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import os.path as osp
from typing import Callable, Optional
import fsspec
import torch
from torch_geometric.data import Data, InMemoryDataset
from torch_geometric.io import fs
class SuiteSparseMatrixCollection(InMemoryDataset):
r"""A suite of sparse matrix benchmarks known as the `Suite Sparse Matrix
Collection <https://sparse.tamu.edu>`_ collected from a wide range of
applications.
Args:
root (str): Root directory where the dataset should be saved.
group (str): The group of the sparse matrix.
name (str): The name of the sparse matrix.
transform (callable, optional): A function/transform that takes in an
:obj:`torch_geometric.data.Data` 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.Data` object and returns a
transformed version. The data object will be transformed before
being saved to disk. (default: :obj:`None`)
force_reload (bool, optional): Whether to re-process the dataset.
(default: :obj:`False`)
"""
url = 'https://sparse.tamu.edu/mat/{}/{}.mat'
def __init__(
self,
root: str,
group: str,
name: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
self.group = group
self.name = name
super().__init__(root, transform, pre_transform,
force_reload=force_reload)
self.load(self.processed_paths[0])
@property
def raw_dir(self) -> str:
return osp.join(self.root, self.group, self.name, 'raw')
@property
def processed_dir(self) -> str:
return osp.join(self.root, self.group, self.name, 'processed')
@property
def raw_file_names(self) -> str:
return f'{self.name}.mat'
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self) -> None:
fs.cp(self.url.format(self.group, self.name), self.raw_dir)
def process(self) -> None:
from scipy.io import loadmat
with fsspec.open(self.raw_paths[0], 'rb') as f:
mat = loadmat(f)['Problem'][0][0][2].tocsr().tocoo()
row = torch.from_numpy(mat.row).to(torch.long)
col = torch.from_numpy(mat.col).to(torch.long)
edge_index = torch.stack([row, col], dim=0)
value = torch.from_numpy(mat.data).to(torch.float)
edge_attr = None if torch.all(value == 1.0) else value
size: Optional[torch.Size] = torch.Size(mat.shape)
if mat.shape[0] == mat.shape[1]:
size = None
num_nodes = mat.shape[0]
data = Data(edge_index=edge_index, edge_attr=edge_attr, size=size,
num_nodes=num_nodes)
if self.pre_transform is not None:
data = self.pre_transform(data)
self.save([data], self.processed_paths[0])
def __repr__(self) -> str:
return (f'{self.__class__.__name__}(group={self.group}, '
f'name={self.name})')
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