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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 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | """Generate full-test dot-score ensemble submissions from saved full checkpoints."""
from __future__ import annotations
import argparse
import importlib.util
import pickle as pkl
from pathlib import Path
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from torch_geometric.data import HeteroData
EDGE_TYPES = [
("author", "ref", "paper"),
("paper", "beref", "author"),
("paper", "cite", "paper"),
("author", "coauthor", "author"),
]
def read_txt(path: Path):
return [list(map(int, line.strip().split())) for line in path.open()]
def log_norm(x):
x = np.log1p(x)
return (x - x.mean()) / (x.std() + 1e-8)
class LightGCNLayer(nn.Module):
def forward(self, x_dict, edge_index_dict):
agg_dict = {node_type: [] for node_type in x_dict}
for et in EDGE_TYPES:
if et not in edge_index_dict:
continue
st, _, dt = et
src, dst = edge_index_dict[et]
sx = x_dict[st]
agg = sx.new_zeros((x_dict[dt].size(0), sx.size(-1)))
deg = sx.new_zeros((x_dict[dt].size(0), 1))
agg.index_add_(0, dst, sx[src])
deg.index_add_(0, dst, torch.ones((dst.numel(), 1), dtype=sx.dtype, device=sx.device))
agg_dict[dt].append(agg / deg.clamp(min=1.0))
return {nt: sum(v) / len(v) if v else x_dict[nt] for nt, v in agg_dict.items()}
class LightGCN(nn.Module):
def __init__(self, n_author, feat_dim, dim, layers=4):
super().__init__()
self.author_emb = nn.Embedding(n_author, dim)
self.paper_proj = nn.Linear(feat_dim, dim)
self.layers = nn.ModuleList([LightGCNLayer() for _ in range(layers)])
self.num_layers = layers
def encode(self, data):
x = {"author": self.author_emb.weight, "paper": self.paper_proj(data["paper"].x)}
all_x = [x]
for layer in self.layers:
x = layer(x, data.edge_index_dict)
all_x.append(x)
w = 1.0 / len(all_x)
return {nt: sum(w * xx[nt] for xx in all_x) for nt in x}
def build(root: Path, device):
data_dir = root / "data_and_docs"
refs = read_txt(data_dir / "bipartite_train_ann.txt")
test = read_txt(data_dir / "bipartite_test_ann.txt")
cite = read_txt(data_dir / "paper_file_ann.txt")
coa = read_txt(data_dir / "author_file_ann.txt")
with (data_dir / "feature.pkl").open("rb") as f:
feat = pkl.load(f).numpy().astype(np.float32)
n_paper = 79937
ref_deg = np.zeros(n_paper, np.float32)
cout = np.zeros(n_paper, np.float32)
cin = np.zeros(n_paper, np.float32)
for _, p in refs:
ref_deg[p] += 1
for s, t in cite:
cout[s] += 1
cin[t] += 1
deg = np.stack([log_norm(ref_deg), log_norm(cout), log_norm(cin)], axis=-1)
paper_x = np.concatenate([feat, deg], axis=1)
paper_x = (paper_x - paper_x.mean(0)) / (paper_x.std(0) + 1e-8)
rt = torch.as_tensor(np.array(refs), dtype=torch.long)
ct = torch.as_tensor(np.array(cite), dtype=torch.long)
co = torch.as_tensor(np.array(coa), dtype=torch.long)
data = HeteroData()
data["author"].num_nodes = 6611
data["paper"].num_nodes = n_paper
data["paper"].x = torch.as_tensor(paper_x, dtype=torch.float)
data["author", "ref", "paper"].edge_index = rt.t().contiguous()
data["paper", "beref", "author"].edge_index = rt[:, [1, 0]].t().contiguous()
data["paper", "cite", "paper"].edge_index = torch.cat([ct, ct[:, [1, 0]]], 0).t().contiguous()
data["author", "coauthor", "author"].edge_index = torch.cat([co, co[:, [1, 0]]], 0).t().contiguous()
return data.to(device), np.array(test, dtype=np.int64), refs, paper_x.shape[1]
@torch.no_grad()
def predict_dot(model, data, pairs, batch_size):
z = model.encode(data)
a = z["author"].detach().cpu().numpy()
p = z["paper"].detach().cpu().numpy()
out = []
for st in range(0, len(pairs), batch_size):
b = pairs[st : st + batch_size]
out.append(np.sum(a[b[:, 0]] * p[b[:, 1]], axis=1).astype(np.float32))
return np.concatenate(out)
def rank01(x):
order = np.argsort(x, kind="mergesort")
r = np.empty(len(x), dtype=np.float32)
r[order] = np.linspace(0, 1, len(x), dtype=np.float32)
return r
def write(scores, known, out_dir, prefix, ratios):
forced = scores.copy()
forced[known] = np.inf
order = np.argsort(forced)[::-1]
for ratio in ratios:
k = int(round(len(scores) * ratio))
pred = np.zeros(len(scores), dtype=np.int8)
pred[order[:k]] = 1
df = pd.DataFrame({"Index": np.arange(len(pred)), "Predicted": pred.astype(str)})
path = out_dir / f"{prefix}_r{ratio:.3f}.csv"
df.to_csv(path, index=False)
print(path, int(pred.sum()), float(pred.mean()))
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--package-root", type=Path, default=Path(__file__).resolve().parents[1])
parser.add_argument("--device", default="cuda:0" if torch.cuda.is_available() else "cpu")
parser.add_argument("--batch-size", type=int, default=65536)
parser.add_argument("--ratios", nargs="*", type=float, default=[0.505, 0.515, 0.521, 0.530, 0.540])
args = parser.parse_args()
root = args.package_root
device = torch.device(args.device)
data, pairs, refs, feat_dim = build(root, device)
train_set = set(map(tuple, refs))
known = np.array([tuple(x) in train_set for x in pairs])
ckpts = [
"model_best_s0_d512.pt",
"model_best_s0_d384.pt",
"model_lgcn_s23.pt",
"model_lgcn_s0.pt",
"model_lgcn_s100.pt",
"model_lgcn_s77.pt",
"model_lgcn_s42.pt",
"model_lgcn_s2024.pt",
]
score_dir = root / "cached_scores" / "dot_full"
out_dir = root / "submissions" / "dot_full"
score_dir.mkdir(parents=True, exist_ok=True)
out_dir.mkdir(parents=True, exist_ok=True)
scores = []
for name in ckpts:
path = root / "checkpoints" / "extra_models" / name
cache = score_dir / f"{path.stem}_dot.npy"
if cache.exists():
s = np.load(cache)
else:
state = torch.load(path, map_location=device)
dim = state["author_emb.weight"].shape[1]
model = LightGCN(6611, feat_dim, dim, 4).to(device)
model.load_state_dict(state)
s = predict_dot(model, data, pairs, args.batch_size)
np.save(cache, s)
del model
torch.cuda.empty_cache()
print(name, s.mean(), s.std())
scores.append(s)
zmean = np.mean([(s - s.mean()) / (s.std() + 1e-8) for s in scores[:4]], axis=0)
rmean = np.mean([rank01(s) for s in scores[:4]], axis=0)
write(zmean, known, out_dir, "sub_dot_top4_z", args.ratios)
write(rmean, known, out_dir, "sub_dot_top4_rank", args.ratios)
if __name__ == "__main__":
main()
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