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data.py
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import utils
import dgl
import torch
from ogb.nodeproppred import DglNodePropPredDataset
import scipy.sparse as sp
import os.path
from dgl.data import CoraGraphDataset, CiteseerGraphDataset, PubmedGraphDataset
from dgl.data import CoraFullDataset, AmazonCoBuyComputerDataset, AmazonCoBuyPhotoDataset,CoauthorCSDataset,CoauthorPhysicsDataset
import random
import pickle as pkl
import numpy as np
from make_dataset import get_train_val_test_split
from sklearn.preprocessing import StandardScaler
# from cache_sample import cache_sample_rand_csr
def get_dataset(dataset, pe_dim, rw_dim, split_seed=0):
if dataset in {"arxiv", "products", "proteins", "papers100M", "mag"}:
if dataset == "arxiv":
dataset = DglNodePropPredDataset(name="ogbn-arxiv")
elif dataset == "products":
dataset = DglNodePropPredDataset(name="ogbn-products")
elif dataset == "proteins":
dataset = DglNodePropPredDataset(name="ogbn-proteins")
elif dataset == "papers100M":
dataset = DglNodePropPredDataset(name="ogbn-papers100M")
elif dataset == "mag":
dataset = DglNodePropPredDataset(name="ogbn-mag")
split_idx = dataset.get_idx_split()
graph, labels = dataset[0]
features = graph.ndata['feat']
adj = graph.adj(scipy_fmt="csr")
# adj = cache_sample_rand_csr(adj, s_len)
# print(labels)
idx_train = split_idx['train']
idx_val = split_idx['valid']
idx_test = split_idx['test']
graph = dgl.from_scipy(adj)
adj = utils.sparse_mx_to_torch_sparse_tensor(adj)
labels = labels.reshape(-1)
# RWPE
# lpe = utils.randomwalk_positional_encoding(adj, rw_dim)
# features = torch.cat((features, lpe.ndata['PE']), dim=1)
# LPE
lpe = utils.laplacian_positional_encoding(graph, pe_dim)
features = torch.cat((features, lpe), dim=1)
elif dataset in {"pubmed", "corafull", "computer", "photo", "cs", "physics","cora", "citeseer"}:
file_path = "dataset/"+dataset+".pt"
data_list = torch.load(file_path)
# data_list = [adj, features, labels, idx_train, idx_val, idx_test]
adj = data_list[0]
features = data_list[1]
labels = data_list[2]
idx_train = data_list[3]
idx_val = data_list[4]
idx_test = data_list[5]
if dataset == "pubmed":
graph = PubmedGraphDataset()[0]
elif dataset == "corafull":
graph = CoraFullDataset()[0]
elif dataset == "computer":
graph = AmazonCoBuyComputerDataset()[0]
elif dataset == "photo":
graph = AmazonCoBuyPhotoDataset()[0]
elif dataset == "cs":
graph = CoauthorCSDataset()[0]
elif dataset == "physics":
graph = CoauthorPhysicsDataset()[0]
elif dataset == "cora":
graph = CoraGraphDataset()[0]
elif dataset == "citeseer":
graph = CiteseerGraphDataset()[0]
graph = dgl.to_bidirected(graph)
# RWPE
# lpe = utils.randomwalk_positional_encoding(adj, rw_dim)
# features = torch.cat((features, lpe.ndata['PE']), dim=1)
# LPE
lpe = utils.laplacian_positional_encoding(graph, pe_dim)
features = torch.cat((features, lpe), dim=1)
# col_normalize
# features = col_normalize(features)
# features = torch.tensor(features)
elif dataset == 'aminer':
path = './dataset/'+dataset
adj = pkl.load(open(os.path.join(path, "{}.adj.sp.pkl".format(dataset)), "rb"))
features = pkl.load(
open(os.path.join(path, "{}.features.pkl".format(dataset)), "rb"))
labels = pkl.load(
open(os.path.join(path, "{}.labels.pkl".format(dataset)), "rb"))
random_state = np.random.RandomState(split_seed)
idx_train, idx_val, idx_test = get_train_val_test_split(
random_state, labels, train_examples_per_class=20, val_examples_per_class=30)
# idx_unlabel = np.concatenate((idx_val, idx_test))
features = col_normalize(features)
labels = torch.tensor(labels)
idx_train = torch.tensor(idx_train)
idx_val = torch.tensor(idx_val)
idx_test = torch.tensor(idx_test)
# graph = dgl.from_scipy(adj)
# lpe = utils.laplacian_positional_encoding(graph, pe_dim)
# features = torch.cat((features, lpe), dim=1)
adj = utils.sparse_mx_to_torch_sparse_tensor(adj)
print(adj)
labels = torch.argmax(labels, -1)
elif dataset in {"reddit", "Amazon2M"}:
file_path = './dataset/'+dataset+'.pt'
data_list = torch.load(file_path)
#adj, features, labels, idx_train, idx_val, idx_test
adj = data_list[0]
#print(type(adj))
features = torch.tensor(data_list[1], dtype=torch.float32)
labels = torch.tensor(data_list[2])
idx_train = torch.tensor(data_list[3])
idx_val = torch.tensor(data_list[4])
idx_test = torch.tensor(data_list[5])
graph = dgl.from_scipy(adj)
adj = utils.sparse_mx_to_torch_sparse_tensor(adj)
labels = torch.argmax(labels, -1)
# RWPE
# lpe = utils.randomwalk_positional_encoding(adj, rw_dim)
# features = torch.cat((features, lpe.ndata['PE']), dim=1)
# LPE
lpe = utils.laplacian_positional_encoding(graph, pe_dim)
features = torch.cat((features, lpe), dim=1)
return adj, features, labels, idx_train, idx_val, idx_test
def col_normalize(mx):
"""Column-normalize sparse matrix"""
scaler = StandardScaler()
mx = scaler.fit_transform(mx)
return mx