
val_pairs = pd.concat([val_pos.reset_index(drop=True), val_neg], ignore_index=True)
val_pairs = val_pairs.sample(frac=1, random_state=args.seed, axis=0).reset_index(drop=True)
train_refs[["source", "target"]].to_csv(split_dir / "train_refs.csv", index=False)
val_pairs[["source", "target", "label"]].to_csv(split_dir / "val_pairs.csv", index=False)
np.save(split_dir / "test_refs.npy", test_arr)
print(f"wrote {split_dir}")
print(f"train positives: {len(train_refs)}")
print(f"val positives: {int(val_pairs['label'].sum())}")
print(f"val negatives: {int((val_pairs['label'] == 0).sum())}")
print(f"val total: {len(val_pairs)}")
if __name__ == "__main__":
main()
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