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- import os
- from typing import Dict, List, Tuple
- from ogb.nodeproppred import PygNodePropPredDataset
- import torch
- from torch_geometric.data import Data
- from torch_geometric.datasets import Amazon, Coauthor, PPI, WikiCS
- from torch_geometric import transforms as T
- from torch_geometric.utils import to_undirected
- from gssl import DATA_DIR
- def load_dataset(name: str) -> Tuple[Data, List[Dict[str, torch.Tensor]]]:
- ds_path = os.path.join(DATA_DIR, "datasets/", name)
- feature_norm = T.NormalizeFeatures()
- create_masks = T.AddTrainValTestMask(
- split="train_rest",
- num_splits=20,
- num_val=0.1,
- num_test=0.8,
- )
- if name == "WikiCS":
- data = WikiCS(
- root=ds_path,
- transform=feature_norm,
- )[0]
- elif name == "Amazon-CS":
- data = Amazon(
- root=ds_path,
- name="computers",
- transform=feature_norm,
- pre_transform=create_masks,
- )[0]
- elif name == "Amazon-Photo":
- data = Amazon(
- root=ds_path,
- name="photo",
- transform=feature_norm,
- pre_transform=create_masks,
- )[0]
- elif name == "Coauthor-CS":
- data = Coauthor(
- root=ds_path,
- name="cs",
- transform=feature_norm,
- pre_transform=create_masks,
- )[0]
- elif name == "Coauthor-Physics":
- data = Coauthor(
- root=ds_path,
- name="physics",
- transform=feature_norm,
- pre_transform=create_masks,
- )[0]
- elif name == "ogbn-arxiv":
- data = read_ogb_dataset(name=name, path=ds_path)
- data.edge_index = to_undirected(data.edge_index, data.num_nodes)
- elif name == "ogbn-products":
- data = read_ogb_dataset(name=name, path=ds_path)
- else:
- raise ValueError(f"Unknown dataset: {name}")
- if name in ("ogbn-arxiv", "ogbn-products"):
- masks = [
- {
- "train": data.train_mask,
- "val": data.val_mask,
- "test": data.test_mask,
- }
- ]
- else:
- masks = [
- {
- "train": data.train_mask[:, i],
- "val": data.val_mask[:, i],
- "test": (
- data.test_mask
- if name == "WikiCS"
- else data.test_mask[:, i]
- ),
- }
- for i in range(20)
- ]
- return data, masks
- def read_ogb_dataset(name: str, path: str) -> Data:
- dataset = PygNodePropPredDataset(root=path, name=name)
- split_idx = dataset.get_idx_split()
- data = dataset[0]
- data.train_mask = torch.zeros((data.num_nodes,), dtype=torch.bool)
- data.train_mask[split_idx["train"]] = True
- data.val_mask = torch.zeros((data.num_nodes,), dtype=torch.bool)
- data.val_mask[split_idx["valid"]] = True
- data.test_mask = torch.zeros((data.num_nodes,), dtype=torch.bool)
- data.test_mask[split_idx["test"]] = True
- data.y = data.y.squeeze(dim=-1)
- return data
- def load_ppi() -> Tuple[PPI, PPI, PPI]:
- ds_path = os.path.join(DATA_DIR, "datasets/PPI")
- feature_norm = T.NormalizeFeatures()
- train_ppi = PPI(root=ds_path, split="train", transform=feature_norm)
- val_ppi = PPI(root=ds_path, split="val", transform=feature_norm)
- test_ppi = PPI(root=ds_path, split="test", transform=feature_norm)
- return train_ppi, val_ppi, test_ppi
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