import os
import os.path as osp
from itertools import product
from typing import Callable, List, Optional
import numpy as np
import scipy.sparse as sp
import torch
from torch_geometric.data import (
HeteroData,
InMemoryDataset,
download_url,
extract_zip,
)
[docs]class LastFM(InMemoryDataset):
r"""A subset of the last.fm music website keeping track of users' listining
information from various sources, as collected in the
`"MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph
Embedding" <https://arxiv.org/abs/2002.01680>`_ paper.
last.fm is a heterogeneous graph containing three types of entities - users
(1,892 nodes), artists (17,632 nodes), and artist tags (1,088 nodes).
This dataset can be used for link prediction, and no labels or features are
provided.
Args:
root (str): Root directory where the dataset should be saved.
transform (callable, optional): A function/transform that takes in an
:obj:`torch_geometric.data.HeteroData` object and returns a
transformed version. The data object will be transformed before
every access. (default: :obj:`None`)
pre_transform (callable, optional): A function/transform that takes in
an :obj:`torch_geometric.data.HeteroData` object and returns a
transformed version. The data object will be transformed before
being saved to disk. (default: :obj:`None`)
"""
url = 'https://www.dropbox.com/s/jvlbs09pz6zwcka/LastFM_processed.zip?dl=1'
def __init__(self, root: str, transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None):
super().__init__(root, transform, pre_transform)
self.data, self.slices = torch.load(self.processed_paths[0])
@property
def raw_file_names(self) -> List[str]:
return [
'adjM.npz', 'node_types.npy', 'train_val_test_neg_user_artist.npz',
'train_val_test_pos_user_artist.npz'
]
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self):
path = download_url(self.url, self.raw_dir)
extract_zip(path, self.raw_dir)
os.remove(path)
def process(self):
data = HeteroData()
node_type_idx = np.load(osp.join(self.raw_dir, 'node_types.npy'))
node_type_idx = torch.from_numpy(node_type_idx).to(torch.long)
node_types = ['user', 'artist', 'tag']
for i, node_type in enumerate(node_types):
data[node_type].num_nodes = int((node_type_idx == i).sum())
pos_split = np.load(
osp.join(self.raw_dir, 'train_val_test_pos_user_artist.npz'))
neg_split = np.load(
osp.join(self.raw_dir, 'train_val_test_neg_user_artist.npz'))
for name in ['train', 'val', 'test']:
if name != 'train':
edge_index = pos_split[f'{name}_pos_user_artist']
edge_index = torch.from_numpy(edge_index)
edge_index = edge_index.t().to(torch.long).contiguous()
data['user', 'artist'][f'{name}_pos_edge_index'] = edge_index
edge_index = neg_split[f'{name}_neg_user_artist']
edge_index = torch.from_numpy(edge_index)
edge_index = edge_index.t().to(torch.long).contiguous()
data['user', 'artist'][f'{name}_neg_edge_index'] = edge_index
s = {}
N_u = data['user'].num_nodes
N_a = data['artist'].num_nodes
N_t = data['tag'].num_nodes
s['user'] = (0, N_u)
s['artist'] = (N_u, N_u + N_a)
s['tag'] = (N_u + N_a, N_u + N_a + N_t)
A = sp.load_npz(osp.join(self.raw_dir, 'adjM.npz'))
for src, dst in product(node_types, node_types):
A_sub = A[s[src][0]:s[src][1], s[dst][0]:s[dst][1]].tocoo()
if A_sub.nnz > 0:
row = torch.from_numpy(A_sub.row).to(torch.long)
col = torch.from_numpy(A_sub.col).to(torch.long)
data[src, dst].edge_index = torch.stack([row, col], dim=0)
if self.pre_transform is not None:
data = self.pre_transform(data)
torch.save(self.collate([data]), self.processed_paths[0])
def __repr__(self) -> str:
return f'{self.__class__.__name__}()'